Search

Your search keyword '"Bielak, Lawrence F."' showing total 49 results
Start Over Author "Bielak, Lawrence F." Topic human genome
49 results on '"Bielak, Lawrence F."'

1. Genome-wide association meta-analysis identifies 17 loci associated with nonalcoholic fatty liver disease

Author

Chen, Yanhua, Du, Xiaomeng, Kuppa, Annapurna, Feitosa, Mary F, Bielak, Lawrence F, O’Connell, Jeffrey R, Musani, Solomon K, Guo, Xiuqing, Kahali, Bratati, Chen, Vincent L, Smith, Albert V, Ryan, Kathleen A, Eirksdottir, Gudny, Allison, Matthew A, Bowden, Donald W, Budoff, Matthew J, Carr, John Jeffrey, Chen, Yii-Der I, Taylor, Kent D, Oliveri, Antonino, Correa, Adolfo, Crudup, Breland F, Kardia, Sharon LR, Mosley, Thomas H, Norris, Jill M, Terry, James G, Rotter, Jerome I, Wagenknecht, Lynne E, Halligan, Brian D, Young, Kendra A, Hokanson, John E, Washko, George R, Gudnason, Vilmundur, Province, Michael A, Peyser, Patricia A, Palmer, Nicholette D, and Speliotes, Elizabeth K

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Digestive Diseases, Liver Disease, Hepatitis, Chronic Liver Disease and Cirrhosis, Human Genome, Prevention, 2.1 Biological and endogenous factors, Humans, Non-alcoholic Fatty Liver Disease, Genome-Wide Association Study, Liver Cirrhosis, Acyltransferases, Phospholipases, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Liver, Protein Serine-Threonine Kinases, Intracellular Signaling Peptides and Proteins, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Nonalcoholic fatty liver disease (NAFLD) is common and partially heritable and has no effective treatments. We carried out a genome-wide association study (GWAS) meta-analysis of imaging (n = 66,814) and diagnostic code (3,584 cases versus 621,081 controls) measured NAFLD across diverse ancestries. We identified NAFLD-associated variants at torsin family 1 member B (TOR1B), fat mass and obesity associated (FTO), cordon-bleu WH2 repeat protein like 1 (COBLL1)/growth factor receptor-bound protein 14 (GRB14), insulin receptor (INSR), sterol regulatory element-binding transcription factor 1 (SREBF1) and patatin-like phospholipase domain-containing protein 2 (PNPLA2), as well as validated NAFLD-associated variants at patatin-like phospholipase domain-containing protein 3 (PNPLA3), transmembrane 6 superfamily 2 (TM6SF2), apolipoprotein E (APOE), glucokinase regulator (GCKR), tribbles homolog 1 (TRIB1), glycerol-3-phosphate acyltransferase (GPAM), mitochondrial amidoxime-reducing component 1 (MARC1), microsomal triglyceride transfer protein large subunit (MTTP), alcohol dehydrogenase 1B (ADH1B), transmembrane channel like 4 (TMC4)/membrane-bound O-acyltransferase domain containing 7 (MBOAT7) and receptor-type tyrosine-protein phosphatase δ (PTPRD). Implicated genes highlight mitochondrial, cholesterol and de novo lipogenesis as causally contributing to NAFLD predisposition. Phenome-wide association study (PheWAS) analyses suggest at least seven subtypes of NAFLD. Individuals in the top 10% and 1% of genetic risk have a 2.5-fold to 6-fold increased risk of NAFLD, cirrhosis and hepatocellular carcinoma. These genetic variants identify subtypes of NAFLD, improve estimates of disease risk and can guide the development of targeted therapeutics.

Published
2023

2. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Author

Kavousi, Maryam, Bos, Maxime M, Barnes, Hanna J, Lino Cardenas, Christian L, Wong, Doris, Lu, Haojie, Hodonsky, Chani J, Landsmeer, Lennart PL, Turner, Adam W, Kho, Minjung, Hasbani, Natalie R, de Vries, Paul S, Bowden, Donald W, Chopade, Sandesh, Deelen, Joris, Benavente, Ernest Diez, Guo, Xiuqing, Hofer, Edith, Hwang, Shih-Jen, Lutz, Sharon M, Lyytikäinen, Leo-Pekka, Slenders, Lotte, Smith, Albert V, Stanislawski, Maggie A, van Setten, Jessica, Wong, Quenna, Yanek, Lisa R, Becker, Diane M, Beekman, Marian, Budoff, Matthew J, Feitosa, Mary F, Finan, Chris, Hilliard, Austin T, Kardia, Sharon LR, Kovacic, Jason C, Kral, Brian G, Langefeld, Carl D, Launer, Lenore J, Malik, Shaista, Hoesein, Firdaus AA Mohamed, Mokry, Michal, Schmidt, Reinhold, Smith, Jennifer A, Taylor, Kent D, Terry, James G, van der Grond, Jeroen, van Meurs, Joyce, Vliegenthart, Rozemarijn, Xu, Jianzhao, Young, Kendra A, Zilhão, Nuno R, Zweiker, Robert, Assimes, Themistocles L, Becker, Lewis C, Bos, Daniel, Carr, J Jeffrey, Cupples, L Adrienne, de Kleijn, Dominique PV, de Winther, Menno, den Ruijter, Hester M, Fornage, Myriam, Freedman, Barry I, Gudnason, Vilmundur, Hingorani, Aroon D, Hokanson, John E, Ikram, M Arfan, Išgum, Ivana, Jacobs, David R, Kähönen, Mika, Lange, Leslie A, Lehtimäki, Terho, Pasterkamp, Gerard, Raitakari, Olli T, Schmidt, Helena, Slagboom, P Eline, Uitterlinden, André G, Vernooij, Meike W, Bis, Joshua C, Franceschini, Nora, Psaty, Bruce M, Post, Wendy S, Rotter, Jerome I, Björkegren, Johan LM, O’Donnell, Christopher J, Bielak, Lawrence F, Peyser, Patricia A, Malhotra, Rajeev, van der Laan, Sander W, and Miller, Clint L

Subjects
Biological Sciences, Genetics, Heart Disease - Coronary Heart Disease, Human Genome, Cardiovascular, Prevention, Heart Disease, Atherosclerosis, 2.1 Biological and endogenous factors, Aetiology, Humans, Black People, Coronary Artery Disease, Genome-Wide Association Study, Risk Factors, European People, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Published
2023

3. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Author

Chen, Fang, Wang, Xingyan, Jang, Seon-Kyeong, Quach, Bryan C, Weissenkampen, J Dylan, Khunsriraksakul, Chachrit, Yang, Lina, Sauteraud, Renan, Albert, Christine M, Allred, Nicholette DD, Arnett, Donna K, Ashley-Koch, Allison E, Barnes, Kathleen C, Barr, R Graham, Becker, Diane M, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boorgula, Meher Preethi, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der I, Chuang, Lee-Ming, Correa, Adolfo, Curran, Joanne E, David, Sean P, Fuentes, Lisa de las, Deka, Ranjan, Duggirala, Ravindranath, Faul, Jessica D, Garrett, Melanie E, Gharib, Sina A, Guo, Xiuqing, Hall, Michael E, Hawley, Nicola L, He, Jiang, Hobbs, Brian D, Hokanson, John E, Hsiung, Chao A, Hwang, Shih-Jen, Hyde, Thomas M, Irvin, Marguerite R, Jaffe, Andrew E, Johnson, Eric O, Kaplan, Robert, Kardia, Sharon LR, Kaufman, Joel D, Kelly, Tanika N, Kleinman, Joel E, Kooperberg, Charles, Lee, I-Te, Levy, Daniel, Lutz, Sharon M, Manichaikul, Ani W, Martin, Lisa W, Marx, Olivia, McGarvey, Stephen T, Minster, Ryan L, Moll, Matthew, Moussa, Karine A, Naseri, Take, North, Kari E, Oelsner, Elizabeth C, Peralta, Juan M, Peyser, Patricia A, Psaty, Bruce M, Rafaels, Nicholas, Raffield, Laura M, Reupena, Muagututi’a Sefuiva, Rich, Stephen S, Rotter, Jerome I, Schwartz, David A, Shadyab, Aladdin H, Sheu, Wayne H-H, Sims, Mario, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Telen, Marilyn J, Watson, Harold, Weeks, Daniel E, Weir, David R, Yanek, Lisa R, Young, Kendra A, Young, Kristin L, Zhao, Wei, Hancock, Dana B, Jiang, Bibo, Vrieze, Scott, and Liu, Dajiang J

Subjects
Genetics, Tobacco, Drug Abuse (NIDA only), Tobacco Smoke and Health, Substance Misuse, Brain Disorders, Human Genome, Good Health and Well Being, Humans, Transcriptome, Drug Repositioning, Genome-Wide Association Study, Tobacco Use, Biology, Polymorphism, Single Nucleotide, Genetic Predisposition to Disease, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Published
2023

4. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Author

Li, Xihao, Quick, Corbin, Zhou, Hufeng, Gaynor, Sheila M, Liu, Yaowu, Chen, Han, Selvaraj, Margaret Sunitha, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Haessler, Jeffrey, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Manichaikul, Ani, Martin, Lisa W, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Sitlani, Colleen M, Smith, Jennifer A, Taylor, Kent D, Vasan, Ramachandran S, Willer, Cristen J, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Natarajan, Pradeep, Peloso, Gina M, Li, Zilin, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Generic health relevance, Good Health and Well Being, Genome-Wide Association Study, Whole Genome Sequencing, Exome Sequencing, Phenotype, Lipids, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Published
2023

5. Genetic diversity fuels gene discovery for tobacco and alcohol use

Author

Saunders, Gretchen RB, Wang, Xingyan, Chen, Fang, Jang, Seon-Kyeong, Liu, Mengzhen, Wang, Chen, Gao, Shuang, Jiang, Yu, Khunsriraksakul, Chachrit, Otto, Jacqueline M, Addison, Clifton, Akiyama, Masato, Albert, Christine M, Aliev, Fazil, Alonso, Alvaro, Arnett, Donna K, Ashley-Koch, Allison E, Ashrani, Aneel A, Barnes, Kathleen C, Barr, R Graham, Bartz, Traci M, Becker, Diane M, Bielak, Lawrence F, Benjamin, Emelia J, Bis, Joshua C, Bjornsdottir, Gyda, Blangero, John, Bleecker, Eugene R, Boardman, Jason D, Boerwinkle, Eric, Boomsma, Dorret I, Boorgula, Meher Preethi, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Chasman, Daniel I, Chavan, Sameer, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Iona, Cho, Michael H, Choquet, Hélène, Cole, John W, Cornelis, Marilyn C, Cucca, Francesco, Curran, Joanne E, de Andrade, Mariza, Dick, Danielle M, Docherty, Anna R, Duggirala, Ravindranath, Eaton, Charles B, Ehringer, Marissa A, Esko, Tõnu, Faul, Jessica D, Fernandes Silva, Lilian, Fiorillo, Edoardo, Fornage, Myriam, Freedman, Barry I, Gabrielsen, Maiken E, Garrett, Melanie E, Gharib, Sina A, Gieger, Christian, Gillespie, Nathan, Glahn, David C, Gordon, Scott D, Gu, Charles C, Gu, Dongfeng, Gudbjartsson, Daniel F, Guo, Xiuqing, Haessler, Jeffrey, Hall, Michael E, Haller, Toomas, Harris, Kathleen Mullan, He, Jiang, Herd, Pamela, Hewitt, John K, Hickie, Ian, Hidalgo, Bertha, Hokanson, John E, Hopfer, Christian, Hottenga, JoukeJan, Hou, Lifang, Huang, Hongyan, Hung, Yi-Jen, Hunter, David J, Hveem, Kristian, Hwang, Shih-Jen, Hwu, Chii-Min, Iacono, William, Irvin, Marguerite R, Jee, Yon Ho, Johnson, Eric O, Joo, Yoonjung Y, Jorgenson, Eric, Justice, Anne E, Kamatani, Yoichiro, Kaplan, Robert C, Kaprio, Jaakko, Kardia, Sharon LR, and Keller, Matthew C

Subjects
Genetics, Alcoholism, Alcohol Use and Health, Prevention, Human Genome, Substance Misuse, Aetiology, 2.1 Biological and endogenous factors, Cancer, Good Health and Well Being, Humans, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Multifactorial Inheritance, Risk Factors, Tobacco Use, Alcohol Drinking, Transcriptome, Sample Size, Genetic Loci, Internationality, Europe, 23andMe Research Team, Biobank Japan Project, General Science & Technology
Abstract

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

Published
2022

6. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies

Author

Li, Zilin, Li, Xihao, Zhou, Hufeng, Gaynor, Sheila M, Selvaraj, Margaret Sunitha, Arapoglou, Theodore, Quick, Corbin, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Auer, Paul L, Bielak, Lawrence F, Bis, Joshua C, Blackwell, Thomas W, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Brody, Jennifer A, Cade, Brian E, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, de Vries, Paul S, Duggirala, Ravindranath, Franceschini, Nora, Freedman, Barry I, Göring, Harald HH, Guo, Xiuqing, Kalyani, Rita R, Kooperberg, Charles, Kral, Brian G, Lange, Leslie A, Lin, Bridget M, Manichaikul, Ani, Manning, Alisa K, Martin, Lisa W, Mathias, Rasika A, Meigs, James B, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Redline, Susan, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Taylor, Kent D, Taub, Margaret A, Vasan, Ramachandran S, Weeks, Daniel E, Wilson, James G, Yanek, Lisa R, Zhao, Wei, Rotter, Jerome I, Willer, Cristen J, Natarajan, Pradeep, Peloso, Gina M, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, Generic health relevance, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Genome, Phenotype, Genetic Variation, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Published
2022

7. Rare coding variants in RCN3 are associated with blood pressure

Author

He, Karen Y, Kelly, Tanika N, Wang, Heming, Liang, Jingjing, Zhu, Luke, Cade, Brian E, Assimes, Themistocles L, Becker, Lewis C, Beitelshees, Amber L, Bielak, Lawrence F, Bress, Adam P, Brody, Jennifer A, Chang, Yen-Pei Christy, Chang, Yi-Cheng, de Vries, Paul S, Duggirala, Ravindranath, Fox, Ervin R, Franceschini, Nora, Furniss, Anna L, Gao, Yan, Guo, Xiuqing, Haessler, Jeffrey, Hung, Yi-Jen, Hwang, Shih-Jen, Irvin, Marguerite Ryan, Kalyani, Rita R, Liu, Ching-Ti, Liu, Chunyu, Martin, Lisa Warsinger, Montasser, May E, Muntner, Paul M, Mwasongwe, Stanford, Naseri, Take, Palmas, Walter, Reupena, Muagututi’a Sefuiva, Rice, Kenneth M, Sheu, Wayne H-H, Shimbo, Daichi, Smith, Jennifer A, Snively, Beverly M, Yanek, Lisa R, Zhao, Wei, Blangero, John, Boerwinkle, Eric, Chen, Yii-Der Ida, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Fornage, Myriam, He, Jiang, Hou, Lifang, Kaplan, Robert C, Kardia, Sharon LR, Kenny, Eimear E, Kooperberg, Charles, Lloyd-Jones, Donald, Loos, Ruth JF, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, North, Kari E, Peyser, Patricia A, Psaty, Bruce M, Raffield, Laura M, Rao, DC, Redline, Susan, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Taylor, Kent D, Tracy, Russell, Vasan, Ramachandran S, Morrison, Alanna C, Levy, Daniel, Chakravarti, Aravinda, Arnett, Donna K, and Zhu, Xiaofeng

Subjects
Biological Sciences, Genetics, Cardiovascular, Human Genome, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Blood Pressure, Genetic Linkage, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Precision Medicine, Whole Genome Sequencing, Rare variant analysis, Blood pressure, Whole genome sequencing, Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA) Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundWhile large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.ResultsAssociations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).ConclusionsLow frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Published
2022

8. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U, Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D, Graham, Sarah E, Mukamel, Ronen E, Spracklen, Cassandra N, Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H, Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E, Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S, Akiyama, Masato, Allison, Matthew A, Alvarez, Marcus, Andersen, Mette K, Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F, Bollepalli, Sailalitha, Bonnycastle, Lori L, Bork-Jensen, Jette, Bradfield, Jonathan P, Bradford, Yuki, Braund, Peter S, Brody, Jennifer A, Burgdorf, Kristoffer S, Cade, Brian E, Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E Warwick, Degenhard, Frauke, Delgado, Graciela E, Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B, Engmann, Jorgen E, Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D, Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E, Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P, Goel, Anuj, Gordon, Scott D, Gorski, Mathias, Grove, Jakob, and Guo, Xiuqing

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Humans, Body Height, Gene Frequency, Genome, Human, Genome-Wide Association Study, Haplotypes, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Europe, Sample Size, Phenotype, Chromosome Mapping, 23andMe Research Team, VA Million Veteran Program, DiscovEHR, eMERGE, Lifelines Cohort Study, PRACTICAL Consortium, Understanding Society Scientific Group, General Science & Technology
Abstract

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

Published
2022

9. Multi‐phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

Author

Temprano‐Sagrera, Gerard, Sitlani, Colleen M, Bone, William P, Martin‐Bornez, Miguel, Voight, Benjamin F, Morrison, Alanna C, Damrauer, Scott M, de Vries, Paul S, Smith, Nicholas L, Sabater‐Lleal, Maria, Dehghan, Abbas, Heath, Adam S, Reiner, Alex P, Johnson, Andrew, Richmond, Anne, Peters, Annette, van Hylckama Vlieg, Astrid, McKnight, Barbara, Psaty, Bruce M, Hayward, Caroline, Ward‐Caviness, Cavin, O’Donnell, Christopher, Chasman, Daniel, Strachan, David P, Tregouet, David A, Mook‐Kanamori, Dennis, Gill, Dipender, Thibord, Florian, Asselbergs, Folkert W, Leebeek, Frank WG, Rosendaal, Frits R, Davies, Gail, Homuth, Georg, Temprano, Gerard, Campbell, Harry, Taylor, Herman A, Bressler, Jan, Huffman, Jennifer E, Rotter, Jerome I, Yao, Jie, Wilson, James F, Bis, Joshua C, Hahn, Julie M, Desch, Karl C, Wiggins, Kerri L, Raffield, Laura M, Bielak, Lawrence F, Yanek, Lisa R, Kleber, Marcus E, Mueller, Martina, Kavousi, Maryam, Mangino, Massimo, Liu, Melissa, Brown, Michael R, Conomos, Matthew P, Jhun, Min‐A, Chen, Ming‐Huei, de Maat, Moniek PM, Pankratz, Nathan, Peyser, Patricia A, Elliot, Paul, Wei, Peng, Wild, Philipp S, Morange, Pierre E, van der Harst, Pim, Yang, Qiong, Le, Ngoc‐Quynh, Marioni, Riccardo, Li, Ruifang, Cox, Simon R, Trompet, Stella, Felix, Stephan B, Völker, Uwe, Tang, Weihong, Koenig, Wolfgang, Jukema, J Wouter, Guo, Xiuqing, Lindstrom, Sara, Wang, Lu, Smith, Erin N, Gordon, William, de Andrade, Mariza, Brody, Jennifer A, Pattee, Jack W, Haessler, Jeffrey, Brumpton, Ben M, Chasman, Daniel I, Suchon, Pierre, Turman, Constance, Germain, Marine, MacDonald, James, and Braekkan, Sigrid K

Subjects
Genetics, Biotechnology, Human Genome, Atherosclerosis, Prevention, Cardiovascular, Hematology, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular Diseases, Factor XI, Genetic Predisposition to Disease, Genome-Wide Association Study, Hemostasis, Hemostatics, Humans, Phenotype, Polymorphism, Single Nucleotide, Tissue Plasminogen Activator, blood coagulation, cardiovascular diseases, genetic pleiotropy, genome-wide association study, hemostasis, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology
Abstract

BackgroundMulti-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.ObjectivesTo discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.MethodsSummary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).ResultsAcross the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.ConclusionsThe discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Published
2022

10. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation

Author

Mahajan, Anubha, Spracklen, Cassandra N, Zhang, Weihua, Ng, Maggie CY, Petty, Lauren E, Kitajima, Hidetoshi, Yu, Grace Z, Rüeger, Sina, Speidel, Leo, Kim, Young Jin, Horikoshi, Momoko, Mercader, Josep M, Taliun, Daniel, Moon, Sanghoon, Kwak, Soo-Heon, Robertson, Neil R, Rayner, Nigel W, Loh, Marie, Kim, Bong-Jo, Chiou, Joshua, Miguel-Escalada, Irene, della Briotta Parolo, Pietro, Lin, Kuang, Bragg, Fiona, Preuss, Michael H, Takeuchi, Fumihiko, Nano, Jana, Guo, Xiuqing, Lamri, Amel, Nakatochi, Masahiro, Scott, Robert A, Lee, Jung-Jin, Huerta-Chagoya, Alicia, Graff, Mariaelisa, Chai, Jin-Fang, Parra, Esteban J, Yao, Jie, Bielak, Lawrence F, Tabara, Yasuharu, Hai, Yang, Steinthorsdottir, Valgerdur, Cook, James P, Kals, Mart, Grarup, Niels, Schmidt, Ellen M, Pan, Ian, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloe, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Ahmad, Meraj, Noordam, Raymond, Lim, Victor JY, Tam, Claudia HT, Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M, Lecoeur, Cécile, Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R, Chen, Guanjie, Jensen, Richard A, Tajuddin, Salman, Kabagambe, Edmond K, An, Ping, Xiang, Anny H, Choi, Hyeok Sun, Cade, Brian E, Tan, Jingyi, Flanagan, Jack, Abaitua, Fernando, Adair, Linda S, Adeyemo, Adebowale, Aguilar-Salinas, Carlos A, Akiyama, Masato, Anand, Sonia S, Bertoni, Alain, Bian, Zheng, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A, Brummett, Chad M, Buchanan, Thomas A, Canouil, Mickaël, Chan, Juliana CN, Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, and Cushman, Mary

Subjects
Genetics, Diabetes, Human Genome, Metabolic and endocrine, Diabetes Mellitus, Type 2, Ethnicity, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Risk Factors, FinnGen, eMERGE Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P 50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.

