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39 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. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. Association of mitochondrial DNA copy number with cardiometabolic diseases

Author

Liu, Xue, Longchamps, Ryan J, Wiggins, Kerri L, Raffield, Laura M, Bielak, Lawrence F, Zhao, Wei, Pitsillides, Achilleas, Blackwell, Thomas W, Yao, Jie, Guo, Xiuqing, Kurniansyah, Nuzulul, Thyagarajan, Bharat, Pankratz, Nathan, Rich, Stephen S, Taylor, Kent D, Peyser, Patricia A, Heckbert, Susan R, Seshadri, Sudha, Cupples, L Adrienne, Boerwinkle, Eric, Grove, Megan L, Larson, Nicholas B, Smith, Jennifer A, Vasan, Ramachandran S, Sofer, Tamar, Fitzpatrick, Annette L, Fornage, Myriam, Ding, Jun, Correa, Adolfo, Abecasis, Goncalo, Psaty, Bruce M, Wilson, James G, Levy, Daniel, Rotter, Jerome I, Bis, Joshua C, Consortium, TOPMed mtDNA Working Group in NHLBI Trans-Omics for Precision Medicine, Satizabal, Claudia L, Arking, Dan E, and Liu, Chunyu

Subjects
Biological Sciences, Genetics, Aging, Metabolic and endocrine, Generic health relevance, Good Health and Well Being, TOPMed mtDNA Working Group in NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
Abstract

Mitochondrial DNA (mtDNA) is present in multiple copies in human cells. We evaluated cross-sectional associations of whole blood mtDNA copy number (CN) with several cardiometabolic disease traits in 408,361 participants of multiple ancestries in TOPMed and UK Biobank. Age showed a threshold association with mtDNA CN: among younger participants (

Published
2021

13. 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

14. Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci.

Author

de Las Fuentes, Lisa, Sung, Yun Ju, Noordam, Raymond, Winkler, Thomas, Feitosa, Mary F, Schwander, Karen, Bentley, Amy R, Brown, Michael R, Guo, Xiuqing, Manning, Alisa, Chasman, Daniel I, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Campbell, Archie, Cheng, Ching-Yu, Dorajoo, Rajkumar, Hartwig, Fernando P, Horimoto, ARVR, Li, Changwei, Li-Gao, Ruifang, Liu, Yongmei, Marten, Jonathan, Musani, Solomon K, Ntalla, Ioanna, Rankinen, Tuomo, Richard, Melissa, Sim, Xueling, Smith, Albert V, Tajuddin, Salman M, Tayo, Bamidele O, Vojinovic, Dina, Warren, Helen R, Xuan, Deng, Alver, Maris, Boissel, Mathilde, Chai, Jin-Fang, Chen, Xu, Christensen, Kaare, Divers, Jasmin, Evangelou, Evangelos, Gao, Chuan, Girotto, Giorgia, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Kühnel, Brigitte, Laguzzi, Federica, Li, Xiaoyin, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Poveda, Alaitz, Rauramaa, Rainer, Riaz, Muhammad, Rueedi, Rico, Shu, Xiao-Ou, Snieder, Harold, Sofer, Tamar, Takeuchi, Fumihiko, Verweij, Niek, Ware, Erin B, Weiss, Stefan, Yanek, Lisa R, Amin, Najaf, Arking, Dan E, Arnett, Donna K, Bergmann, Sven, Boerwinkle, Eric, Brody, Jennifer A, Broeckel, Ulrich, Brumat, Marco, Burke, Gregory, Cabrera, Claudia P, Canouil, Mickaël, Chee, Miao Li, Chen, Yii-Der Ida, Cocca, Massimiliano, Connell, John, de Silva, H Janaka, de Vries, Paul S, Eiriksdottir, Gudny, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Fox, Ervin F, Friedlander, Yechiel, Gao, He, Gigante, Bruna, Giulianini, Franco, Gu, Chi Charles, Gu, Dongfeng, Harris, Tamara B, He, Jiang, Heikkinen, Sami, Heng, Chew-Kiat, Hunt, Steven, Ikram, M Arfan, Irvin, Marguerite R, Kähönen, Mika, and Kavousi, Maryam

Subjects
Lifelines Cohort Study, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P

Published
2021

15. 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

16. 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

17. 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, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Neale, Benjamin M, Sunyaev, Shamil R, Abecasis, Gonçalo R, Rotter, Jerome I, Willer, Cristen J, Peloso, Gina M, Natarajan, Pradeep, and Lin, Xihong

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Humans, Genetic Predisposition to Disease, Phenotype, Genome, Models, Genetic, Computer Simulation, Cholesterol, LDL, Genetic Variation, Genome-Wide Association Study, Molecular Sequence Annotation, Whole Genome Sequencing, Models, Genetic, Cholesterol, LDL, Biological Sciences, Medical and Health Sciences, Developmental 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. Publisher Correction: 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
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, Nutrition, Obesity, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

In the version of this article originally published, one of the two authors with the name Wei Zhao was omitted from the author list and the affiliations for both authors were assigned to the single Wei Zhao in the author list. In addition, the ORCID for Wei Zhao (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA) was incorrectly assigned to author Wei Zhou. The errors have been corrected in the HTML and PDF versions of the article.

Published
2018

25. 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, ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, 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, and Lin, Keng-Hung

Subjects
ExomeBP Consortium, MAGIC Consortium, GIANT Consortium, Humans, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Chromosome Mapping, Alleles, European Continental Ancestry Group, Female, Male, Genetic Variation, Genome-Wide Association Study, Whole Exome Sequencing, Diabetes Mellitus, Type 2, Developmental Biology, Biological Sciences, Medical and Health Sciences
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

26. 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

27. 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

28. Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Author

Rusu, Victor, Hoch, Eitan, Mercader, Josep M, Tenen, Danielle E, Gymrek, Melissa, Hartigan, Christina R, DeRan, Michael, von Grotthuss, Marcin, Fontanillas, Pierre, Spooner, Alexandra, Guzman, Gaelen, Deik, Amy A, Pierce, Kerry A, Dennis, Courtney, Clish, Clary B, Carr, Steven A, Wagner, Bridget K, Schenone, Monica, Ng, Maggie CY, Chen, Brian H, Consortium, MEDIA, Shriner, Daniel, 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, Consortium, the FIND, 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, Consortium, the eMERGE, Consortium, the DIAGRAM, Grundberg, Elin, Consortium, the MuTHER, Rich, Stephen S, Hayes, M Geoffrey, Shu, Xiao-Ou, Loos, Ruth JF, Borecki, Ingrid B, Peyser, Patricia A, Cummings, Steven R, and Psaty, Bruce M

Subjects
Biological Sciences, Genetics, Clinical Research, Diabetes, Digestive Diseases, Liver Disease, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Basigin, Cell Membrane, Chromosomes, Human, Pair 17, Diabetes Mellitus, Type 2, Gene Knockdown Techniques, Haplotypes, Hepatocytes, Heterozygote, Histone Code, Humans, Liver, Models, Molecular, Monocarboxylic Acid Transporters, MEDIA Consortium, SIGMA T2D Consortium, MCT11, SLC16A11, disease mechanism, fatty acid metabolism, genetics, lipid metabolism, monocarboxylates, precision medicine, solute carrier, type 2 diabetes, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. VIDEO ABSTRACT.

Published
2017

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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, 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, Grundberg, Elin, 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, and Bowden, Donald W

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

36. 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

37. 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

38. 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, 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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
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39. 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
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