Published
2022

11. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, Laurence J, Nivard, Michel G, Morris, Tim T, Hansen, Ailin F, Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael, Lind, Penelope A, Palviainen, Teemu, van der Zee, Matthijs D, Cheesman, Rosa, Mangino, Massimo, Wang, Yunzhang, Li, Shuai, Klaric, Lucija, Ratliff, Scott M, Bielak, Lawrence F, Nygaard, Marianne, Giannelis, Alexandros, Willoughby, Emily A, Reynolds, Chandra A, Balbona, Jared V, Andreassen, Ole A, Ask, Helga, Baras, Aris, Bauer, Christopher R, Boomsma, Dorret I, Campbell, Archie, Campbell, Harry, Chen, Zhengming, Christofidou, Paraskevi, Corfield, Elizabeth, Dahm, Christina C, Dokuru, Deepika R, Evans, Luke M, de Geus, Eco JC, Giddaluru, Sudheer, Gordon, Scott D, Harden, K Paige, Hill, W David, Hughes, Amanda, Kerr, Shona M, Kim, Yongkang, Kweon, Hyeokmoon, Latvala, Antti, Lawlor, Deborah A, Li, Liming, Lin, Kuang, Magnus, Per, Magnusson, Patrik KE, Mallard, Travis T, Martikainen, Pekka, Mills, Melinda C, Njølstad, Pål Rasmus, Overton, John D, Pedersen, Nancy L, Porteous, David J, Reid, Jeffrey, Silventoinen, Karri, Southey, Melissa C, Stoltenberg, Camilla, Tucker-Drob, Elliot M, Wright, Margaret J, Hewitt, John K, Keller, Matthew C, Stallings, Michael C, Lee, James J, Christensen, Kaare, Kardia, Sharon LR, Peyser, Patricia A, Smith, Jennifer A, Wilson, James F, Hopper, John L, Hägg, Sara, Spector, Tim D, Pingault, Jean-Baptiste, Plomin, Robert, Havdahl, Alexandra, Bartels, Meike, Martin, Nicholas G, Oskarsson, Sven, Justice, Anne E, Millwood, Iona Y, Hveem, Kristian, Naess, Øyvind, Willer, Cristen J, Åsvold, Bjørn Olav, Koellinger, Philipp D, Kaprio, Jaakko, Medland, Sarah E, Walters, Robin G, Benjamin, Daniel J, Turley, Patrick, Evans, David M, Davey Smith, George, Hayward, Caroline, Brumpton, Ben, Hemani, Gibran, and Davies, Neil M

Subjects
Human Genome, Genetics, Pediatric, 2.1 Biological and endogenous factors, Aetiology, Mental health, Generic health relevance, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Social Science Genetic Association Consortium, Within Family Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

12. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author

DiCorpo, Daniel, Gaynor, Sheila M, Russell, Emily M, Westerman, Kenneth E, Raffield, Laura M, Majarian, Timothy D, Wu, Peitao, Sarnowski, Chloé, Highland, Heather M, Jackson, Anne, Hasbani, Natalie R, de Vries, Paul S, Brody, Jennifer A, Hidalgo, Bertha, Guo, Xiuqing, Perry, James A, O’Connell, Jeffrey R, Lent, Samantha, Montasser, May E, Cade, Brian E, Jain, Deepti, Wang, Heming, D’Oliveira Albanus, Ricardo, Varshney, Arushi, Yanek, Lisa R, Lange, Leslie, Palmer, Nicholette D, Almeida, Marcio, Peralta, Juan M, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Bielak, Lawrence F, Chen, Chung-Shiuan, Chen, Yii-Der Ida, Choi, Won Jung, Goodarzi, Mark O, Floyd, James S, Irvin, Marguerite R, Kalyani, Rita R, Kelly, Tanika N, Lee, Seonwook, Liu, Ching-Ti, Loesch, Douglas, Manson, JoAnn E, Minster, Ryan L, Naseri, Take, Pankow, James S, Rasmussen-Torvik, Laura J, Reiner, Alexander P, Reupena, Muagututi’a Sefuiva, Selvin, Elizabeth, Smith, Jennifer A, Weeks, Daniel E, Xu, Huichun, Yao, Jie, Zhao, Wei, Parker, Stephen, Alonso, Alvaro, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Duggirala, Ravindranath, He, Jiang, Heckbert, Susan R, Kardia, Sharon LR, Kim, Ryan W, Kooperberg, Charles, Liu, Simin, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Morrison, Alanna C, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Shuldiner, Alan R, Taylor, Kent D, Vasan, Ramachandran S, Viaud-Martinez, Karine A, Florez, Jose C, Wilson, James G, Sladek, Robert, Rich, Stephen S, Rotter, Jerome I, Lin, Xihong, Dupuis, Josée, Meigs, James B, Wessel, Jennifer, and Manning, Alisa K

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, Diabetes, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Good Health and Well Being, Diabetes Mellitus, Type 2, Fasting, Glucose, Humans, Insulin, National Heart, Lung, and Blood Institute (U.S.), Nerve Tissue Proteins, Polymorphism, Single Nucleotide, Precision Medicine, Receptors, Immunologic, United States, Biological sciences, Biomedical and clinical sciences
Abstract

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI's Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Published
2022

13. Epigenome-wide association study of mitochondrial genome copy number.

Author

Wang, Penglong, Castellani, Christina A, Yao, Jie, Huan, Tianxiao, Bielak, Lawrence F, Zhao, Wei, Haessler, Jeffrey, Joehanes, Roby, Sun, Xianbang, Guo, Xiuqing, Longchamps, Ryan J, Manson, JoAnn E, Grove, Megan L, Bressler, Jan, Taylor, Kent D, Lappalainen, Tuuli, Kasela, Silva, Van Den Berg, David J, Hou, Lifang, Reiner, Alexander, Liu, Yongmei, Boerwinkle, Eric, Smith, Jennifer A, Peyser, Patricia A, Fornage, Myriam, Rich, Stephen S, Rotter, Jerome I, Kooperberg, Charles, Arking, Dan E, Levy, Daniel, and Liu, Chunyu

Subjects
Heart Disease, Human Genome, Genetics, Cardiovascular, Generic health relevance, Aged, DNA Copy Number Variations, DNA Methylation, DNA, Mitochondrial, Epigenome, Female, Genome, Mitochondrial, Humans, Male, Middle Aged, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

We conducted cohort- and race-specific epigenome-wide association analyses of mitochondrial deoxyribonucleic acid (mtDNA) copy number (mtDNA CN) measured in whole blood from participants of African and European origins in five cohorts (n = 6182, mean age = 57-67 years, 65% women). In the meta-analysis of all the participants, we discovered 21 mtDNA CN-associated DNA methylation sites (CpG) (P

Published
2021

14. A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program

Author

Stilp, Adrienne M, Emery, Leslie S, Broome, Jai G, Buth, Erin J, Khan, Alyna T, Laurie, Cecelia A, Wang, Fei Fei, Wong, Quenna, Chen, Dongquan, D'Augustine, Catherine M, Heard-Costa, Nancy L, Hohensee, Chancellor R, Johnson, William Craig, Juarez, Lucia D, Liu, Jingmin, Mutalik, Karen M, Raffield, Laura M, Wiggins, Kerri L, de Vries, Paul S, Kelly, Tanika N, Kooperberg, Charles, Natarajan, Pradeep, Peloso, Gina M, Peyser, Patricia A, Reiner, Alex P, Arnett, Donna K, Aslibekyan, Stella, Barnes, Kathleen C, Bielak, Lawrence F, Bis, Joshua C, Cade, Brian E, Chen, Ming-Huei, Correa, Adolfo, Cupples, L Adrienne, de Andrade, Mariza, Ellinor, Patrick T, Fornage, Myriam, Franceschini, Nora, Gan, Weiniu, Ganesh, Santhi K, Graffelman, Jan, Grove, Megan L, Guo, Xiuqing, Hawley, Nicola L, Hsu, Wan-Ling, Jackson, Rebecca D, Jaquish, Cashell E, Johnson, Andrew D, Kardia, Sharon LR, Kelly, Shannon, Lee, Jiwon, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, North, Kari E, Nouraie, Seyed Mehdi, Oelsner, Elizabeth C, Pankratz, Nathan, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Taylor, Kent D, Vasan, Ramachandran S, Weeks, Daniel E, Weiss, Scott T, Wilson, Carla G, Yanek, Lisa R, Psaty, Bruce M, Heckbert, Susan R, and Laurie, Cathy C

Subjects
Epidemiology, Health Sciences, Human Genome, Lung, Genetics, Clinical Research, Cardiovascular, Good Health and Well Being, Data Aggregation, Genetic Association Studies, Humans, Information Dissemination, National Heart, Lung, and Blood Institute (U.S.), Phenomics, Phenotype, Precision Medicine, Program Evaluation, United States, cardiovascular disease, common data elements, hematologic disease, information dissemination, lung diseases, phenotypes, sleep-wake disorders, Mathematical Sciences, Medical and Health Sciences
Abstract

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Published
2021

15. Multiethnic Genome-Wide Association Study of Subclinical Atherosclerosis in Individuals With Type 2 Diabetes

Author

Lu, Yingchang, Dimitrov, Latchezar, Chen, Shyh-Huei, Bielak, Lawrence F, Bis, Joshua C, Feitosa, Mary F, Lu, Lingyi, Kavousi, Maryam, Raffield, Laura M, Smith, Albert V, Wang, Lihua, Weiss, Stefan, Yao, Jie, Zhu, Jiaxi, Gudmundsson, Elias F, Gudmundsdottir, Valborg, Bos, Daniel, Ghanbari, Mohsen, Ikram, M Arfan, Hwang, Shih-Jen, Taylor, Kent D, Budoff, Matthew J, Gíslason, Gauti K, O’Donnell, Christopher J, An, Ping, Franceschini, Nora, Freedman, Barry I, Fu, Yi-Ping, Guo, Xiuqing, Heiss, Gerardo, Kardia, Sharon LR, Wilson, James G, Langefeld, Carl D, Schminke, Ulf, Uitterlinden, André G, Lange, Leslie A, Peyser, Patricia A, Gudnason, Vilmundur G, Psaty, Bruce M, Rotter, Jerome I, Bowden, Donald W, and Ng, Maggie CY

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Genetics, Diabetes, Biotechnology, Heart Disease, Atherosclerosis, Biomedical Imaging, Aging, Prevention, Heart Disease - Coronary Heart Disease, Cardiovascular, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Black People, Diabetes Complications, Diabetes Mellitus, Type 2, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, White People, atherosclerosis, carotid intima-media thickness, coronary artery disease, genetics, genome-wide association study, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundCoronary artery calcification (CAC) and carotid artery intima-media thickness (cIMT) are measures of subclinical atherosclerosis in asymptomatic individuals and strong risk factors for cardiovascular disease. Type 2 diabetes (T2D) is an independent cardiovascular disease risk factor that accelerates atherosclerosis.MethodsWe performed meta-analyses of genome-wide association studies in up to 2500 T2D individuals of European ancestry (EA) and 1590 T2D individuals of African ancestry with or without exclusion of prevalent cardiovascular disease, for CAC measured by cardiac computed tomography, and 3608 individuals of EA and 838 individuals of African ancestry with T2D for cIMT measured by ultrasonography within the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium.ResultsWe replicated 2 loci (rs9369640 and rs9349379 near PHACTR1 and rs10757278 near CDKN2B) for CAC and one locus for cIMT (rs7412 and rs445925 near APOE-APOC1) that were previously reported in the general EA populations. We identified one novel CAC locus (rs8000449 near CSNK1A1L/LINC00547/POSTN at 13q13.3) at P=2.0×10-8 in EA. No additional loci were identified with the meta-analyses of EA and African ancestry. The expression quantitative trait loci analysis with nearby expressed genes derived from arterial wall and metabolic tissues from the Genotype-Tissue Expression project pinpoints POSTN, encoding a matricellular protein involved in bone formation and bone matrix organization, as the potential candidate gene at this locus. In addition, we found significant associations (P

Published
2021

16. Population sequencing data reveal a compendium of mutational processes in the human germ line.

Author

Seplyarskiy, Vladimir B, Soldatov, Ruslan A, Koch, Evan, McGinty, Ryan J, Goldmann, Jakob M, Hernandez, Ryan D, Barnes, Kathleen, Correa, Adolfo, Burchard, Esteban G, Ellinor, Patrick T, McGarvey, Stephen T, Mitchell, Braxton D, Vasan, Ramachandran S, Redline, Susan, Silverman, Edwin, Weiss, Scott T, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, He, Jiang, Montgomery, Courtney, Rao, DC, Rotter, Jerome I, Taylor, Kent D, Brody, Jennifer A, Chen, Yii-Der Ida, de Las Fuentes, Lisa, Hwu, Chii-Min, Rich, Stephen S, Manichaikul, Ani W, Mychaleckyj, Josyf C, Palmer, Nicholette D, Smith, Jennifer A, Kardia, Sharon LR, Peyser, Patricia A, Bielak, Lawrence F, O'Connor, Timothy D, Emery, Leslie S, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Population Genetics Working Group, Gilissen, Christian, Wong, Wendy SW, Kharchenko, Peter V, and Sunyaev, Shamil

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Population Genetics Working Group, Germ Cells, Oocytes, Humans, DNA Damage, DNA Mutational Analysis, DNA Replication, Transcription, Genetic, Mutagenesis, CpG Islands, Long Interspersed Nucleotide Elements, Germ-Line Mutation, Genome, Human, Algorithms, Genetic Variation, DNA Demethylation, Human Genome, Genetics, 1.1 Normal biological development and functioning, Aetiology, Underpinning research, 2.1 Biological and endogenous factors, Generic health relevance, General Science & Technology
Abstract

Biological mechanisms underlying human germline mutations remain largely unknown. We statistically decompose variation in the rate and spectra of mutations along the genome using volume-regularized nonnegative matrix factorization. The analysis of a sequencing dataset (TOPMed) reveals nine processes that explain the variation in mutation properties between loci. We provide a biological interpretation for seven of these processes. We associate one process with bulky DNA lesions that are resolved asymmetrically with respect to transcription and replication. Two processes track direction of replication fork and replication timing, respectively. We identify a mutagenic effect of active demethylation primarily acting in regulatory regions and a mutagenic effect of long interspersed nuclear elements. We localize a mutagenic process specific to oocytes from population sequencing data. This process appears transcriptionally asymmetric.

Published
2021

17. Allele-specific variation at APOE increases nonalcoholic fatty liver disease and obesity but decreases risk of Alzheimer’s disease and myocardial infarction

Author

Palmer, Nicholette D, Kahali, Bratati, Kuppa, Annapurna, Chen, Yanhua, Du, Xiaomeng, Feitosa, Mary F, Bielak, Lawrence F, O’Connell, Jeffrey R, Musani, Solomon K, Guo, Xiuqing, Smith, Albert V, Ryan, Kathleen A, Eirksdottir, Gudny, Allison, Matthew A, Bowden, Donald W, Budoff, Matthew J, Carr, J Jeffrey, Chen, Yii-Der I, Taylor, Kent D, Correa, Adolfo, Crudup, Breland F, Halligan, Brian, Yang, Jian, Kardia, Sharon LR, Launer, Lenore J, Fu, Yi-Ping, Mosley, Thomas H, Norris, Jill M, Terry, James G, O’Donnell, Christopher J, Rotter, Jerome I, Wagenknecht, Lynne E, Gudnason, Vilmundur, Province, Michael A, Peyser, Patricia A, and Speliotes, Elizabeth K

Subjects
Biological Sciences, Genetics, Digestive Diseases, Brain Disorders, Prevention, Aging, Chronic Liver Disease and Cirrhosis, Obesity, Neurosciences, Alzheimer's Disease, Liver Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Acquired Cognitive Impairment, Neurodegenerative, Human Genome, Hepatitis, Dementia, 2.1 Biological and endogenous factors, Aetiology, Oral and gastrointestinal, Alanine Transaminase, Alleles, Alzheimer Disease, Apolipoproteins E, Databases, Genetic, Exome, Gene Frequency, Genome-Wide Association Study, Humans, Liver, Liver Cirrhosis, Myocardial Infarction, Non-alcoholic Fatty Liver Disease, Phenotype, Polymorphism, Single Nucleotide, Prognosis, Risk Factors, Triglycerides, Medical and Health Sciences, Genetics & Heredity
Abstract

Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and is highly correlated with metabolic disease. NAFLD results from environmental exposures acting on a susceptible polygenic background. This study performed the largest multiethnic investigation of exonic variation associated with NAFLD and correlated metabolic traits and diseases. An exome array meta-analysis was carried out among eight multiethnic population-based cohorts (n = 16 492) with computed tomography (CT) measured hepatic steatosis. A fixed effects meta-analysis identified five exome-wide significant loci (P

Published
2021

18. Discovery and fine-mapping of height loci via high-density imputation of GWASs in individuals of African ancestry

Author

Graff, Mariaelisa, Justice, Anne E, Young, Kristin L, Marouli, Eirini, Zhang, Xinruo, Fine, Rebecca S, Lim, Elise, Buchanan, Victoria, Rand, Kristin, Feitosa, Mary F, Wojczynski, Mary K, Yanek, Lisa R, Shao, Yaming, Rohde, Rebecca, Adeyemo, Adebowale A, Aldrich, Melinda C, Allison, Matthew A, Ambrosone, Christine B, Ambs, Stefan, Amos, Christopher, Arnett, Donna K, Atwood, Larry, Bandera, Elisa V, Bartz, Traci, Becker, Diane M, Berndt, Sonja I, Bernstein, Leslie, Bielak, Lawrence F, Blot, William J, Bottinger, Erwin P, Bowden, Donald W, Bradfield, Jonathan P, Brody, Jennifer A, Broeckel, Ulrich, Burke, Gregory, Cade, Brian E, Cai, Qiuyin, Caporaso, Neil, Carlson, Chris, Carpten, John, Casey, Graham, Chanock, Stephen J, Chen, Guanjie, Chen, Minhui, Chen, Yii-Der I, Chen, Wei-Min, Chesi, Alessandra, Chiang, Charleston WK, Chu, Lisa, Coetzee, Gerry A, Conti, David V, Cooper, Richard S, Cushman, Mary, Demerath, Ellen, Deming, Sandra L, Dimitrov, Latchezar, Ding, Jingzhong, Diver, W Ryan, Duan, Qing, Evans, Michele K, Falusi, Adeyinka G, Faul, Jessica D, Fornage, Myriam, Fox, Caroline, Freedman, Barry I, Garcia, Melissa, Gillanders, Elizabeth M, Goodman, Phyllis, Gottesman, Omri, Grant, Struan FA, Guo, Xiuqing, Hakonarson, Hakon, Haritunians, Talin, Harris, Tamara B, Harris, Curtis C, Henderson, Brian E, Hennis, Anselm, Hernandez, Dena G, Hirschhorn, Joel N, McNeill, Lorna Haughton, Howard, Timothy D, Howard, Barbara, Hsing, Ann W, Hsu, Yu-Han H, Hu, Jennifer J, Huff, Chad D, Huo, Dezheng, Ingles, Sue A, Irvin, Marguerite R, John, Esther M, Johnson, Karen C, Jordan, Joanne M, Kabagambe, Edmond K, Kang, Sun J, Kardia, Sharon L, Keating, Brendan J, Kittles, Rick A, Klein, Eric A, Kolb, Suzanne, and Kolonel, Laurence N

Subjects
Human Genome, Genetics, Africa, Black or African American, Black People, Body Height, Europe, Female, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, African ancestry, fine-mapping, genome-wide, height, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Although many loci have been associated with height in European ancestry populations, very few have been identified in African ancestry individuals. Furthermore, many of the known loci have yet to be generalized to and fine-mapped within a large-scale African ancestry sample. We performed sex-combined and sex-stratified meta-analyses in up to 52,764 individuals with height and genome-wide genotyping data from the African Ancestry Anthropometry Genetics Consortium (AAAGC). We additionally combined our African ancestry meta-analysis results with published European genome-wide association study (GWAS) data. In the African ancestry analyses, we identified three novel loci (SLC4A3, NCOA2, ECD/FAM149B1) in sex-combined results and two loci (CRB1, KLF6) in women only. In the African plus European sex-combined GWAS, we identified an additional three novel loci (RCCD1, G6PC3, CEP95) which were equally driven by AAAGC and European results. Among 39 genome-wide significant signals at known loci, conditioning index SNPs from European studies identified 20 secondary signals. Two of the 20 new secondary signals and none of the 8 novel loci had minor allele frequencies (MAF) < 5%. Of 802 known European height signals, 643 displayed directionally consistent associations with height, of which 205 were nominally significant (p < 0.05) in the African ancestry sex-combined sample. Furthermore, 148 of 241 loci contained ≤20 variants in the credible sets that jointly account for 99% of the posterior probability of driving the associations. In summary, trans-ethnic meta-analyses revealed novel signals and further improved fine-mapping of putative causal variants in loci shared between African and European ancestry populations.

Published
2021

19. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Taliun, Daniel, Harris, Daniel N, Kessler, Michael D, Carlson, Jedidiah, Szpiech, Zachary A, Torres, Raul, Taliun, Sarah A Gagliano, Corvelo, André, Gogarten, Stephanie M, Kang, Hyun Min, Pitsillides, Achilleas N, LeFaive, Jonathon, Lee, Seung-been, Tian, Xiaowen, Browning, Brian L, Das, Sayantan, Emde, Anne-Katrin, Clarke, Wayne E, Loesch, Douglas P, Shetty, Amol C, Blackwell, Thomas W, Smith, Albert V, Wong, Quenna, Liu, Xiaoming, Conomos, Matthew P, Bobo, Dean M, Aguet, François, Albert, Christine, Alonso, Alvaro, Ardlie, Kristin G, Arking, Dan E, Aslibekyan, Stella, Auer, Paul L, Barnard, John, Barr, R Graham, Barwick, Lucas, Becker, Lewis C, Beer, Rebecca L, Benjamin, Emelia J, Bielak, Lawrence F, Blangero, John, Boehnke, Michael, Bowden, Donald W, Brody, Jennifer A, Burchard, Esteban G, Cade, Brian E, Casella, James F, Chalazan, Brandon, Chasman, Daniel I, Chen, Yii-Der Ida, Cho, Michael H, Choi, Seung Hoan, Chung, Mina K, Clish, Clary B, Correa, Adolfo, Curran, Joanne E, Custer, Brian, Darbar, Dawood, Daya, Michelle, de Andrade, Mariza, DeMeo, Dawn L, Dutcher, Susan K, Ellinor, Patrick T, Emery, Leslie S, Eng, Celeste, Fatkin, Diane, Fingerlin, Tasha, Forer, Lukas, Fornage, Myriam, Franceschini, Nora, Fuchsberger, Christian, Fullerton, Stephanie M, Germer, Soren, Gladwin, Mark T, Gottlieb, Daniel J, Guo, Xiuqing, Hall, Michael E, He, Jiang, Heard-Costa, Nancy L, Heckbert, Susan R, Irvin, Marguerite R, Johnsen, Jill M, Johnson, Andrew D, Kaplan, Robert, Kardia, Sharon LR, Kelly, Tanika, Kelly, Shannon, Kenny, Eimear E, Kiel, Douglas P, Klemmer, Robert, Konkle, Barbara A, Kooperberg, Charles, Köttgen, Anna, Lange, Leslie A, Lasky-Su, Jessica, Levy, Daniel, Lin, Xihong, Lin, Keng-Han, Liu, Chunyu, and Loos, Ruth JF

Subjects
Genetics, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Good Health and Well Being, Cytochrome P-450 CYP2D6, Genetic Variation, Genome, Human, Genomics, Haplotypes, Heterozygote, Humans, INDEL Mutation, Loss of Function Mutation, Mutagenesis, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Population Density, Precision Medicine, Quality Control, Sample Size, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, General Science & Technology
Abstract

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Published
2021

20. Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Author

Sun, Daokun, Richard, Melissa A, Musani, Solomon K, Sung, Yun Ju, Winkler, Thomas W, Schwander, Karen, Chai, Jin Fang, Guo, Xiuqing, Kilpeläinen, Tuomas O, Vojinovic, Dina, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Brown, Michael R, Chitrala, Kumaraswamy, Hartwig, Fernando P, Horimoto, Andrea RVR, Liu, Yongmei, Manning, Alisa K, Noordam, Raymond, Smith, Albert V, Harris, Sarah E, Kühnel, Brigitte, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Rauramaa, Rainer, van der Most, Peter J, Wang, Rujia, Ware, Erin B, Weiss, Stefan, Wen, Wanqing, Yanek, Lisa R, Arking, Dan E, Arnett, Donna K, Barac, Ana, Boerwinkle, Eric, Broeckel, Ulrich, Chakravarti, Aravinda, Chen, Yii-Der Ida, Cupples, L Adrienne, Davigulus, Martha L, de las Fuentes, Lisa, de Mutsert, Renée, de Vries, Paul S, Delaney, Joseph AC, Roux, Ana V Diez, Dörr, Marcus, Faul, Jessica D, Fretts, Amanda M, Gallo, Linda C, Grabe, Hans Jörgen, Gu, C Charles, Harris, Tamara B, Hartman, Catharina CA, Heikkinen, Sami, Ikram, M Arfan, Isasi, Carmen, Johnson, W Craig, Jonas, Jost Bruno, Kaplan, Robert C, Komulainen, Pirjo, Krieger, Jose E, Levy, Daniel, Study, Lifelines Cohort, Liu, Jianjun, Lohman, Kurt, Luik, Annemarie I, Martin, Lisa W, Meitinger, Thomas, Milaneschi, Yuri, O’Connell, Jeff R, Palmas, Walter R, Peters, Annette, Peyser, Patricia A, Pulkki-Råback, Laura, Raffel, Leslie J, Reiner, Alex P, Rice, Kenneth, Robinson, Jennifer G, Rosendaal, Frits R, Schmidt, Carsten Oliver, Schreiner, Pamela J, Schwettmann, Lars, Shikany, James M, Shu, Xiao-ou, Sidney, Stephen, Sims, Mario, Smith, Jennifer A, Sotoodehnia, Nona, Strauch, Konstantin, Tai, E Shyong, Taylor, Kent D, Uitterlinden, André G, van Duijn, Cornelia M, Waldenberger, Melanie, Wee, Hwee-Lin, Wei, Wen-Bin, Wilson, Gregory, Xuan, Deng, and Yao, Jie

Subjects
Hypertension, Mental Health, Genetics, Cardiovascular, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Lifelines Cohort Study
Abstract

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from 5 ancestry groups. In the combined meta-analyses of Stages 1 and 2, we identified 59 loci (p value

Published
2021

21. Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Author

Bick, Alexander G, Weinstock, Joshua S, Nandakumar, Satish K, Fulco, Charles P, Bao, Erik L, Zekavat, Seyedeh M, Szeto, Mindy D, Liao, Xiaotian, Leventhal, Matthew J, Nasser, Joseph, Chang, Kyle, Laurie, Cecelia, Burugula, Bala Bharathi, Gibson, Christopher J, Niroula, Abhishek, Lin, Amy E, Taub, Margaret A, Aguet, Francois, Ardlie, Kristin, Mitchell, Braxton D, Barnes, Kathleen C, Moscati, Arden, Fornage, Myriam, Redline, Susan, Psaty, Bruce M, Silverman, Edwin K, Weiss, Scott T, Palmer, Nicholette D, Vasan, Ramachandran S, Burchard, Esteban G, Kardia, Sharon LR, He, Jiang, Kaplan, Robert C, Smith, Nicholas L, Arnett, Donna K, Schwartz, David A, Correa, Adolfo, de Andrade, Mariza, Guo, Xiuqing, Konkle, Barbara A, Custer, Brian, Peralta, Juan M, Gui, Hongsheng, Meyers, Deborah A, McGarvey, Stephen T, Chen, Ida Yii-Der, Shoemaker, M Benjamin, Peyser, Patricia A, Broome, Jai G, Gogarten, Stephanie M, Wang, Fei Fei, Wong, Quenna, Montasser, May E, Daya, Michelle, Kenny, Eimear E, North, Kari E, Launer, Lenore J, Cade, Brian E, Bis, Joshua C, Cho, Michael H, Lasky-Su, Jessica, Bowden, Donald W, Cupples, L Adrienne, Mak, Angel CY, Becker, Lewis C, Smith, Jennifer A, Kelly, Tanika N, Aslibekyan, Stella, Heckbert, Susan R, Tiwari, Hemant K, Yang, Ivana V, Heit, John A, Lubitz, Steven A, Johnsen, Jill M, Curran, Joanne E, Wenzel, Sally E, Weeks, Daniel E, Rao, Dabeeru C, Darbar, Dawood, Moon, Jee-Young, Tracy, Russell P, Buth, Erin J, Rafaels, Nicholas, Loos, Ruth JF, Durda, Peter, Liu, Yongmei, Hou, Lifang, Lee, Jiwon, Kachroo, Priyadarshini, Freedman, Barry I, Levy, Daniel, Bielak, Lawrence F, Hixson, James E, Floyd, James S, Whitsel, Eric A, Ellinor, Patrick T, Irvin, Marguerite R, Fingerlin, Tasha E, Raffield, Laura M, and Armasu, Sebastian M

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Biotechnology, Regenerative Medicine, Stem Cell Research, Human Genome, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Cardiovascular, Generic health relevance, Inflammatory and immune system, Good Health and Well Being, Adult, Africa, Aged, Aged, 80 and over, Black People, Cell Self Renewal, Clonal Hematopoiesis, DNA-Binding Proteins, Dioxygenases, Female, Genetic Predisposition to Disease, Genome, Human, Germ-Line Mutation, Hematopoietic Stem Cells, Humans, Intracellular Signaling Peptides and Proteins, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Precision Medicine, Proto-Oncogene Proteins, Tripartite Motif Proteins, United States, Whole Genome Sequencing, alpha Karyopherins, NHLBI Trans-Omics for Precision Medicine Consortium, General Science & Technology
Abstract

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown1. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer2-4 and coronary heart disease5-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)6. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

Published
2020

22. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

Author

Li, Xihao, Li, Zilin, Zhou, Hufeng, Gaynor, Sheila M, Liu, Yaowu, Chen, Han, Sun, Ryan, Dey, Rounak, Arnett, Donna K, Aslibekyan, Stella, Ballantyne, Christie M, Bielak, Lawrence F, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Broome, Jai G, Conomos, Matthew P, Correa, Adolfo, Cupples, L Adrienne, Curran, Joanne E, Freedman, Barry I, Guo, Xiuqing, Hindy, George, Irvin, Marguerite R, Kardia, Sharon LR, Kathiresan, Sekar, Khan, Alyna T, Kooperberg, Charles L, Laurie, Cathy C, Liu, X Shirley, Mahaney, Michael C, Manichaikul, Ani W, Martin, Lisa W, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Moore, Jill E, Morrison, Alanna C, O’Connell, Jeffrey R, Palmer, Nicholette D, Pampana, Akhil, Peralta, Juan M, Peyser, Patricia A, Psaty, Bruce M, Redline, Susan, Rice, Kenneth M, Rich, Stephen S, Smith, Jennifer A, Tiwari, Hemant K, Tsai, Michael Y, Vasan, Ramachandran S, Wang, Fei Fei, Weeks, Daniel E, Weng, Zhiping, Wilson, James G, Yanek, Lisa R, Neale, Benjamin M, Sunyaev, Shamil R, Abecasis, Gonçalo R, Rotter, Jerome I, Willer, Cristen J, Peloso, Gina M, Natarajan, Pradeep, and Lin, Xihong

Subjects
Biological Sciences, Genetics, Human Genome, Good Health and Well Being, Cholesterol, LDL, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome, Genome-Wide Association Study, Humans, Models, Genetic, Molecular Sequence Annotation, Phenotype, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Large-scale whole-genome sequencing studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests have limited scope to leverage variant functions. We propose STAAR (variant-set test for association using annotation information), a scalable and powerful RV association test method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce 'annotation principal components', multidimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness and is scalable for analyzing very large cohort and biobank whole-genome sequencing studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery and 17,822 replication samples from the Trans-Omics for Precision Medicine Program. We discovered and replicated new RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.

Published
2020

23. Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

Author

Wang, Zhe, Chen, Han, Bartz, Traci M, Bielak, Lawrence F, Chasman, Daniel I, Feitosa, Mary F, Franceschini, Nora, Guo, Xiuqing, Lim, Elise, Noordam, Raymond, Richard, Melissa A, Wang, Heming, Cade, Brian, Cupples, L Adrienne, de Vries, Paul S, Giulanini, Franco, Lee, Jiwon, Lemaitre, Rozenn N, Martin, Lisa W, Reiner, Alex P, Rich, Stephen S, Schreiner, Pamela J, Sidney, Stephen, Sitlani, Colleen M, Smith, Jennifer A, van Dijk, Ko Willems, Yao, Jie, Zhao, Wei, Fornage, Myriam, Kardia, Sharon LR, Kooperberg, Charles, Liu, Ching-Ti, Mook-Kanamori, Dennis O, Province, Michael A, Psaty, Bruce M, Redline, Susan, Ridker, Paul M, Rotter, Jerome I, Boerwinkle, Eric, Morrison, Alanna C, and Group, on behalf of the CHARGE Gene-Lifestyle Interactions Working

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Substance Misuse, Human Genome, Genetics, Cardiovascular, Heart Disease, Alcoholism, Alcohol Use and Health, Aetiology, 2.1 Biological and endogenous factors, Stroke, Cancer, Oral and gastrointestinal, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Alcohol Drinking, Apolipoproteins E, Cholesterol, HDL, Female, Gene Frequency, Genetic Loci, Genome-Wide Association Study, Humans, Lipids, Male, Middle Aged, Polymorphism, Single Nucleotide, Proprotein Convertase 9, Triglycerides, White People, Young Adult, exome, gene-environment interaction, genome-wide association study, lipids, self-report, CHARGE Gene-Lifestyle Interactions Working Group, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundAlcohol intake influences plasma lipid levels, and such effects may be moderated by genetic variants. We aimed to characterize the role of aggregated rare and low-frequency protein-coding variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels.MethodsIn the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, fasting plasma triglycerides and high- and low-density lipoprotein cholesterol were measured in 34 153 individuals with European ancestry from 5 discovery studies and 32 277 individuals from 6 replication studies. Rare and low-frequency functional protein-coding variants (minor allele frequency, ≤5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction test and a joint test of genetic main and gene-environment interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least 2 drinks per week, were considered.ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3, and CD300LG) remained significant after conditioning on the common index single-nucleotide polymorphism identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on triglycerides in a novel locus was significantly discovered (P=6.65×10-6 for the interaction test) and replicated at nominal significance level (P=0.013) in SMC5.ConclusionsIn conclusion, this study applied new gene-based statistical approaches and suggested that rare and low-frequency genetic variants interacted with alcohol consumption on lipid levels.

Published
2020

24. Smoking-by-genotype interaction in type 2 diabetes risk and fasting glucose

Author

Wu, Peitao, Rybin, Denis, Bielak, Lawrence F, Feitosa, Mary F, Franceschini, Nora, Li, Yize, Lu, Yingchang, Marten, Jonathan, Musani, Solomon K, Noordam, Raymond, Raghavan, Sridharan, Rose, Lynda M, Schwander, Karen, Smith, Albert V, Tajuddin, Salman M, Vojinovic, Dina, Amin, Najaf, Arnett, Donna K, Bottinger, Erwin P, Demirkan, Ayse, Florez, Jose C, Ghanbari, Mohsen, Harris, Tamara B, Launer, Lenore J, Liu, Jingmin, Liu, Jun, Mook-Kanamori, Dennis O, Murray, Alison D, Nalls, Mike A, Peyser, Patricia A, Uitterlinden, André G, Voortman, Trudy, Bouchard, Claude, Chasman, Daniel, Correa, Adolfo, de Mutsert, Renée, Evans, Michele K, Gudnason, Vilmundur, Hayward, Caroline, Kao, Linda, Kardia, Sharon LR, Kooperberg, Charles, Loos, Ruth JF, Province, Michael M, Rankinen, Tuomo, Redline, Susan, Ridker, Paul M, Rotter, Jerome I, Siscovick, David, Smith, Blair H, van Duijn, Cornelia, Zonderman, Alan B, Rao, DC, Wilson, James G, Dupuis, Josée, Meigs, James B, Liu, Ching-Ti, and Vassy, Jason L

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Pharmacology and Pharmaceutical Sciences, Tobacco, Aging, Tobacco Smoke and Health, Human Genome, Nutrition, Diabetes, Clinical Research, Prevention, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Aged, Black People, Blood Glucose, Cigarette Smoking, Cohort Studies, Diabetes Mellitus, Type 2, Fasting, Feasibility Studies, Female, Genetic Loci, Genome-Wide Association Study, Genotype, Humans, Incidence, Male, Middle Aged, Polymorphism, Single Nucleotide, Risk, White People, General Science & Technology
Abstract

Smoking is a potentially causal behavioral risk factor for type 2 diabetes (T2D), but not all smokers develop T2D. It is unknown whether genetic factors partially explain this variation. We performed genome-environment-wide interaction studies to identify loci exhibiting potential interaction with baseline smoking status (ever vs. never) on incident T2D and fasting glucose (FG). Analyses were performed in participants of European (EA) and African ancestry (AA) separately. Discovery analyses were conducted using genotype data from the 50,000-single-nucleotide polymorphism (SNP) ITMAT-Broad-CARe (IBC) array in 5 cohorts from from the Candidate Gene Association Resource Consortium (n = 23,189). Replication was performed in up to 16 studies from the Cohorts for Heart Aging Research in Genomic Epidemiology Consortium (n = 74,584). In meta-analysis of discovery and replication estimates, 5 SNPs met at least one criterion for potential interaction with smoking on incident T2D at p

Published
2020

25. Loss-of-function genomic variants highlight potential therapeutic targets for cardiovascular disease

Author

Nielsen, Jonas B, Rom, Oren, Surakka, Ida, Graham, Sarah E, Zhou, Wei, Roychowdhury, Tanmoy, Fritsche, Lars G, Gagliano Taliun, Sarah A, Sidore, Carlo, Liu, Yuhao, Gabrielsen, Maiken E, Skogholt, Anne Heidi, Wolford, Brooke, Overton, William, Zhao, Ying, Chen, Jin, Zhang, He, Hornsby, Whitney E, Acheampong, Akua, Grooms, Austen, Schaefer, Amanda, Zajac, Gregory JM, Villacorta, Luis, Zhang, Jifeng, Brumpton, Ben, Løset, Mari, Rai, Vivek, Lundegaard, Pia R, Olesen, Morten S, Taylor, Kent D, Palmer, Nicholette D, Chen, Yii-Der, Choi, Seung H, Lubitz, Steven A, Ellinor, Patrick T, Barnes, Kathleen C, Daya, Michelle, Rafaels, Nicholas, Weiss, Scott T, Lasky-Su, Jessica, Tracy, Russell P, Vasan, Ramachandran S, Cupples, L Adrienne, Mathias, Rasika A, Yanek, Lisa R, Becker, Lewis C, Peyser, Patricia A, Bielak, Lawrence F, Smith, Jennifer A, Aslibekyan, Stella, Hidalgo, Bertha A, Arnett, Donna K, Irvin, Marguerite R, Wilson, James G, Musani, Solomon K, Correa, Adolfo, Rich, Stephen S, Guo, Xiuqing, Rotter, Jerome I, Konkle, Barbara A, Johnsen, Jill M, Ashley-Koch, Allison E, Telen, Marilyn J, Sheehan, Vivien A, Blangero, John, Curran, Joanne E, Peralta, Juan M, Montgomery, Courtney, Sheu, Wayne H-H, Chung, Ren-Hua, Schwander, Karen, Nouraie, Seyed M, Gordeuk, Victor R, Zhang, Yingze, Kooperberg, Charles, Reiner, Alexander P, Jackson, Rebecca D, Bleecker, Eugene R, Meyers, Deborah A, Li, Xingnan, Das, Sayantan, Yu, Ketian, LeFaive, Jonathon, Smith, Albert, Blackwell, Tom, Taliun, Daniel, Zollner, Sebastian, Forer, Lukas, Schoenherr, Sebastian, Fuchsberger, Christian, Pandit, Anita, Zawistowski, Matthew, Kheterpal, Sachin, Brummett, Chad M, Natarajan, Pradeep, Schlessinger, David, Lee, Seunggeun, Kang, Hyun Min, Cucca, Francesco, and Holmen, Oddgeir L

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Heart Disease, Human Genome, Prevention, Biotechnology, Digestive Diseases, Aging, Cardiovascular, Liver Disease, Atherosclerosis, Aetiology, Development of treatments and therapeutic interventions, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Good Health and Well Being, Biological Specimen Banks, Cardiovascular Diseases, Gene Silencing, Gene Targeting, Genome, Human, Genome-Wide Association Study, Humans, Lipids, Liver, Loss of Function Mutation, Molecular Targeted Therapy, Phenomics, Receptors, LDL, United Kingdom
Abstract

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10-8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD.

Published
2020

26. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

Author

de Vries, Paul S, Brown, Michael R, Bentley, Amy R, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Schwander, Karen, Kraja, Aldi T, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Huffman, Jennifer E, Musani, Solomon K, Li, Changwei, Feitosa, Mary F, Richard, Melissa A, Noordam, Raymond, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Deng, Xuan, Dorajoo, Rajkumar, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Evangelou, Evangelos, Graff, Mariaelisa, Alver, Maris, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gandin, Ilaria, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, Hartwig, Fernando P, He, Meian, Horimoto, Andrea RVR, Hsu, Fang-Chi, Jackson, Anne U, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Lee, Joseph H, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Matoba, Nana, Nolte, Ilja M, Pietzner, Maik, Riaz, Muhammad, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stančáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Wang, Yajuan, Ware, Erin B, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Ballantyne, Christie, Boerwinkle, Eric, Broeckel, Ulrich, Campbell, Archie, Canouil, Mickaël, Charumathi, Sabanayagam, Chen, Yii-Der Ida, Connell, John M, de Faire, Ulf, de las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Ding, Jingzhong, Dominiczak, Anna F, Duan, Qing, Eaton, Charles B, Eppinga, Ruben N, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Franco, Oscar H, Friedlander, Yechiel, Ghanbari, Mohsen, and Giulianini, Franco

Subjects
Epidemiology, Health Sciences, Substance Misuse, Alcoholism, Alcohol Use and Health, Human Genome, Prevention, Genetics, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Adolescent, Adult, Aged, Alcohol Drinking, Cholesterol, HDL, Cholesterol, LDL, Female, Genome-Wide Association Study, Genotype, Humans, Life Style, Lipids, Male, Middle Aged, Phenotype, Racial Groups, Triglycerides, Vascular Endothelial Growth Factor B, Young Adult, alcohol consumption, cholesterol, gene-environment interactions, gene-lifestyle interactions, genome-wide association studies, lipids, triglycerides, InterAct Consortium, Lifelines Cohort, Groningen, The Netherlands, Mathematical Sciences, Medical and Health Sciences
Abstract

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

Published
2019

27. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution

Author

Justice, Anne E, Karaderi, Tugce, Highland, Heather M, Young, Kristin L, Graff, Mariaelisa, Lu, Yingchang, Turcot, Valérie, Auer, Paul L, Fine, Rebecca S, Guo, Xiuqing, Schurmann, Claudia, Lempradl, Adelheid, Marouli, Eirini, Mahajan, Anubha, Winkler, Thomas W, Locke, Adam E, Medina-Gomez, Carolina, Esko, Tõnu, Vedantam, Sailaja, Giri, Ayush, Lo, Ken Sin, Alfred, Tamuno, Mudgal, Poorva, Ng, Maggie CY, Heard-Costa, Nancy L, Feitosa, Mary F, Manning, Alisa K, Willems, Sara M, Sivapalaratnam, Suthesh, Abecasis, Goncalo, Alam, Dewan S, Allison, Matthew, Amouyel, Philippe, Arzumanyan, Zorayr, Balkau, Beverley, Bastarache, Lisa, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Caulfield, Mark J, Cesana, Giancarlo, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Collins, Francis S, Cook, James P, Cox, Amanda J, Crosslin, David S, Danesh, John, de Bakker, Paul IW, Denus, Simon de, Mutsert, Renée de, Dedoussis, George, Demerath, Ellen W, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Dörr, Marcus, Drenos, Fotios, Dubé, Marie-Pierre, Dunning, Alison M, Easton, Douglas F, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, Feng, Shuang, Ferrannini, Ele, Ferrieres, Jean, Florez, Jose C, Fornage, Myriam, Fox, Caroline S, Franks, Paul W, Friedrich, Nele, Gan, Wei, Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Girotto, Giorgia, Gorski, Mathias, Grallert, Harald, Grarup, Niels, Grove, Megan L, Gustafsson, Stefan, Haessler, Jeff, Hansen, Torben, and Hattersley, Andrew T

Subjects
Clinical Research, Obesity, Genetics, Nutrition, Prevention, Biotechnology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Cardiovascular, Animals, Body Fat Distribution, Body Mass Index, Case-Control Studies, Drosophila, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Homeostasis, Humans, Lipids, Male, Proteins, Risk Factors, Waist-Hip Ratio, CHD Exome+ Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, InterAct, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF

Published
2019

28. Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

Author

Chen, Han, Huffman, Jennifer E, Brody, Jennifer A, Wang, Chaolong, Lee, Seunggeun, Li, Zilin, Gogarten, Stephanie M, Sofer, Tamar, Bielak, Lawrence F, Bis, Joshua C, Blangero, John, Bowler, Russell P, Cade, Brian E, Cho, Michael H, Correa, Adolfo, Curran, Joanne E, de Vries, Paul S, Glahn, David C, Guo, Xiuqing, Johnson, Andrew D, Kardia, Sharon, Kooperberg, Charles, Lewis, Joshua P, Liu, Xiaoming, Mathias, Rasika A, Mitchell, Braxton D, O’Connell, Jeffrey R, Peyser, Patricia A, Post, Wendy S, Reiner, Alex P, Rich, Stephen S, Rotter, Jerome I, Silverman, Edwin K, Smith, Jennifer A, Vasan, Ramachandran S, Wilson, James G, Yanek, Lisa R, Consortium, NHLBI Trans-Omics for Precision Medicine, Group, TOPMed Hematology and Hemostasis Working, Redline, Susan, Smith, Nicholas L, Boerwinkle, Eric, Borecki, Ingrid B, Cupples, L Adrienne, Laurie, Cathy C, Morrison, Alanna C, Rice, Kenneth M, and Lin, Xihong

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Biotechnology, Aetiology, 2.5 Research design and methodologies (aetiology), Generic health relevance, Good Health and Well Being, Chromosomes, Human, Pair 4, Cloud Computing, Female, Fibrinogen, Genetic Association Studies, Genetics, Population, Humans, Male, Models, Genetic, National Heart, Lung, and Blood Institute (U.S.), Precision Medicine, Research Design, Time Factors, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology and Hemostasis Working Group, TOPMed, generalized linear mixed model, population structure, rare variants, relatedness, variant set association test, whole-genome sequencing, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

Published
2019

29. Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease

Author

Ward-Caviness, Cavin K, de Vries, Paul S, Wiggins, Kerri L, Huffman, Jennifer E, Yanek, Lisa R, Bielak, Lawrence F, Giulianini, Franco, Guo, Xiuqing, Kleber, Marcus E, Kacprowski, Tim, Groß, Stefan, Petersman, Astrid, Smith, George Davey, Hartwig, Fernando P, Bowden, Jack, Hemani, Gibran, Müller-Nuraysid, Martina, Strauch, Konstantin, Koenig, Wolfgang, Waldenberger, Melanie, Meitinger, Thomas, Pankratz, Nathan, Boerwinkle, Eric, Tang, Weihong, Fu, Yi-Ping, Johnson, Andrew D, Song, Ci, de Maat, Moniek PM, Uitterlinden, André G, Franco, Oscar H, Brody, Jennifer A, McKnight, Barbara, Chen, Yii-Der Ida, Psaty, Bruce M, Mathias, Rasika A, Becker, Diane M, Peyser, Patricia A, Smith, Jennifer A, Bielinski, Suzette J, Ridker, Paul M, Taylor, Kent D, Yao, Jie, Tracy, Russell, Delgado, Graciela, Trompet, Stella, Sattar, Naveed, Jukema, J Wouter, Becker, Lewis C, Kardia, Sharon LR, Rotter, Jerome I, März, Winfried, Dörr, Marcus, Chasman, Daniel I, Dehghan, Abbas, O’Donnell, Christopher J, Smith, Nicholas L, Peters, Annette, and Morrison, Alanna C

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Heart Disease, Cardiovascular, Prevention, Human Genome, Heart Disease - Coronary Heart Disease, Alleles, Coronary Disease, Fibrinogen, Genetic Pleiotropy, Genetic Variation, Genome-Wide Association Study, Humans, Incidence, Mendelian Randomization Analysis, Models, Genetic, Multivariate Analysis, Myocardial Infarction, Odds Ratio, General Science & Technology
Abstract

BackgroundFibrinogen is an essential hemostatic factor and cardiovascular disease risk factor. Early attempts at evaluating the causal effect of fibrinogen on coronary heart disease (CHD) and myocardial infraction (MI) using Mendelian randomization (MR) used single variant approaches, and did not take advantage of recent genome-wide association studies (GWAS) or multi-variant, pleiotropy robust MR methodologies.Methods and findingsWe evaluated evidence for a causal effect of fibrinogen on both CHD and MI using MR. We used both an allele score approach and pleiotropy robust MR models. The allele score was composed of 38 fibrinogen-associated variants from recent GWAS. Initial analyses using the allele score used a meta-analysis of 11 European-ancestry prospective cohorts, free of CHD and MI at baseline, to examine incidence CHD and MI. We also applied 2 sample MR methods with data from a prevalent CHD and MI GWAS. Results are given in terms of the hazard ratio (HR) or odds ratio (OR), depending on the study design, and associated 95% confidence interval (CI). In single variant analyses no causal effect of fibrinogen on CHD or MI was observed. In multi-variant analyses using incidence CHD cases and the allele score approach, the estimated causal effect (HR) of a 1 g/L higher fibrinogen concentration was 1.62 (CI = 1.12, 2.36) when using incident cases and the allele score approach. In 2 sample MR analyses that accounted for pleiotropy, the causal estimate (OR) was reduced to 1.18 (CI = 0.98, 1.42) and 1.09 (CI = 0.89, 1.33) in the 2 most precise (smallest CI) models, out of 4 models evaluated. In the 2 sample MR analyses for MI, there was only very weak evidence of a causal effect in only 1 out of 4 models.ConclusionsA small causal effect of fibrinogen on CHD is observed using multi-variant MR approaches which account for pleiotropy, but not single variant MR approaches. Taken together, results indicate that even with large sample sizes and multi-variant approaches MR analyses still cannot exclude the null when estimating the causal effect of fibrinogen on CHD, but that any potential causal effect is likely to be much smaller than observed in epidemiological studies.

Published
2019

30. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Author

Kilpeläinen, Tuomas O, Bentley, Amy R, Noordam, Raymond, Sung, Yun Ju, Schwander, Karen, Winkler, Thomas W, Jakupović, Hermina, Chasman, Daniel I, Manning, Alisa, Ntalla, Ioanna, Aschard, Hugues, Brown, Michael R, de las Fuentes, Lisa, Franceschini, Nora, Guo, Xiuqing, Vojinovic, Dina, Aslibekyan, Stella, Feitosa, Mary F, Kho, Minjung, Musani, Solomon K, Richard, Melissa, Wang, Heming, Wang, Zhe, Bartz, Traci M, Bielak, Lawrence F, Campbell, Archie, Dorajoo, Rajkumar, Fisher, Virginia, Hartwig, Fernando P, Horimoto, Andrea RVR, Li, Changwei, Lohman, Kurt K, Marten, Jonathan, Sim, Xueling, Smith, Albert V, Tajuddin, Salman M, Alver, Maris, Amini, Marzyeh, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Evangelou, Evangelos, Gao, Chuan, Graff, Mariaelisa, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Jackson, Anne U, Zhao, Jing Hua, Kraja, Aldi T, Kühnel, Brigitte, Laguzzi, Federica, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Rauramaa, Rainer, Riaz, Muhammad, Robino, Antonietta, Rueedi, Rico, Stringham, Heather M, Takeuchi, Fumihiko, van der Most, Peter J, Varga, Tibor V, Verweij, Niek, Ware, Erin B, Wen, Wanqing, Li, Xiaoyin, Yanek, Lisa R, Amin, Najaf, Arnett, Donna K, Boerwinkle, Eric, Brumat, Marco, Cade, Brian, Canouil, Mickaël, Chen, Yii-Der Ida, Concas, Maria Pina, Connell, John, de Mutsert, Renée, de Silva, H Janaka, de Vries, Paul S, Demirkan, Ayşe, Ding, Jingzhong, Eaton, Charles B, Faul, Jessica D, Friedlander, Yechiel, Gabriel, Kelley P, Ghanbari, Mohsen, Giulianini, Franco, Gu, Chi Charles, Gu, Dongfeng, Harris, Tamara B, He, Jiang, Heikkinen, Sami, Heng, Chew-Kiat, Hunt, Steven C, Ikram, M Arfan, Jonas, Jost B, Koh, Woon-Puay, Komulainen, Pirjo, and Krieger, Jose E

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Genetics, Cardiovascular, Clinical Research, Prevention, Atherosclerosis, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Adolescent, Adult, Aged, Aged, 80 and over, Asian People, Black People, Brazil, Calcium-Binding Proteins, Cholesterol, Cholesterol, HDL, Cholesterol, LDL, Exercise, Female, Genetic Loci, Genome-Wide Association Study, Genotype, Hispanic or Latino, Humans, LIM-Homeodomain Proteins, Lipid Metabolism, Lipids, Male, Membrane Proteins, Microtubule-Associated Proteins, Middle Aged, Muscle Proteins, Nerve Tissue Proteins, Transcription Factors, Triglycerides, White People, Young Adult, Lifelines Cohort Study
Abstract

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

Published
2019

31. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Author

Mahajan, Anubha, Wessel, Jennifer, Willems, Sara M, Zhao, Wei, Robertson, Neil R, Chu, Audrey Y, Gan, Wei, Kitajima, Hidetoshi, Taliun, Daniel, Rayner, N William, Guo, Xiuqing, Lu, Yingchang, Li, Man, Jensen, Richard A, Hu, Yao, Huo, Shaofeng, Lohman, Kurt K, Zhang, Weihua, Cook, James P, Prins, Bram Peter, Flannick, Jason, Grarup, Niels, Trubetskoy, Vassily Vladimirovich, Kravic, Jasmina, Kim, Young Jin, Rybin, Denis V, Yaghootkar, Hanieh, Müller-Nurasyid, Martina, Meidtner, Karina, Li-Gao, Ruifang, Varga, Tibor V, Marten, Jonathan, Li, Jin, Smith, Albert Vernon, An, Ping, Ligthart, Symen, Gustafsson, Stefan, Malerba, Giovanni, Demirkan, Ayse, Tajes, Juan Fernandez, Steinthorsdottir, Valgerdur, Wuttke, Matthias, Lecoeur, Cécile, Preuss, Michael, Bielak, Lawrence F, Graff, Marielisa, Highland, Heather M, Justice, Anne E, Liu, Dajiang J, Marouli, Eirini, Peloso, Gina Marie, Warren, Helen R, Afaq, Saima, Afzal, Shoaib, Ahlqvist, Emma, Almgren, Peter, Amin, Najaf, Bang, Lia B, Bertoni, Alain G, Bombieri, Cristina, Bork-Jensen, Jette, Brandslund, Ivan, Brody, Jennifer A, Burtt, Noël P, Canouil, Mickaël, Chen, Yii-Der Ida, Cho, Yoon Shin, Christensen, Cramer, Eastwood, Sophie V, Eckardt, Kai-Uwe, Fischer, Krista, Gambaro, Giovanni, Giedraitis, Vilmantas, Grove, Megan L, de Haan, Hugoline G, Hackinger, Sophie, Hai, Yang, Han, Sohee, Tybjærg-Hansen, Anne, Hivert, Marie-France, Isomaa, Bo, Jäger, Susanne, Jørgensen, Marit E, Jørgensen, Torben, Käräjämäki, Annemari, Kim, Bong-Jo, Kim, Sung Soo, Koistinen, Heikki A, Kovacs, Peter, Kriebel, Jennifer, Kronenberg, Florian, Läll, Kristi, Lange, Leslie A, Lee, Jung-Jin, Lehne, Benjamin, Li, Huaixing, Lin, Keng-Hung, Linneberg, Allan, Liu, Ching-Ti, and Liu, Jun

Subjects
Diabetes, Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Alleles, Chromosome Mapping, Diabetes Mellitus, Type 2, Female, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Male, White People, Exome Sequencing, ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P

Published
2018

32. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

Author

Turcot, Valérie, Lu, Yingchang, Highland, Heather M, Schurmann, Claudia, Justice, Anne E, Fine, Rebecca S, Bradfield, Jonathan P, Esko, Tõnu, Giri, Ayush, Graff, Mariaelisa, Guo, Xiuqing, Hendricks, Audrey E, Karaderi, Tugce, Lempradl, Adelheid, Locke, Adam E, Mahajan, Anubha, Marouli, Eirini, Sivapalaratnam, Suthesh, Young, Kristin L, Alfred, Tamuno, Feitosa, Mary F, Masca, Nicholas GD, Manning, Alisa K, Medina-Gomez, Carolina, Mudgal, Poorva, Ng, Maggie CY, Reiner, Alex P, Vedantam, Sailaja, Willems, Sara M, Winkler, Thomas W, Abecasis, Gonçalo, Aben, Katja K, Alam, Dewan S, Alharthi, Sameer E, Allison, Matthew, Amouyel, Philippe, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Bang, Lia E, Barroso, Inês, Bastarache, Lisa, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boehnke, Michael, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Brumat, Marco, Burt, Amber A, Butterworth, Adam S, Campbell, Peter T, Cappellani, Stefania, Carey, David J, Catamo, Eulalia, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der I, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Corominas Galbany, Jordi, Cox, Amanda J, Crosslin, David S, Cuellar-Partida, Gabriel, D’Eustacchio, Angela, Danesh, John, Davies, Gail, Bakker, Paul IW, Groot, Mark CH, Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, Heijer, Martin, Hollander, Anneke I, Ruijter, Hester M, Dennis, Joe G, Denny, Josh C, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dubé, Marie-Pierre, Dunning, Alison M, and Easton, Douglas F

Subjects
Genetics, Biotechnology, Nutrition, Obesity, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Cancer, Stroke, Metabolic and endocrine, Cardiovascular, Oral and gastrointestinal, Adult, Animals, Body Mass Index, Drosophila, Energy Intake, Energy Metabolism, Female, Gene Frequency, Genetic Variation, Humans, Male, Proteins, Syndrome, CHD Exome+ Consortium, EPIC-CVD Consortium, ExomeBP Consortium, Global Lipids Genetic Consortium, GoT2D Genes Consortium, EPIC InterAct Consortium, INTERVAL Study, ReproGen Consortium, T2D-Genes Consortium, MAGIC Investigators, Understanding Society Scientific Group, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Published
2018

33. Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries

Author

Feitosa, Mary F, Kraja, Aldi T, Chasman, Daniel I, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Marten, Jonathan, Musani, Solomon K, Li, Changwei, Bentley, Amy R, Brown, Michael R, Schwander, Karen, Richard, Melissa A, Noordam, Raymond, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Dorajoo, Rajkumar, Fisher, Virginia, Hartwig, Fernando P, Horimoto, Andrea RVR, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Wojczynski, Mary K, Alver, Maris, Boissel, Mathilde, Cai, Qiuyin, Campbell, Archie, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Jackson, Anne U, Kähönen, Mika, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Luan, Jian'an, Matoba, Nana, Nolte, Ilja M, Padmanabhan, Sandosh, Riaz, Muhammad, Rueedi, Rico, Robino, Antonietta, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stančáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Vitart, Veronique, Wang, Yajuan, Ware, Erin B, Warren, Helen R, Weiss, Stefan, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Boerwinkle, Eric, Borecki, Ingrid, Broeckel, Ulrich, Brown, Morris, Brumat, Marco, Burke, Gregory L, Canouil, Mickaël, Chakravarti, Aravinda, Charumathi, Sabanayagam, Ida Chen, Yii-Der, Connell, John M, Correa, Adolfo, de las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Deng, Xuan, Ding, Jingzhong, Duan, Qing, Eaton, Charles B, and Ehret, Georg

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Pharmacology and Pharmaceutical Sciences, Substance Misuse, Human Genome, Alcoholism, Alcohol Use and Health, Biotechnology, Hypertension, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Alcohol Drinking, Blood Pressure, Cohort Studies, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Pedigree, Polymorphism, Single Nucleotide, Racial Groups, Young Adult, InterAct Consortium, General Science & Technology
Abstract

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

Published
2018

34. Genome-wide physical activity interactions in adiposity ― A meta-analysis of 200,452 adults

Author

Graff, Mariaelisa, Scott, Robert A, Justice, Anne E, Young, Kristin L, Feitosa, Mary F, Barata, Llilda, Winkler, Thomas W, Chu, Audrey Y, Mahajan, Anubha, Hadley, David, Xue, Luting, Workalemahu, Tsegaselassie, Heard-Costa, Nancy L, den Hoed, Marcel, Ahluwalia, Tarunveer S, Qi, Qibin, Ngwa, Julius S, Renström, Frida, Quaye, Lydia, Eicher, John D, Hayes, James E, Cornelis, Marilyn, Kutalik, Zoltan, Lim, Elise, Luan, Jian’an, Huffman, Jennifer E, Zhang, Weihua, Zhao, Wei, Griffin, Paula J, Haller, Toomas, Ahmad, Shafqat, Marques-Vidal, Pedro M, Bien, Stephanie, Yengo, Loic, Teumer, Alexander, Smith, Albert Vernon, Kumari, Meena, Harder, Marie Neergaard, Justesen, Johanne Marie, Kleber, Marcus E, Hollensted, Mette, Lohman, Kurt, Rivera, Natalia V, Whitfield, John B, Zhao, Jing Hua, Stringham, Heather M, Lyytikäinen, Leo-Pekka, Huppertz, Charlotte, Willemsen, Gonneke, Peyrot, Wouter J, Wu, Ying, Kristiansson, Kati, Demirkan, Ayse, Fornage, Myriam, Hassinen, Maija, Bielak, Lawrence F, Cadby, Gemma, Tanaka, Toshiko, Mägi, Reedik, van der Most, Peter J, Jackson, Anne U, Bragg-Gresham, Jennifer L, Vitart, Veronique, Marten, Jonathan, Navarro, Pau, Bellis, Claire, Pasko, Dorota, Johansson, Åsa, Snitker, Søren, Cheng, Yu-Ching, Eriksson, Joel, Lim, Unhee, Aadahl, Mette, Adair, Linda S, Amin, Najaf, Balkau, Beverley, Auvinen, Juha, Beilby, John, Bergman, Richard N, Bergmann, Sven, Bertoni, Alain G, Blangero, John, Bonnefond, Amélie, Bonnycastle, Lori L, Borja, Judith B, Brage, Søren, Busonero, Fabio, Buyske, Steve, Campbell, Harry, Chines, Peter S, Collins, Francis S, Corre, Tanguy, Smith, George Davey, Delgado, Graciela E, Dueker, Nicole, Dörr, Marcus, Ebeling, Tapani, Eiriksdottir, Gudny, Esko, Tõnu, and Faul, Jessica D

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Nutrition, Obesity, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Stroke, Adiposity, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Body Mass Index, Epigenomics, Exercise, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Waist Circumference, Waist-Hip Ratio, CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium, Developmental Biology
Abstract

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.

Published
2017

35. Rare and low-frequency coding variants alter human adult height

Author

Marouli, Eirini, Graff, Mariaelisa, Medina-Gomez, Carolina, Lo, Ken Sin, Wood, Andrew R, Kjaer, Troels R, Fine, Rebecca S, Lu, Yingchang, Schurmann, Claudia, Highland, Heather M, Rüeger, Sina, Thorleifsson, Gudmar, Justice, Anne E, Lamparter, David, Stirrups, Kathleen E, Turcot, Valérie, Young, Kristin L, Winkler, Thomas W, Esko, Tõnu, Karaderi, Tugce, Locke, Adam E, Masca, Nicholas GD, Ng, Maggie CY, Mudgal, Poorva, Rivas, Manuel A, Vedantam, Sailaja, Mahajan, Anubha, Guo, Xiuqing, Abecasis, Goncalo, Aben, Katja K, Adair, Linda S, Alam, Dewan S, Albrecht, Eva, Allin, Kristine H, Allison, Matthew, Amouyel, Philippe, Appel, Emil V, Arveiler, Dominique, Asselbergs, Folkert W, Auer, Paul L, Balkau, Beverley, Banas, Bernhard, Bang, Lia E, Benn, Marianne, Bergmann, Sven, Bielak, Lawrence F, Blüher, Matthias, Boeing, Heiner, Boerwinkle, Eric, Böger, Carsten A, Bonnycastle, Lori L, Bork-Jensen, Jette, Bots, Michiel L, Bottinger, Erwin P, Bowden, Donald W, Brandslund, Ivan, Breen, Gerome, Brilliant, Murray H, Broer, Linda, Burt, Amber A, Butterworth, Adam S, Carey, David J, Caulfield, Mark J, Chambers, John C, Chasman, Daniel I, Chen, Yii-Der Ida, Chowdhury, Rajiv, Christensen, Cramer, Chu, Audrey Y, Cocca, Massimiliano, Collins, Francis S, Cook, James P, Corley, Janie, Galbany, Jordi Corominas, Cox, Amanda J, Cuellar-Partida, Gabriel, Danesh, John, Davies, Gail, de Bakker, Paul IW, de Borst, Gert J, de Denus, Simon, de Groot, Mark CH, de Mutsert, Renée, Deary, Ian J, Dedoussis, George, Demerath, Ellen W, den Hollander, Anneke I, Dennis, Joe G, Di Angelantonio, Emanuele, Drenos, Fotios, Du, Mengmeng, Dunning, Alison M, Easton, Douglas F, Ebeling, Tapani, Edwards, Todd L, Ellinor, Patrick T, Elliott, Paul, Evangelou, Evangelos, Farmaki, Aliki-Eleni, and Faul, Jessica D

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, ADAMTS Proteins, Adult, Alleles, Body Height, Cell Adhesion Molecules, Female, Gene Frequency, Genetic Variation, Genome, Human, Glycoproteins, Glycosaminoglycans, Hedgehog Proteins, Humans, Intercellular Signaling Peptides and Proteins, Interferon Regulatory Factors, Interleukin-11 Receptor alpha Subunit, Male, Multifactorial Inheritance, NADPH Oxidase 4, NADPH Oxidases, Phenotype, Pregnancy-Associated Plasma Protein-A, Procollagen N-Endopeptidase, Proteoglycans, Proteolysis, Receptors, Androgen, Somatomedins, EPIC-InterAct Consortium, CHD Exome+ Consortium, ExomeBP Consortium, T2D-Genes Consortium, GoT2D Genes Consortium, Global Lipids Genetics Consortium, ReproGen Consortium, MAGIC Investigators, General Science & Technology
Abstract

Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

Published
2017

36. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation

Author

Chu, Audrey Y, Deng, Xuan, Fisher, Virginia A, Drong, Alexander, Zhang, Yang, Feitosa, Mary F, Liu, Ching-Ti, Weeks, Olivia, Choh, Audrey C, Duan, Qing, Dyer, Thomas D, Eicher, John D, Guo, Xiuqing, Heard-Costa, Nancy L, Kacprowski, Tim, Kent, Jack W, Lange, Leslie A, Liu, Xinggang, Lohman, Kurt, Lu, Lingyi, Mahajan, Anubha, O'Connell, Jeffrey R, Parihar, Ankita, Peralta, Juan M, Smith, Albert V, Zhang, Yi, Homuth, Georg, Kissebah, Ahmed H, Kullberg, Joel, Laqua, René, Launer, Lenore J, Nauck, Matthias, Olivier, Michael, Peyser, Patricia A, Terry, James G, Wojczynski, Mary K, Yao, Jie, Bielak, Lawrence F, Blangero, John, Borecki, Ingrid B, Bowden, Donald W, Carr, John Jeffrey, Czerwinski, Stefan A, Ding, Jingzhong, Friedrich, Nele, Gudnason, Vilmunder, Harris, Tamara B, Ingelsson, Erik, Johnson, Andrew D, Kardia, Sharon LR, Langefeld, Carl D, Lind, Lars, Liu, Yongmei, Mitchell, Braxton D, Morris, Andrew P, Mosley, Thomas H, Rotter, Jerome I, Shuldiner, Alan R, Towne, Bradford, Völzke, Henry, Wallaschofski, Henri, Wilson, James G, Allison, Matthew, Lindgren, Cecilia M, Goessling, Wolfram, Cupples, L Adrienne, Steinhauser, Matthew L, and Fox, Caroline S

Subjects
Nutrition, Human Genome, Genetics, Adipocytes, Animals, Body Fat Distribution, Cell Differentiation, Cohort Studies, Ethnicity, Female, Genetic Loci, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Mice, Mice, Inbred C57BL, Obesity, Phenotype, Polymorphism, Single Nucleotide, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Variation in body fat distribution contributes to the metabolic sequelae of obesity. The genetic determinants of body fat distribution are poorly understood. The goal of this study was to gain new insights into the underlying genetics of body fat distribution by conducting sample-size-weighted fixed-effects genome-wide association meta-analyses in up to 9,594 women and 8,738 men of European, African, Hispanic and Chinese ancestry, with and without sex stratification, for six traits associated with ectopic fat (hereinafter referred to as ectopic-fat traits). In total, we identified seven new loci associated with ectopic-fat traits (ATXN1, UBE2E2, EBF1, RREB1, GSDMB, GRAMD3 and ENSA; P < 5 × 10-8; false discovery rate < 1%). Functional analysis of these genes showed that loss of function of either Atxn1 or Ube2e2 in primary mouse adipose progenitor cells impaired adipocyte differentiation, suggesting physiological roles for ATXN1 and UBE2E2 in adipogenesis. Future studies are necessary to further explore the mechanisms by which these genes affect adipocyte biology and how their perturbations contribute to systemic metabolic disease.

Published
2017

37. Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis

Author

Natarajan, Pradeep, Bis, Joshua C, Bielak, Lawrence F, Cox, Amanda J, Dörr, Marcus, Feitosa, Mary F, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, Isaacs, Aaron, Jhun, Min A, Kavousi, Maryam, Li-Gao, Ruifang, Lyytikäinen, Leo-Pekka, Marioni, Riccardo E, Schminke, Ulf, Stitziel, Nathan O, Tada, Hayato, van Setten, Jessica, Smith, Albert V, Vojinovic, Dina, Yanek, Lisa R, Yao, Jie, Yerges-Armstrong, Laura M, Amin, Najaf, Baber, Usman, Borecki, Ingrid B, Carr, J Jeffrey, Chen, Yii-Der Ida, Cupples, L Adrienne, de Jong, Pim A, de Koning, Harry, de Vos, Bob D, Demirkan, Ayse, Fuster, Valentin, Franco, Oscar H, Goodarzi, Mark O, Harris, Tamara B, Heckbert, Susan R, Heiss, Gerardo, Hoffmann, Udo, Hofman, Albert, Išgum, Ivana, Jukema, J Wouter, Kähönen, Mika, Kardia, Sharon LR, Kral, Brian G, Launer, Lenore J, Massaro, Joe, Mehran, Roxana, Mitchell, Braxton D, Mosley, Thomas H, de Mutsert, Renée, Newman, Anne B, Nguyen, Khanh-Dung, North, Kari E, O'Connell, Jeffrey R, Oudkerk, Matthijs, Pankow, James S, Peloso, Gina M, Post, Wendy, Province, Michael A, Raffield, Laura M, Raitakari, Olli T, Reilly, Dermot F, Rivadeneira, Fernando, Rosendaal, Frits, Sartori, Samantha, Taylor, Kent D, Teumer, Alexander, Trompet, Stella, Turner, Stephen T, Uitterlinden, Andre G, Vaidya, Dhananjay, van der Lugt, Aad, Völker, Uwe, Wardlaw, Joanna M, Wassel, Christina L, Weiss, Stefan, Wojczynski, Mary K, Becker, Diane M, Becker, Lewis C, Boerwinkle, Eric, Bowden, Donald W, Deary, Ian J, Dehghan, Abbas, Felix, Stephan B, Gudnason, Vilmundur, Lehtimäki, Terho, Mathias, Rasika, Mook-Kanamori, Dennis O, Psaty, Bruce M, Rader, Daniel J, Rotter, Jerome I, Wilson, James G, van Duijn, Cornelia M, Völzke, Henry, Kathiresan, Sekar, Peyser, Patricia A, and O'Donnell, Christopher J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Heart Disease - Coronary Heart Disease, Aging, Human Genome, Atherosclerosis, Cardiovascular, Heart Disease, Clinical Research, Genetics, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Apolipoprotein B-100, Apolipoprotein E2, Asymptomatic Diseases, Black People, Carotid Artery Diseases, Carotid Intima-Media Thickness, Cholesterol, LDL, Computed Tomography Angiography, Coronary Angiography, Coronary Artery Disease, Exome, Gene Frequency, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Odds Ratio, Oligonucleotide Array Sequence Analysis, Phenotype, Prognosis, Risk Assessment, Risk Factors, Vascular Calcification, White People, carotid intima-media thickness, coronary artery calcification, exome, genome-wide association study, genomics, CHARGE Consortium, carotid intima–media thickness, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundThe burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease. We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent coronary heart disease.Methods and resultsWe studied a total of 25 109 European ancestry and African ancestry participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52 869 participants with common carotid intima-media thickness measured by ultrasonography within the CHARGE Consortium (Cohorts for Heart and Aging Research in Genomic Epidemiology). Participants were genotyped for 247 870 DNA sequence variants (231 539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and carotid intima-media thickness. APOB p.Arg3527Gln was associated with 4-fold excess CAC (P=3×10-10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P=1×10-12) and 1.4% reduced carotid intima-media thickness (P=4×10-14) in carriers compared with noncarriers. In secondary analyses conditioning on low-density lipoprotein cholesterol concentration, the ε2 protective association with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2 was associated with reduced risk for coronary heart disease (odds ratio 0.77; P=1×10-11).ConclusionsExome-wide association meta-analysis demonstrates that protein-coding variants in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first significant association for multiple subclinical atherosclerosis traits across multiple ethnicities, as well as clinical coronary heart disease.

Published
2016

38. An Empirical Comparison of Joint and Stratified Frameworks for Studying G × E Interactions: Systolic Blood Pressure and Smoking in the CHARGE Gene‐Lifestyle Interactions Working Group

Author

Sung, Yun Ju, Winkler, Thomas W, Manning, Alisa K, Aschard, Hugues, Gudnason, Vilmundur, Harris, Tamara B, Smith, Albert V, Boerwinkle, Eric, Brown, Michael R, Morrison, Alanna C, Fornage, Myriam, Lin, Li-An, Richard, Melissa, Bartz, Traci M, Psaty, Bruce M, Hayward, Caroline, Polasek, Ozren, Marten, Jonathan, Rudan, Igor, Feitosa, Mary F, Kraja, Aldi T, Province, Michael A, Deng, Xuan, Fisher, Virginia A, Zhou, Yanhua, Bielak, Lawrence F, Smith, Jennifer, Huffman, Jennifer E, Padmanabhan, Sandosh, Smith, Blair H, Ding, Jingzhong, Liu, Yongmei, Lohman, Kurt, Bouchard, Claude, Rankinen, Tuomo, Rice, Treva K, Arnett, Donna, Schwander, Karen, Guo, Xiuqing, Palmas, Walter, Rotter, Jerome I, Alfred, Tamuno, Bottinger, Erwin P, Loos, Ruth JF, Amin, Najaf, Franco, Oscar H, van Duijn, Cornelia M, Vojinovic, Dina, Chasman, Daniel I, Ridker, Paul M, Rose, Lynda M, Kardia, Sharon, Zhu, Xiaofeng, Rice, Kenneth, Borecki, Ingrid B, Rao, Dabeeru C, Gauderman, W James, and Cupples, L Adrienne

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Clinical Research, Human Genome, Blood Pressure, Cohort Studies, Databases, Factual, Family, Gene Frequency, Gene-Environment Interaction, Genome-Wide Association Study, Genotype, Humans, Phenotype, Smoking, gene-environment interaction, meta-analysis, low-frequency variants, Public Health and Health Services
Abstract

Studying gene-environment (G × E) interactions is important, as they extend our knowledge of the genetic architecture of complex traits and may help to identify novel variants not detected via analysis of main effects alone. The main statistical framework for studying G × E interactions uses a single regression model that includes both the genetic main and G × E interaction effects (the "joint" framework). The alternative "stratified" framework combines results from genetic main-effect analyses carried out separately within the exposed and unexposed groups. Although there have been several investigations using theory and simulation, an empirical comparison of the two frameworks is lacking. Here, we compare the two frameworks using results from genome-wide association studies of systolic blood pressure for 3.2 million low frequency and 6.5 million common variants across 20 cohorts of European ancestry, comprising 79,731 individuals. Our cohorts have sample sizes ranging from 456 to 22,983 and include both family-based and population-based samples. In cohort-specific analyses, the two frameworks provided similar inference for population-based cohorts. The agreement was reduced for family-based cohorts. In meta-analyses, agreement between the two frameworks was less than that observed in cohort-specific analyses, despite the increased sample size. In meta-analyses, agreement depended on (1) the minor allele frequency, (2) inclusion of family-based cohorts in meta-analysis, and (3) filtering scheme. The stratified framework appears to approximate the joint framework well only for common variants in population-based cohorts. We conclude that the joint framework is the preferred approach and should be used to control false positives when dealing with low-frequency variants and/or family-based cohorts.

Published
2016

39. Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Author

Liu, Ching-Ti, Raghavan, Sridharan, Maruthur, Nisa, Kabagambe, Edmond Kato, Hong, Jaeyoung, Ng, Maggie CY, Hivert, Marie-France, Lu, Yingchang, An, Ping, Bentley, Amy R, Drolet, Anne M, Gaulton, Kyle J, Guo, Xiuqing, Armstrong, Loren L, Irvin, Marguerite R, Li, Man, Lipovich, Leonard, Rybin, Denis V, Taylor, Kent D, Agyemang, Charles, Palmer, Nicholette D, Cade, Brian E, Chen, Wei-Min, Dauriz, Marco, Delaney, Joseph AC, Edwards, Todd L, Evans, Daniel S, Evans, Michele K, Lange, Leslie A, Leong, Aaron, Liu, Jingmin, Liu, Yongmei, Nayak, Uma, Patel, Sanjay R, Porneala, Bianca C, Rasmussen-Torvik, Laura J, Snijder, Marieke B, Stallings, Sarah C, Tanaka, Toshiko, Yanek, Lisa R, Zhao, Wei, Becker, Diane M, Bielak, Lawrence F, Biggs, Mary L, Bottinger, Erwin P, Bowden, Donald W, Chen, Guanjie, Correa, Adolfo, Couper, David J, Crawford, Dana C, Cushman, Mary, Eicher, John D, Fornage, Myriam, Franceschini, Nora, Fu, Yi-Ping, Goodarzi, Mark O, Gottesman, Omri, Hara, Kazuo, Harris, Tamara B, Jensen, Richard A, Johnson, Andrew D, Jhun, Min A, Karter, Andrew J, Keller, Margaux F, Kho, Abel N, Kizer, Jorge R, Krauss, Ronald M, Langefeld, Carl D, Li, Xiaohui, Liang, Jingling, Liu, Simin, Lowe, William L, Mosley, Thomas H, North, Kari E, Pacheco, Jennifer A, Peyser, Patricia A, Patrick, Alan L, Rice, Kenneth M, Selvin, Elizabeth, Sims, Mario, Smith, Jennifer A, Tajuddin, Salman M, Vaidya, Dhananjay, Wren, Mary P, Yao, Jie, Zhu, Xiaofeng, Ziegler, Julie T, Zmuda, Joseph M, Zonderman, Alan B, Zwinderman, Aeilko H, Consortium, AAAG, Consortium, CARe, Consortium, COGENT-BP, Consortium, eMERGE, Consortium, MEDIA, Adeyemo, Adebowale, Boerwinkle, Eric, Ferrucci, Luigi, Hayes, M Geoffrey, and Kardia, Sharon LR

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Health Disparities, Human Genome, Diabetes, Minority Health, Metabolic and endocrine, Asian People, Black People, Blood Glucose, Diabetes Mellitus, Type 2, Enhancer Elements, Genetic, Ethnicity, Fasting, Female, Gene Frequency, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Introns, Islets of Langerhans, Male, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Transcription Factors, White People, AAAG Consortium, CARe Consortium, COGENT-BP Consortium, eMERGE Consortium, MEDIA Consortium, MAGIC Consortium, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loci.

Published
2016

40. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

de Vries, Paul S, Chasman, Daniel I, Sabater-Lleal, Maria, Chen, Ming-Huei, Huffman, Jennifer E, Steri, Maristella, Tang, Weihong, Teumer, Alexander, Marioni, Riccardo E, Grossmann, Vera, Hottenga, Jouke J, Trompet, Stella, Müller-Nurasyid, Martina, Zhao, Jing Hua, Brody, Jennifer A, Kleber, Marcus E, Guo, Xiuqing, Wang, Jie Jin, Auer, Paul L, Attia, John R, Yanek, Lisa R, Ahluwalia, Tarunveer S, Lahti, Jari, Venturini, Cristina, Tanaka, Toshiko, Bielak, Lawrence F, Joshi, Peter K, Rocanin-Arjo, Ares, Kolcic, Ivana, Navarro, Pau, Rose, Lynda M, Oldmeadow, Christopher, Riess, Helene, Mazur, Johanna, Basu, Saonli, Goel, Anuj, Yang, Qiong, Ghanbari, Mohsen, Willemsen, Gonneke, Rumley, Ann, Fiorillo, Edoardo, de Craen, Anton JM, Grotevendt, Anne, Scott, Robert, Taylor, Kent D, Delgado, Graciela E, Yao, Jie, Kifley, Annette, Kooperberg, Charles, Qayyum, Rehan, Lopez, Lorna M, Berentzen, Tina L, Räikkönen, Katri, Mangino, Massimo, Bandinelli, Stefania, Peyser, Patricia A, Wild, Sarah, Trégouët, David-Alexandre, Wright, Alan F, Marten, Jonathan, Zemunik, Tatijana, Morrison, Alanna C, Sennblad, Bengt, Tofler, Geoffrey, de Maat, Moniek PM, de Geus, Eco JC, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Binder, Harald, Völker, Uwe, Waldenberger, Melanie, Khaw, Kay-Tee, Mcknight, Barbara, Huang, Jie, Jenny, Nancy S, Holliday, Elizabeth G, Qi, Lihong, Mcevoy, Mark G, Becker, Diane M, Starr, John M, Sarin, Antti-Pekka, Hysi, Pirro G, Hernandez, Dena G, Jhun, Min A, Campbell, Harry, Hamsten, Anders, Rivadeneira, Fernando, Mcardle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Koenig, Wolfgang, Psaty, Bruce M, Haritunians, Talin, Liu, Jingmin, Palotie, Aarno, Uitterlinden, André G, Stott, David J, Hofman, Albert, and Franco, Oscar H

Subjects
Biological Sciences, Genetics, Human Genome, Adult, Aged, Aged, 80 and over, Female, Fibrinogen, Genetic Loci, Genome-Wide Association Study, Humans, INDEL Mutation, Male, Middle Aged, Polymorphism, Single Nucleotide, White People, Medical and Health Sciences, Genetics & Heredity
Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ∼120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016

41. Directional dominance on stature and cognition in diverse human populations

Author

Joshi, Peter K, Esko, Tonu, Mattsson, Hannele, Eklund, Niina, Gandin, Ilaria, Nutile, Teresa, Jackson, Anne U, Schurmann, Claudia, Smith, Albert V, Zhang, Weihua, Okada, Yukinori, Stančáková, Alena, Faul, Jessica D, Zhao, Wei, Bartz, Traci M, Concas, Maria Pina, Franceschini, Nora, Enroth, Stefan, Vitart, Veronique, Trompet, Stella, Guo, Xiuqing, Chasman, Daniel I, O'Connel, Jeffrey R, Corre, Tanguy, Nongmaithem, Suraj S, Chen, Yuning, Mangino, Massimo, Ruggiero, Daniela, Traglia, Michela, Farmaki, Aliki-Eleni, Kacprowski, Tim, Bjonnes, Andrew, van der Spek, Ashley, Wu, Ying, Giri, Anil K, Yanek, Lisa R, Wang, Lihua, Hofer, Edith, Rietveld, Cornelius A, McLeod, Olga, Cornelis, Marilyn C, Pattaro, Cristian, Verweij, Niek, Baumbach, Clemens, Abdellaoui, Abdel, Warren, Helen R, Vuckovic, Dragana, Mei, Hao, Bouchard, Claude, Perry, John RB, Cappellani, Stefania, Mirza, Saira S, Benton, Miles C, Broeckel, Ulrich, Medland, Sarah E, Lind, Penelope A, Malerba, Giovanni, Drong, Alexander, Yengo, Loic, Bielak, Lawrence F, Zhi, Degui, van der Most, Peter J, Shriner, Daniel, Mägi, Reedik, Hemani, Gibran, Karaderi, Tugce, Wang, Zhaoming, Liu, Tian, Demuth, Ilja, Zhao, Jing Hua, Meng, Weihua, Lataniotis, Lazaros, van der Laan, Sander W, Bradfield, Jonathan P, Wood, Andrew R, Bonnefond, Amelie, Ahluwalia, Tarunveer S, Hall, Leanne M, Salvi, Erika, Yazar, Seyhan, Carstensen, Lisbeth, de Haan, Hugoline G, Abney, Mark, Afzal, Uzma, Allison, Matthew A, Amin, Najaf, Asselbergs, Folkert W, Bakker, Stephan JL, Barr, R Graham, Baumeister, Sebastian E, Benjamin, Daniel J, Bergmann, Sven, Boerwinkle, Eric, Bottinger, Erwin P, Campbell, Archie, Chakravarti, Aravinda, Chan, Yingleong, Chanock, Stephen J, Chen, Constance, and Chen, Y-D Ida

Subjects
Biological Sciences, Genetics, Clinical Research, Human Genome, Biological Evolution, Blood Pressure, Body Height, Cholesterol, LDL, Cognition, Cohort Studies, Educational Status, Female, Forced Expiratory Volume, Genome, Human, Homozygote, Humans, Lung Volume Measurements, Male, Phenotype, General Science & Technology
Abstract

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10(-300), 2.1 × 10(-6), 2.5 × 10(-10) and 1.8 × 10(-10), respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

Published
2015

42. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes.

Author

Ng, Maggie CY, Shriner, Daniel, Chen, Brian H, Li, Jiang, Chen, Wei-Min, Guo, Xiuqing, Liu, Jiankang, Bielinski, Suzette J, Yanek, Lisa R, Nalls, Michael A, Comeau, Mary E, Rasmussen-Torvik, Laura J, Jensen, Richard A, Evans, Daniel S, Sun, Yan V, An, Ping, Patel, Sanjay R, Lu, Yingchang, Long, Jirong, Armstrong, Loren L, Wagenknecht, Lynne, Yang, Lingyao, Snively, Beverly M, Palmer, Nicholette D, Mudgal, Poorva, Langefeld, Carl D, Keene, Keith L, Freedman, Barry I, Mychaleckyj, Josyf C, Nayak, Uma, Raffel, Leslie J, Goodarzi, Mark O, Chen, Y-D Ida, Taylor, Herman A, Correa, Adolfo, Sims, Mario, Couper, David, Pankow, James S, Boerwinkle, Eric, Adeyemo, Adebowale, Doumatey, Ayo, Chen, Guanjie, Mathias, Rasika A, Vaidya, Dhananjay, Singleton, Andrew B, Zonderman, Alan B, Igo, Robert P, Sedor, John R, FIND Consortium, Kabagambe, Edmond K, Siscovick, David S, McKnight, Barbara, Rice, Kenneth, Liu, Yongmei, Hsueh, Wen-Chi, Zhao, Wei, Bielak, Lawrence F, Kraja, Aldi, Province, Michael A, Bottinger, Erwin P, Gottesman, Omri, Cai, Qiuyin, Zheng, Wei, Blot, William J, Lowe, William L, Pacheco, Jennifer A, Crawford, Dana C, eMERGE Consortium, DIAGRAM Consortium, Grundberg, Elin, MuTHER Consortium, Rich, Stephen S, Hayes, M Geoffrey, Shu, Xiao-Ou, Loos, Ruth JF, Borecki, Ingrid B, Peyser, Patricia A, Cummings, Steven R, Psaty, Bruce M, Fornage, Myriam, Iyengar, Sudha K, Evans, Michele K, Becker, Diane M, Kao, WH Linda, Wilson, James G, Rotter, Jerome I, Sale, Michèle M, Liu, Simin, Rotimi, Charles N, Bowden, Donald W, and MEta-analysis of type 2 DIabetes in African Americans Consortium

Subjects
FIND Consortium, eMERGE Consortium, DIAGRAM Consortium, MuTHER Consortium, MEta-analysis of type 2 DIabetes in African Americans Consortium, Humans, Diabetes Mellitus, Type 2, HMGA2 Protein, HLA-B27 Antigen, Polymorphism, Single Nucleotide, African Americans, Mutant Chimeric Proteins, KCNQ1 Potassium Channel, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Human Genome, Diabetes, Genetics, 2.1 Biological and endogenous factors, Metabolic and endocrine, Developmental Biology
Abstract

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)

Published
2014

43. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry

Author

Monda, Keri L, Chen, Gary K, Taylor, Kira C, Palmer, Cameron, Edwards, Todd L, Lange, Leslie A, Ng, Maggie CY, Adeyemo, Adebowale A, Allison, Matthew A, Bielak, Lawrence F, Chen, Guanjie, Graff, Mariaelisa, Irvin, Marguerite R, Rhie, Suhn K, Li, Guo, Liu, Yongmei, Liu, Youfang, Lu, Yingchang, Nalls, Michael A, Sun, Yan V, Wojczynski, Mary K, Yanek, Lisa R, Aldrich, Melinda C, Ademola, Adeyinka, Amos, Christopher I, Bandera, Elisa V, Bock, Cathryn H, Britton, Angela, Broeckel, Ulrich, Cai, Quiyin, Caporaso, Neil E, Carlson, Chris S, Carpten, John, Casey, Graham, Chen, Wei-Min, Chen, Fang, Chen, Yii-Der I, Chiang, Charleston WK, Coetzee, Gerhard A, Demerath, Ellen, Deming-Halverson, Sandra L, Driver, Ryan W, Dubbert, Patricia, Feitosa, Mary F, Feng, Ye, Freedman, Barry I, Gillanders, Elizabeth M, Gottesman, Omri, Guo, Xiuqing, Haritunians, Talin, Harris, Tamara, Harris, Curtis C, Hennis, Anselm JM, Hernandez, Dena G, McNeill, Lorna H, Howard, Timothy D, Howard, Barbara V, Howard, Virginia J, Johnson, Karen C, Kang, Sun J, Keating, Brendan J, Kolb, Suzanne, Kuller, Lewis H, Kutlar, Abdullah, Langefeld, Carl D, Lettre, Guillaume, Lohman, Kurt, Lotay, Vaneet, Lyon, Helen, Manson, JoAnn E, Maixner, William, Meng, Yan A, Monroe, Kristine R, Morhason-Bello, Imran, Murphy, Adam B, Mychaleckyj, Josyf C, Nadukuru, Rajiv, Nathanson, Katherine L, Nayak, Uma, N'Diaye, Amidou, Nemesure, Barbara, Wu, Suh-Yuh, Leske, M Cristina, Neslund-Dudas, Christine, Neuhouser, Marian, Nyante, Sarah, Ochs-Balcom, Heather, Ogunniyi, Adesola, Ogundiran, Temidayo O, Ojengbede, Oladosu, Olopade, Olufunmilayo I, Palmer, Julie R, Ruiz-Narvaez, Edward A, Palmer, Nicholette D, Press, Michael F, Rampersaud, Evandine, Rasmussen-Torvik, Laura J, Rodriguez-Gil, Jorge L, Salako, Babatunde, and Schadt, Eric E

Subjects
Biological Sciences, Genetics, Human Genome, Black or African American, Body Mass Index, Case-Control Studies, Gene Frequency, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Obesity, Polymorphism, Single Nucleotide, NABEC Consortium, UKBEC Consortium, BioBank Japan Project, AGEN Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of >3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.

Published
2013

44. Genome-Wide Association of Body Fat Distribution in African Ancestry Populations Suggests New Loci

Author

Liu, Ching-Ti, Monda, Keri L, Taylor, Kira C, Lange, Leslie, Demerath, Ellen W, Palmas, Walter, Wojczynski, Mary K, Ellis, Jaclyn C, Vitolins, Mara Z, Liu, Simin, Papanicolaou, George J, Irvin, Marguerite R, Xue, Luting, Griffin, Paula J, Nalls, Michael A, Adeyemo, Adebowale, Liu, Jiankang, Li, Guo, Ruiz-Narvaez, Edward A, Chen, Wei-Min, Chen, Fang, Henderson, Brian E, Millikan, Robert C, Ambrosone, Christine B, Strom, Sara S, Guo, Xiuqing, Andrews, Jeanette S, Sun, Yan V, Mosley, Thomas H, Yanek, Lisa R, Shriner, Daniel, Haritunians, Talin, Rotter, Jerome I, Speliotes, Elizabeth K, Smith, Megan, Rosenberg, Lynn, Mychaleckyj, Josyf, Nayak, Uma, Spruill, Ida, Garvey, W Timothy, Pettaway, Curtis, Nyante, Sarah, Bandera, Elisa V, Britton, Angela F, Zonderman, Alan B, Rasmussen-Torvik, Laura J, Chen, Yii-Der Ida, Ding, Jingzhong, Lohman, Kurt, Kritchevsky, Stephen B, Zhao, Wei, Peyser, Patricia A, Kardia, Sharon LR, Kabagambe, Edmond, Broeckel, Ulrich, Chen, Guanjie, Zhou, Jie, Wassertheil-Smoller, Sylvia, Neuhouser, Marian L, Rampersaud, Evadnie, Psaty, Bruce, Kooperberg, Charles, Manson, JoAnn E, Kuller, Lewis H, Ochs-Balcom, Heather M, Johnson, Karen C, Sucheston, Lara, Ordovas, Jose M, Palmer, Julie R, Haiman, Christopher A, McKnight, Barbara, Howard, Barbara V, Becker, Diane M, Bielak, Lawrence F, Liu, Yongmei, Allison, Matthew A, Grant, Struan FA, Burke, Gregory L, Patel, Sanjay R, Schreiner, Pamela J, Borecki, Ingrid B, Evans, Michele K, Taylor, Herman, Sale, Michele M, Howard, Virginia, Carlson, Christopher S, Rotimi, Charles N, Cushman, Mary, Harris, Tamara B, Reiner, Alexander P, Cupples, L Adrienne, North, Kari E, and Fox, Caroline S

Subjects
Biological Sciences, Genetics, Human Genome, Nutrition, Obesity, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Adiposity, Black People, Body Fat Distribution, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Waist-Hip Ratio, White People, Developmental Biology
Abstract

Central obesity, measured by waist circumference (WC) or waist-hip ratio (WHR), is a marker of body fat distribution. Although obesity disproportionately affects minority populations, few studies have conducted genome-wide association study (GWAS) of fat distribution among those of predominantly African ancestry (AA). We performed GWAS of WC and WHR, adjusted and unadjusted for BMI, in up to 33,591 and 27,350 AA individuals, respectively. We identified loci associated with fat distribution in AA individuals using meta-analyses of GWA results for WC and WHR (stage 1). Overall, 25 SNPs with single genomic control (GC)-corrected p-values

Published
2013

45. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E, Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L, Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Zajac, Greg J M, Wu, Kuan-Han H, Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T, Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Havulinna, Aki S, Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A, Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J, Narita, Akira, Takayama, Jun, Martin, Hilary C, Hunt, Karen A, Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Rasheed, Asif, Hindy, George, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian'an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A, Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agnes, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Mauro, Pala, Matteo, Floris, McDaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T, Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Schönherr, Sebastian, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E, Bradfield, Jonathan P, Ruotsalainen, Sanni E, Daw, E Warwick, Zmuda, Joseph M, Mitchell, Jonathan S, Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A, Le, Phuong, Feitosa, Mary F, Wojczynski, Mary K, Hemerich, Daiane, Preuss, Michael, Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W, Engmann, Jorgen, Noah, Tsao L, Verma, Anurag, Slieker, Roderick C, Lo, Ken Sin, Zilhao, Nuno R, Kleber, Marcus E, Delgado, Graciela E, Huo, Shaofeng, Ikeda, Daisuke D, Iha, Hiroyuki, Yang, Jian, Liu, Jun, Demirkan, Ayşe, Leonard, Hampton L, Marten, Jonathan, Emmel, Carina, Schmidt, Börge, Smyth, Laura J, Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Nongmaithem, Suraj S, Sankareswaran, Alagu, Irvin, Marguerite R, Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R H J, Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A, Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N, Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C, Wang, Ya Xing, Wei, Wen B, Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A, Banas, Bernhard, Morgan, Anna, Meidtner, Karina, Bielak, Lawrence F, Smith, Jennifer A, Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J, Pitkänen, Niina, Cade, Brian E, van der Laan, Sander W, Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Bentley, Amy R, Doumatey, Ayo P, Adeyemo, Adebowale A, Lee, Jong Young, Petersen, Eva R B, Nielsen, Aneta A, Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W, Wang, Carol A, Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hidalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O, van Setten, Jessica, He, Karen Y, Tang, Hua, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D, Reiner, Alexander P, Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C, Launer, Lenore J, Li, Huaixing, Nalls, Mike A, Raitakari, Olli T, Ichihara, Sahoko, Wild, Sarah H, Nelson, Christopher P, Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S, Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, de Kleijn, Dominique, de Borst, Gert J, Kim, Eung Kweon, Adams, Hieab H H, Ikram, M Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W, Kraaijeveld, Adriaan O, Beulens, Joline W J, Shu, Xiao-Ou, Rallidis, Loukianos S, Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W, Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Ida Chen, Yii-Der, Pennell, Craig E, Mori, Trevor A, Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M, Stark, Klaus J, Zimmermann, Martina E, Völzke, Henry, Homuth, Georg, Evans, Michele K, Zonderman, Alan B, Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E, Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J, Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Peyser, Patricia A, Kato, Norihiro, Schulze, Matthias B, Girotto, Giorgia, Böger, Carsten A, Jung, Bettina, Joshi, Peter K, Bennett, David A, De Jager, Philip L, Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J, Munroe, Patricia B, Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B, Samani, Nilesh J, Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A, Adair, Linda S, Bechayda, Sonny Augustin, de Silva, H Janaka, Wickremasinghe, Ananda R, Krauss, Ronald M, Wu, Jer-Yuarn, Zheng, Wei, den Hollander, Anneke I, Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G, Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E, Golightly, Yvonne M, Wilson, James F, Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J, Rao, D C, Arnett, Donna K, Walker, Mark, Scott, Laura J, Koistinen, Heikki A, Chandak, Giriraj R, Mercader, Josep M, Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, E Shyong, van Dam, Rob M, Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F, McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I, Palmer, Colin N A, Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M, Jin, Zi-Bing, Lu, Fan, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, T Hart, Leen M, Elders, Petra J M, Rader, Daniel J, Damrauer, Scott M, Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D, Loos, Ruth J F, Province, Michael A, Parra, Esteban J, Cruz, Miguel, Psaty, Bruce M, Brandslund, Ivan, Pramstaller, Peter P, Rotimi, Charles N, Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F A, Kiemeney, Lambertus, de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W, Linneberg, Allan, Jukema, J Wouter, Khera, Amit V, Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Francesco, Cucca, Mook-Kanamori, Dennis O, Willems van Dijk, Ko, Watkins, Hugh, Strachan, David P, Grarup, Niels, Sever, Peter, Poulter, Neil, Huey-Herng Sheu, Wayne, Rotter, Jerome I, Dantoft, Thomas M, Karpe, Fredrik, Neville, Matt J, Timpson, Nicholas J, Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Li, Hengtong, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T, Pedersen, Nancy L, Magnusson, Patrik K E, Boomsma, Dorret I, de Geus, Eco J C, Cupples, L Adrienne, van Meurs, Joyce B J, Ikram, Arfan, Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J, Langenberg, Claudia, Zeggini, Eleftheria, Tuomilehto, Jaakko, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C, Kooner, Jaspal S, de Vries, Paul S, Morrison, Alanna C, Hazelhurst, Scott, Ramsay, Michèle, North, Kari E, Daviglus, Martha, Kraft, Peter, Martin, Nicholas G, Whitfield, John B, Abbas, Shahid, Saleheen, Danish, Walters, Robin G, Holmes, Michael V, Black, Corri, Smith, Blair H, Baras, Aris, Justice, Anne E, Buring, Julie E, Ridker, Paul M, Chasman, Daniel I, Kooperberg, Charles, Tamiya, Gen, Yamamoto, Masayuki, van Heel, David A, Trembath, Richard C, Wei, Wei-Qi, Jarvik, Gail P, Namjou, Bahram, Hayes, M Geoffrey, Ritchie, Marylyn D, Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Kim, Bong-Jo, Thorsteinsdottir, Unnur, Stefansson, Kari, Zhang, Jifeng, Chen, Y Eugene, Ho, Yuk-Lam, Lynch, Julie A, Tsao, Philip S, Chang, Kyong-Mi, Cho, Kelly, O'Donnell, Christopher J, Gaziano, John M, Wilson, Peter, Mohlke, Karen L, Frayling, Timothy M, Hirschhorn, Joel N, Kathiresan, Sekar, Boehnke, Michael, Million Veterans Program, Global Lipids Genetics Consortium, Struan Grant, Natarajan, Pradeep, Sun, Yan V, Morris, Andrew P, Deloukas, Panos, Peloso, Gina, Assimes, Themistocles L, Willer, Cristen J, Zhu, Xiang, Brown, Christopher D, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), Tampere University, Clinical Medicine, Department of Clinical Chemistry, TAYS Heart Centre, Department of Clinical Physiology and Nuclear Medicine, Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E, Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L, Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Zajac, Greg J M, Wu, Kuan-Han H, Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T, Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Havulinna, Aki S, Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A, Lingren, Todd, Feng, Qiping, Kullo, Iftikhar J, Narita, Akira, Takayama, Jun, Martin, Hilary C, Hunt, Karen A, Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Rasheed, Asif, Hindy, George, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian'An, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achillea, Hottenga, Jouke Jan, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A, Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agne, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Mauro, Pala, Matteo, Flori, Mcdaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthia, Trompet, Stella, Sattar, Naveed, Møllehave, Line T, Munz, Matthia, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Schönherr, Sebastian, Forer, Luka, Scholz, Marku, Galesloot, Tessel E, Bradfield, Jonathan P, Ruotsalainen, Sanni E, Daw, E Warwick, Zmuda, Joseph M, Mitchell, Jonathan S, Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A, Le, Phuong, Feitosa, Mary F, Wojczynski, Mary K, Hemerich, Daiane, Preuss, Michael, Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W, Engmann, Jorgen, Noah, Tsao L, Verma, Anurag, Slieker, Roderick C, Lo, Ken Sin, Zilhao, Nuno R, Kleber, Marcus E, Delgado, Graciela E, Huo, Shaofeng, Ikeda, Daisuke D, Iha, Hiroyuki, Yang, Jian, Liu, Jun, Demirkan, Ayşe, Leonard, Hampton L, Marten, Jonathan, Emmel, Carina, Schmidt, Börge, Smyth, Laura J, Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlo, Lyssenko, Valeriya, Nongmaithem, Suraj S, Sankareswaran, Alagu, Irvin, Marguerite R, Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R H J, Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A, Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N, Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C, Wang, Ya Xing, Wei, Wen B, Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A, Banas, Bernhard, Morgan, Anna, Meidtner, Karina, Bielak, Lawrence F, Smith, Jennifer A, Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J, Pitkänen, Niina, Cade, Brian E, van der Laan, Sander W, Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Bentley, Amy R, Doumatey, Ayo P, Adeyemo, Adebowale A, Lee, Jong Young, Petersen, Eva R B, Nielsen, Aneta A, Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W, Wang, Carol A, Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hidalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O, van Setten, Jessica, He, Karen Y, Tang, Hua, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D, Reiner, Alexander P, Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C, Launer, Lenore J, Li, Huaixing, Nalls, Mike A, Raitakari, Olli T, Ichihara, Sahoko, Wild, Sarah H, Nelson, Christopher P, Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S, Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, de Kleijn, Dominique, de Borst, Gert J, Kim, Eung Kweon, Adams, Hieab H H, Ikram, M Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W, Kraaijeveld, Adriaan O, Beulens, Joline W J, Shu, Xiao-Ou, Rallidis, Loukianos S, Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W, Kähönen, Mika, Pérusse, Loui, Bouchard, Claude, Tönjes, Anke, Ida Chen, Yii-Der, Pennell, Craig E, Mori, Trevor A, Lieb, Wolfgang, Franke, Andre, Ohlsson, Clae, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M, Stark, Klaus J, Zimmermann, Martina E, Völzke, Henry, Homuth, Georg, Evans, Michele K, Zonderman, Alan B, Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E, Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J, Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Peyser, Patricia A, Kato, Norihiro, Schulze, Matthias B, Girotto, Giorgia, Böger, Carsten A, Jung, Bettina, Joshi, Peter K, Bennett, David A, De Jager, Philip L, Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morri, Caulfield, Mark J, Munroe, Patricia B, Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B, Samani, Nilesh J, Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A, Adair, Linda S, Bechayda, Sonny Augustin, de Silva, H Janaka, Wickremasinghe, Ananda R, Krauss, Ronald M, Wu, Jer-Yuarn, Zheng, Wei, den Hollander, Anneke I, Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G, Lind, Lar, Heng, Chew-Kiat, Nelson, Amanda E, Golightly, Yvonne M, Wilson, James F, Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J, Rao, D C, Arnett, Donna K, Walker, Mark, Scott, Laura J, Koistinen, Heikki A, Chandak, Giriraj R, Mercader, Josep M, Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, E Shyong, van Dam, Rob M, Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F, Mcknight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, Mccarthy, Mark I, Palmer, Colin N A, Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M, Jin, Zi-Bing, Lu, Fan, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, T Hart, Leen M, Elders, Petra J M, Rader, Daniel J, Damrauer, Scott M, Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D, Loos, Ruth J F, Province, Michael A, Parra, Esteban J, Cruz, Miguel, Psaty, Bruce M, Brandslund, Ivan, Pramstaller, Peter P, Rotimi, Charles N, Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F A, Kiemeney, Lambertu, de Graaf, Jacqueline, Loeffler, Marku, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W, Linneberg, Allan, Jukema, J Wouter, Khera, Amit V, Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Francesco, Cucca, Mook-Kanamori, Dennis O, Willems van Dijk, Ko, Watkins, Hugh, Strachan, David P, Grarup, Niel, Sever, Peter, Poulter, Neil, Huey-Herng Sheu, Wayne, Rotter, Jerome I, Dantoft, Thomas M, Karpe, Fredrik, Neville, Matt J, Timpson, Nicholas J, Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Li, Hengtong, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T, Pedersen, Nancy L, Magnusson, Patrik K E, Boomsma, Dorret I, de Geus, Eco J C, Cupples, L Adrienne, van Meurs, Joyce B J, Ikram, Arfan, Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J, Langenberg, Claudia, Zeggini, Eleftheria, Tuomilehto, Jaakko, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C, Kooner, Jaspal S, de Vries, Paul S, Morrison, Alanna C, Hazelhurst, Scott, Ramsay, Michèle, North, Kari E, Daviglus, Martha, Kraft, Peter, Martin, Nicholas G, Whitfield, John B, Abbas, Shahid, Saleheen, Danish, Walters, Robin G, Holmes, Michael V, Black, Corri, Smith, Blair H, Baras, Ari, Justice, Anne E, Buring, Julie E, Ridker, Paul M, Chasman, Daniel I, Kooperberg, Charle, Tamiya, Gen, Yamamoto, Masayuki, van Heel, David A, Trembath, Richard C, Wei, Wei-Qi, Jarvik, Gail P, Namjou, Bahram, Hayes, M Geoffrey, Ritchie, Marylyn D, Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Kim, Bong-Jo, Thorsteinsdottir, Unnur, Stefansson, Kari, Zhang, Jifeng, Chen, Y Eugene, Ho, Yuk-Lam, Lynch, Julie A, Tsao, Philip S, Chang, Kyong-Mi, Cho, Kelly, O'Donnell, Christopher J, Gaziano, John M, Wilson, Peter, Mohlke, Karen L, Frayling, Timothy M, Hirschhorn, Joel N, Kathiresan, Sekar, Boehnke, Michael, Struan Grant, Null, Natarajan, Pradeep, Sun, Yan V, Morris, Andrew P, Deloukas, Pano, Peloso, Gina, Assimes, Themistocles L, Willer, Cristen J, Zhu, Xiang, Brown, Christopher D, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, General practice, APH - Aging & Later Life, APH - Digital Health, Million Veterans Program, Global Lipids Genetics Consortium, Radiology & Nuclear Medicine, Internal Medicine, Epidemiology, Biological Psychology, APH - Personalized Medicine, APH - Methodology, AMS - Ageing & Vitality, and AMS - Sports

Subjects
regulatory mechanism, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], post-GWAS, Medizin, complex traits, fine-mapping, functional genomics, lipid biology, variant prioritization, Medical and Health Sciences, Polymorphism, Single Nucleotide, Sensory disorders Donders Center for Medical Neuroscience [Radboudumc 12], Article, INTEGRATIVE ANALYSIS, WIDE ASSOCIATION, 3D GENOME, VARIANTS, CHOLESTEROL, ANNOTATION, OBESITY, COMMON, LOCI, EXPRESSION, functional genomic, Genetics, 2.1 Biological and endogenous factors, Humans, Genetics(clinical), Polymorphism, Aetiology, Genetics (clinical), Genetics & Heredity, Chromatin/genetics, Genome-Wide Association Study, Genomics, Lipids/genetics, Polymorphism, Single Nucleotide/genetics, Human Genome, Million Veterans Program, Global Lipids Genetics Consortium, Single Nucleotide, Biological Sciences, Lipids, Chromatin, complex trait, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], 3111 Biomedicine, Biotechnology
Abstract

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology. Xiang Zhu is supported by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from The Pennsylvania State University. C.D.B. is supported by the NIH (R01-HL133218). Funding for the Global Lipids Genetics Consortium was provided by the NIH (R01-HL127564). This research was conducted using the UK Biobank Resource under application number 24460. This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by awards 2I01BX003362-03A1 and 1I01BX004821-01A1. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. We thank Bethany Klunder for administrative support. Study-specific acknowledgments are provided in the supplemental information.

Published
2022

46. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Author

Howe, Laurence J., Nivard, Michel G., Morris, Tim T., Hansen, Ailin F., Rasheed, Humaira, Cho, Yoonsu, Chittoor, Geetha, Ahlskog, Rafael, Lind, Penelope A., Palviainen, Teemu, van der Zee, Matthijs D., Cheesman, Rosa, Mangino, Massimo, Wang, Yunzhang, Li, Shuai, Klaric, Lucija, Ratliff, Scott M., Bielak, Lawrence F., Nygaard, Marianne, Giannelis, Alexandros, Willoughby, Emily A., Reynolds, Chandra A., Balbona, Jared V., Andreassen, Ole A., Ask, Helga, Baras, Aris, Bauer, Christopher R., Boomsma, Dorret I., Campbell, Archie, Campbell, Harry, Chen, Zhengming, Christofidou, Paraskevi, Corfield, Elizabeth, Dahm, Christina C., Dokuru, Deepika R., Evans, Luke M., de Geus, Eco J. C., Giddaluru, Sudheer, Gordon, Scott D., Harden, K. Paige, Hill, W. David, Hughes, Amanda, Kerr, Shona M., Kim, Yongkang, Kweon, Hyeokmoon, Latvala, Antti, Lawlor, Deborah A., Li, Liming, Lin, Kuang, Magnus, Per, Magnusson, Patrik K. E., Mallard, Travis T., Martikainen, Pekka, Mills, Melinda C., Njølstad, Pål Rasmus, Overton, John D., Pedersen, Nancy L., Porteous, David J., Reid, Jeffrey, Silventoinen, Karri, Southey, Melissa C., Stoltenberg, Camilla, Tucker-Drob, Elliot M., Wright, Margaret J., Koellinger, Philipp D., Benjamin, Daniel J., Turley, Patrick, Hewitt, John K., Keller, Matthew C., Stallings, Michael C., Lee, James J., Christensen, Kaare, Kardia, Sharon L. R., Peyser, Patricia A., Smith, Jennifer A., Wilson, James F., Hopper, John L., Hägg, Sara, Spector, Tim D., Pingault, Jean-Baptiste, Plomin, Robert, Havdahl, Alexandra, Bartels, Meike, Martin, Nicholas G., Oskarsson, Sven, Justice, Anne E., Millwood, Iona Y., Hveem, Kristian, Naess, Øyvind, Willer, Cristen J., Åsvold, Bjørn Olav, Kaprio, Jaakko, Medland, Sarah E., Walters, Robin G., Evans, David M., Smith, George Davey, Hayward, Caroline, Brumpton, Ben, Hemani, Gibran, Davies, Neil M., Davey Smith, George, Institute for Molecular Medicine Finland, Genetic Epidemiology, Institute of Criminology and Legal Policy, Population Research Unit (PRU), Demography, Sociology, Helsinki Inequality Initiative (INEQ), Center for Population, Health and Society, Biological Psychology, APH - Mental Health, APH - Methodology, AMS - Sports, AMS - Ageing & Vitality, APH - Personalized Medicine, Economics, Amsterdam Neuroscience - Complex Trait Genetics, and Sociology [until 2010]

Subjects
Multifactorial Inheritance, Polymorphism, Single Nucleotide, Within Family Consortium, Medical and Health Sciences, Social Science Genetic Association Consortium, Genetics, Humans, 2.1 Biological and endogenous factors, Polymorphism, Aetiology, Multifactorial Inheritance/genetics, Medicinsk genetik, Pediatric, Human Genome, 1184 Genetics, developmental biology, physiology, Single Nucleotide, Mendelian Randomization Analysis, Biological Sciences, Polymorphism, Single Nucleotide/genetics, 3142 Public health care science, environmental and occupational health, Phenotype, Mental health, Generic health relevance, Medical Genetics, Genome-Wide Association Study, Developmental Biology
Abstract

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects. Within-sibship genome-wide association analyses using data from 178,076 siblings illustrate differences between population-based and within-sibship GWAS estimates for phenotypes influenced by demographic and indirect genetic effects.

Published
2022

47. A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants.

Author

Broadaway, K. Alaine, Cutler, David J., Duncan, Richard, Moore, Jacob L., Ware, Erin B., Jhun, Min A., Bielak, Lawrence F., Zhao, Wei, Smith, Jennifer A., Peyser, Patricia A., Kardia, Sharon L.R., Ghosh, Debashis, and Epstein, Michael P.

Subjects
*GENETIC pleiotropy, *ANALYSIS of covariance, *GENETIC epidemiology, *GENE mapping, *HUMAN genetics, *HUMAN genome
Abstract

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. [ABSTRACT FROM AUTHOR]

Published
2016
Full Text
View/download PDF

48. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

Author

Alexandre C. Pereira, Jie Yao, Makoto Hirata, Ozren Polasek, Dragana Vuckovic, Mariaelisa Graff, Jing Hua Zhao, Michael R. Brown, Xuan Deng, Barbara V. Howard, Vilmundur Gudnason, Tuomo Rankinen, Yuan Shi, Timo A. Lakka, Ulrich Broeckel, Anuradhani Kasturiratne, Eric Boerwinkle, Charles Kooperberg, Hugues Aschard, Stephen S. Rich, Pamela J. Schreiner, Dan E. Arking, Hans J. Grabe, Ilaria Gandin, Mario Sims, Carl D. Langefeld, Yii-Der Ida Chen, Meian He, W. James Gauderman, Bruce M. Psaty, Kent D. Taylor, Tõnu Esko, Olli T. Raitakari, Saima Afaq, Maik Pietzner, Federica Laguzzi, Dennis O. Mook-Kanamori, Gregory P. Wilson, Xiuqing Guo, Jaspal S. Kooner, David R. Jacobs, Yajuan Wang, Raha Pazoki, Wei Zhao, Andres Metspalu, Mary F. Feitosa, Jost B. Jonas, Tin Aung, Tamara B. Harris, Karin Leander, Nancy L. Pedersen, Charles B. Eaton, Sharon L.R. Kardia, Bernardo L. Horta, Candace M. Kammerer, Terrence Forrester, Alexander P. Reiner, Paul W. Franks, Tanika N. Kelly, Igor Rudan, Ruben N. Eppinga, Melissa A. Richard, Colin A. McKenzie, Christine Williams, Christian Gieger, Joseph H. Lee, Brigitte Kühnel, Christopher P. Nelson, Wen Bin Wei, Antonietta Robino, Anne U. Jackson, E. Shyong Tai, Alisa K. Manning, Lisa R. Yanek, Paul Elliott, Lynne E. Wagenknecht, Ananda R. Wickremasinghe, José Eduardo Krieger, Peter S. Sever, Nora Franceschini, André G. Uitterlinden, Paul M. Ridker, Yun J. Sung, Karen L. Mohlke, Thomas H. Mosley, Nilesh J. Samani, Patricia B. Munroe, Jennifer A. Smith, John M. C. Connell, H. Janaka de Silva, Nicholas Y. Q. Tan, Jian'an Luan, Jennifer E. Huffman, Anna F. Dominiczak, Carsten Oliver Schmidt, Christie M. Ballantyne, Peter J. van der Most, Anuj Goel, Jerome I. Rotter, Amy R. Bentley, Lihua Wang, Rajkumar Dorajoo, Lenore J. Launer, Yechiel Friedlander, Mickaël Canouil, Traci M. Bartz, Thomas Meitinger, Robert A. Scott, Mike A. Nalls, Xueling Sim, Sarah E. Harris, Michael A. Province, Chew-Kiat Heng, Zhe Wang, Norihiro Kato, Evangelos Evangelou, Philippe Froguel, Virginia Fisher, Leslie J. Raffel, Renée de Mutsert, Michele K. Evans, Brenda W.J.H. Penninx, Yih Chung Tham, Tin Louie, Nana Matoba, Lawrence F. Bielak, Andrea R. V. R. Horimoto, Jingmin Liu, Lynda M. Rose, Nona Sotoodehnia, Daniel I. Chasman, Jasmin Divers, John M. Starr, Patricia A. Peyser, Ervin R. Fox, Barry I. Freedman, Megan L. Grove, Jeffrey R. O'Connell, Rob M. van Dam, Michiaki Kubo, Jessica D. Faul, Fang-Chi Hsu, Yize Li, Ilja M. Nolte, Caroline Hayward, Paolo Gasparini, Chiea Chuen Khor, Changwei Li, Xu Chen, James E. Hixson, Patrik K. E. Magnusson, John C. Chambers, Yoichiro Kamatani, Sami Heikkinen, M. Abdullah Said, Martin Farrall, Alena Stančáková, Konstantin Strauch, Albert V. Smith, Yik Ying Teo, Bamidele O. Tayo, Tamar Sofer, Caizheng Yu, Maris Alver, Oscar H. Franco, Yongmei Liu, Ioanna Ntalla, Myriam Fornage, W. Craig Johnson, Dina Vojinovic, William R. Scott, David R. Weir, Yanick Hagemeijer, Raymond Noordam, Mathilde Boissel, Cornelia M. van Duijn, Karen Schwander, Jingzhong Ding, James Scott, Diana van Heemst, Ching-Yu Cheng, Woon-Puay Koh, Jianjun Liu, Kenneth J. Mukamal, Blair H. Smith, Claude Bouchard, Claudia Langenberg, Jingjing Liang, Wei Zheng, Najaf Amin, Ulf de Faire, Bing Yu, Archie Campbell, Kari E. North, Salman M. Tajuddin, Fernando Pires Hartwig, Steve B. Kritchevsky, Tibor V. Varga, Pirjo Komulainen, Hugh Watkins, Johanna Kuusisto, Kurt Lohman, Rozenn N. Lemaitre, Ani Manichaikul, Dabeeru C. Rao, Kenneth Rice, Weihua Zhang, Franco Giulianini, Reedik Mägi, Solomon K. Musani, Wanqing Wen, Nicholas J. Wareham, Charles N. Rotimi, Marzyeh Amini, Rainer Rauramaa, Nicholette D. Palmer, Annette Peters, Neil R Poulter, Benjamin Lehne, Matthias Nauck, Harold Snieder, Donald W. Bowden, M. Arfan Ikram, Lisa de las Fuentes, C. Charles Gu, Xiaofeng Zhu, Markku Laakso, Tuomas O. Kilpeläinen, Ching-Ti Liu, Michael Boehnke, Jin-Fang Chai, Lili Milani, L. Adrienne Cupples, Yuri Milaneschi, Kiang Liu, Stephen Sidney, Alan B. Zonderman, Thomas W. Winkler, Ian J. Deary, Fumihiko Takeuchi, Mary K. Wojczynski, Pim van der Harst, Leo-Pekka Lyytikäinen, Jennifer G. Robinson, Chuan Gao, Xiao-Ou Shu, Diane M. Becker, Mohsen Ghanbari, Alanna C. Morrison, Jian-Min Yuan, Tomohiro Katsuya, Ivana Kolcic, Melanie Waldenberger, Terho Lehtimäki, Heikki A. Koistinen, Erin B. Ware, Ya Xing Wang, Marie Loh, Treva Rice, Qing Duan, Aldi T. Kraja, Sabanayagam Charumathi, Tien Yin Wong, Paul S. de Vries, Yujie Wang, Muhammad Riaz, Home Office, Action on Hearing Loss, Imperial College Healthcare NHS Trust- BRC Funding, British Heart Foundation, Medical Research Council (MRC), Wellcome Trust, National Institute for Health Research, Luan, Jian'an [0000-0003-3137-6337], Pietzner, Maik [0000-0003-3437-9963], Zhao, Jing Hua [0000-0003-4930-3582], Langenberg, Claudia [0000-0002-5017-7344], Wareham, Nicholas [0000-0003-1422-2993], Apollo - University of Cambridge Repository, APH - Mental Health, Psychiatry, APH - Digital Health, The University of Texas Health Science Center at Houston (UTHealth), National Institutes of Health [Bethesda] (NIH), Washington University in Saint Louis (WUSTL), University of Regensburg, Queen Mary University of London (QMUL), Washington University School of Medicine in St. Louis, Harbor UCLA Medical Center [Torrance, Ca.], University of North Carolina at Chapel Hill (UNC), Singapore National Eye Centre and Duke-NUS [Singapore], Singapore Eye Research Institute [Singapore] (SERI), National University of Singapore (NUS), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Département de Biologie Computationnelle - Department of Computational Biology, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), University Management, Department of Medicine, University of Helsinki, Endokrinologian yksikkö, HUS Abdominal Center, Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study), Epidemiology, Neurology, Radiology & Nuclear Medicine, Internal Medicine, Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), de Vries, Paul S, Brown, Michael R, Bentley, Amy R, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Schwander, Karen, Kraja, Aldi T, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Huffman, Jennifer E, Musani, Solomon K, Li, Changwei, Feitosa, Mary F, Richard, Melissa A, Noordam, Raymond, Aschard, Hugue, Bartz, Traci M, Bielak, Lawrence F, Deng, Xuan, Dorajoo, Rajkumar, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Evangelou, Evangelo, Graff, Mariaelisa, Alver, Mari, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gandin, Ilaria, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, Hartwig, Fernando P, He, Meian, Horimoto, Andrea R V R, Hsu, Fang-Chi, Jackson, Anne U, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Lee, Joseph H, Luan, Jian'An, Lyytikäinen, Leo-Pekka, Matoba, Nana, Nolte, Ilja M, Pietzner, Maik, Riaz, Muhammad, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stancáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Wang, Yajuan, Ware, Erin B, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Ballantyne, Christie, Boerwinkle, Eric, Broeckel, Ulrich, Campbell, Archie, Canouil, Mickaël, Charumathi, Sabanayagam, Chen, Yii-Der Ida, Connell, John M, de Faire, Ulf, de Las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Ding, Jingzhong, Dominiczak, Anna F, Duan, Qing, Eaton, Charles B, Eppinga, Ruben N, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Franco, Oscar H, Friedlander, Yechiel, Ghanbari, Mohsen, Giulianini, Franco, Grabe, Hans J, Grove, Megan L, Gu, C Charle, Harris, Tamara B, Heikkinen, Sami, Heng, Chew-Kiat, Hirata, Makoto, Hixson, James E, Howard, Barbara V, Ikram, M Arfan, Jacobs, David R, Johnson, Craig, Jonas, Jost Bruno, Kammerer, Candace M, Katsuya, Tomohiro, Khor, Chiea Chuen, Kilpeläinen, Tuomas O, Koh, Woon-Puay, Koistinen, Heikki A, Kolcic, Ivana, Kooperberg, Charle, Krieger, Jose E, Kritchevsky, Steve B, Kubo, Michiaki, Kuusisto, Johanna, Lakka, Timo A, Langefeld, Carl D, Langenberg, Claudia, Launer, Lenore J, Lehne, Benjamin, Lemaitre, Rozenn N, Li, Yize, Liang, Jingjing, Liu, Jianjun, Liu, Kiang, Loh, Marie, Louie, Tin, Mägi, Reedik, Manichaikul, Ani W, Mckenzie, Colin A, Meitinger, Thoma, Metspalu, Andre, Milaneschi, Yuri, Milani, Lili, Mohlke, Karen L, Mosley, Thomas H, Mukamal, Kenneth J, Nalls, Mike A, Nauck, Matthia, Nelson, Christopher P, Sotoodehnia, Nona, O'Connell, Jeff R, Palmer, Nicholette D, Pazoki, Raha, Pedersen, Nancy L, Peters, Annette, Peyser, Patricia A, Polasek, Ozren, Poulter, Neil, Raffel, Leslie J, Raitakari, Olli T, Reiner, Alex P, Rice, Treva K, Rich, Stephen S, Robino, Antonietta, Robinson, Jennifer G, Rose, Lynda M, Rudan, Igor, Schmidt, Carsten O, Schreiner, Pamela J, Scott, William R, Sever, Peter, Shi, Yuan, Sidney, Stephen, Sims, Mario, Smith, Blair H, Smith, Jennifer A, Snieder, Harold, Starr, John M, Strauch, Konstantin, Tan, Nichola, Taylor, Kent D, Teo, Yik Ying, Tham, Yih Chung, Uitterlinden, André G, van Heemst, Diana, Vuckovic, Dragana, Waldenberger, Melanie, Wang, Lihua, Wang, Yujie, Wang, Zhe, Wei, Wen Bin, Williams, Christine, Wilson, Gregory, Wojczynski, Mary K, Yao, Jie, Yu, Bing, Yu, Caizheng, Yuan, Jian-Min, Zhao, Wei, Zonderman, Alan B, Becker, Diane M, Boehnke, Michael, Bowden, Donald W, Chambers, John C, Deary, Ian J, Esko, Tõnu, Farrall, Martin, Franks, Paul W, Freedman, Barry I, Froguel, Philippe, Gasparini, Paolo, Gieger, Christian, Horta, Bernardo L, Kamatani, Yoichiro, Kato, Norihiro, Kooner, Jaspal S, Laakso, Markku, Leander, Karin, Lehtimäki, Terho, Magnusson, Patrik K E, Penninx, Brenda, Pereira, Alexandre C, Rauramaa, Rainer, Samani, Nilesh J, Scott, Jame, Shu, Xiao-Ou, van der Harst, Pim, Wagenknecht, Lynne E, Wang, Ya Xing, Wareham, Nicholas J, Watkins, Hugh, Weir, David R, Wickremasinghe, Ananda R, Zheng, Wei, Elliott, Paul, North, Kari E, Bouchard, Claude, Evans, Michele K, Gudnason, Vilmundur, Liu, Ching-Ti, Liu, Yongmei, Psaty, Bruce M, Ridker, Paul M, van Dam, Rob M, Kardia, Sharon L R, Zhu, Xiaofeng, Rotimi, Charles N, Mook-Kanamori, Dennis O, Fornage, Myriam, Kelly, Tanika N, Fox, Ervin R, Hayward, Caroline, van Duijn, Cornelia M, Tai, E Shyong, Wong, Tien Yin, Liu, Jingmin, Rotter, Jerome I, Gauderman, W Jame, Province, Michael A, Munroe, Patricia B, Rice, Kenneth, Chasman, Daniel I, Cupples, L Adrienne, Rao, Dabeeru C, Morrison, Alanna C, and Academic Medical Center

Subjects
Male, Epidemiology, Genome-wide association study, Cardiovascular, MESH: Genotype, Substance Misuse, Alcohol Use and Health, 0302 clinical medicine, Aetiology, triglycerides, 11 Medical and Health Sciences, Public, Environmental & Occupational Health, [STAT.AP]Statistics [stat]/Applications [stat.AP], MESH: Middle Aged, MESH: Life Style, genome wide, gene-alcohol interaction, lipid levels, Lipid Measurement, Lifelines Cohort, DENSITY-LIPOPROTEIN CHOLESTEROL, MESH: Young Adult, 030220 oncology & carcinogenesis, Kexin, LOW-FREQUENCY, MESH: Cholesterol, HDL, MESH: Cholesterol, LDL, MESH: Triglycerides, HDL, Alcohol Drinking, Genotype, alcohol consumption, MESH: Phenotype, LDL, lipids, 03 medical and health sciences, Genetics, Humans, Life Style, METAANALYSIS, 01 Mathematical Sciences, Aged, MESH: Adolescent, HDL CHOLESTEROL, MESH: Humans, Science & Technology, Cholesterol, APOBEC1, Lifelines Cohort, Groningen, The Netherlands (Lifelines Cohort Study), MESH: Vascular Endothelial Growth Factor B, Racial Groups, cholesterol, The Netherlands, MESH: Adult, CONSUMPTION, chemistry, MESH: Genome-Wide Association Study, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Lipid profile, MESH: Female, MESH: Alcohol Drinking, MESH: Continental Population Groups, Vascular Endothelial Growth Factor B, Original Contributions, Blood lipids, VEGF-B, Medical and Health Sciences, Mathematical Sciences, BLOOD-LIPIDS, chemistry.chemical_compound, 2.1 Biological and endogenous factors, 030212 general & internal medicine, MESH: Aged, medicine.diagnostic_test, PLASMA, gene-environment interactions, Middle Aged, 3142 Public health care science, environmental and occupational health, Alcoholism, Phenotype, Female, gene-lifestyle interactions, genome-wide association studies, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Life Sciences & Biomedicine, Groningen, Adult, Adolescent, Biology, Young Adult, medicine, CORONARY-HEART-DISEASE, Human Genome, Cholesterol, HDL, Lipid metabolism, Cholesterol, LDL, InterAct Consortium, MESH: Lipids, MESH: Male, INDIVIDUALS, [SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human genetics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Genome-Wide Association Study
Abstract

© The Author(s) 2019. A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-Alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2- degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10?6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10?8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models. National Heart, Lung, and Blood Institute; American Heart Association Grant

Published
2019

49. GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment.

Author

Rietveld, Cornelius A., Medland, Sarah E., Derringer, Jaime, Yang, Jian, Esko, Tönu, Martin, Nicolas W., Westra, Harm-Jan, Shakhbazov, Konstantin, Abdellaoui, Abdel, Agrawal, Arpana, Albrecht, Eva, Alizadeh, Behrooz Z., Amin, Najaf, Barnard, John, Baumeister, Sebastian E., Benke, Kelly S., Bielak, Lawrence F., Boatman, Jeffrey A., Boyle, Patricia A., and Davies, Gail

Subjects
*EDUCATION & heredity, *EDUCATIONAL attainment, *COGNITIVE ability, *HUMAN genome, *HUMAN genetic variation, *SINGLE nucleotide polymorphisms
Abstract

A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rsll584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R² ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics. [ABSTRACT FROM AUTHOR]

Published
2013
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results