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1. 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 JM, 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, and Sabater-Lleal, Maria

Subjects
Human Genome, Biotechnology, Genetics, 2.1 Biological and endogenous factors, Aetiology, Chromatin, Genome-Wide Association Study, Genomics, Humans, Lipids, Polymorphism, Single Nucleotide, Million Veterans Program, Global Lipids Genetics Consortium, complex traits, fine-mapping, functional genomics, lipid biology, post-GWAS, regulatory mechanism, variant prioritization, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
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.

Published
2022

2. The power of genetic diversity in genome-wide association studies of lipids

Author

Graham, Sarah E, Clarke, Shoa L, Wu, Kuan-Han H, Kanoni, Stavroula, Zajac, Greg JM, Ramdas, Shweta, Surakka, Ida, Ntalla, Ioanna, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Hwang, Mi Yeong, Han, Sohee, Narita, Akira, Choudhury, Ananyo, Bentley, Amy R, Ekoru, Kenneth, Verma, Anurag, Trivedi, Bhavi, Martin, Hilary C, Hunt, Karen A, Hui, Qin, Klarin, Derek, Zhu, Xiang, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Ruotsalainen, Sanni E, Havulinna, Aki S, Veturi, Yogasudha, Feng, QiPing, Rosenthal, Elisabeth A, Lingren, Todd, Pacheco, Jennifer Allen, Pendergrass, Sarah A, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Hindy, George, Rasheed, Asif, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, 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, Willemsen, Gonneke, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Yao, Jie, Manichaikul, Ani, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Sidore, Carlo, Fiorillo, Edoardo, McDaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, and Sattar, Naveed

Subjects
Heart Disease, Prevention, Human Genome, Genetics, Cardiovascular, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Cardiovascular Diseases, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Population Groups, VA Million Veteran Program, Global Lipids Genetics Consortium*, General Science & Technology
Abstract

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

Published
2021

3. Multi-ancestry genome-wide gene–sleep interactions identify novel loci for blood pressure

Author

Wang, Heming, Noordam, Raymond, Cade, Brian E, Schwander, Karen, Winkler, Thomas W, Lee, Jiwon, Sung, Yun Ju, Bentley, Amy R, Manning, Alisa K, Aschard, Hugues, Kilpeläinen, Tuomas O, Ilkov, Marjan, Brown, Michael R, Horimoto, Andrea R, Richard, Melissa, Bartz, Traci M, Vojinovic, Dina, Lim, Elise, Nierenberg, Jovia L, Liu, Yongmei, Chitrala, Kumaraswamynaidu, Rankinen, Tuomo, Musani, Solomon K, Franceschini, Nora, Rauramaa, Rainer, Alver, Maris, Zee, Phyllis C, Harris, Sarah E, van der Most, Peter J, Nolte, Ilja M, Munroe, Patricia B, Palmer, Nicholette D, Kühnel, Brigitte, Weiss, Stefan, Wen, Wanqing, Hall, Kelly A, Lyytikäinen, Leo-Pekka, O’Connell, Jeff, Eiriksdottir, Gudny, Launer, Lenore J, de Vries, Paul S, Arking, Dan E, Chen, Han, Boerwinkle, Eric, Krieger, Jose E, Schreiner, Pamela J, Sidney, Stephen, Shikany, James M, Rice, Kenneth, Chen, Yii-Der Ida, Gharib, Sina A, Bis, Joshua C, Luik, Annemarie I, Ikram, M Arfan, Uitterlinden, André G, Amin, Najaf, Xu, Hanfei, Levy, Daniel, He, Jiang, Lohman, Kurt K, Zonderman, Alan B, Rice, Treva K, Sims, Mario, Wilson, Gregory, Sofer, Tamar, Rich, Stephen S, Palmas, Walter, Yao, Jie, Guo, Xiuqing, Rotter, Jerome I, Biermasz, Nienke R, Mook-Kanamori, Dennis O, Martin, Lisa W, Barac, Ana, Wallace, Robert B, Gottlieb, Daniel J, Komulainen, Pirjo, Heikkinen, Sami, Mägi, Reedik, Milani, Lili, Metspalu, Andres, Starr, John M, Milaneschi, Yuri, Waken, RJ, Gao, Chuan, Waldenberger, Melanie, Peters, Annette, Strauch, Konstantin, Meitinger, Thomas, Roenneberg, Till, Völker, Uwe, Dörr, Marcus, Shu, Xiao-Ou, Mukherjee, Sutapa, Hillman, David R, Kähönen, Mika, Wagenknecht, Lynne E, Gieger, Christian, Grabe, Hans J, and Zheng, Wei

Subjects
Human Genome, Sleep Research, Clinical Research, Biotechnology, Hypertension, Genetics, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Blood Pressure, Genetic Loci, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Sleep, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups in two stages using 2 degree of freedom (df) joint test followed by 1df test of interaction effects. Primary multi-ancestry analysis in 62,969 individuals in stage 1 identified three novel gene by sleep interactions that were replicated in an additional 59,296 individuals in stage 2 (stage 1 + 2 Pjoint

Published
2021

4. 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

5. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Author

Gorski, Mathias, Jung, Bettina, Li, Yong, Matias-Garcia, Pamela R, Wuttke, Matthias, Coassin, Stefan, Thio, Chris HL, Kleber, Marcus E, Winkler, Thomas W, Wanner, Veronika, Chai, Jin-Fang, Chu, Audrey Y, Cocca, Massimiliano, Feitosa, Mary F, Ghasemi, Sahar, Hoppmann, Anselm, Horn, Katrin, Li, Man, Nutile, Teresa, Scholz, Markus, Sieber, Karsten B, Teumer, Alexander, Tin, Adrienne, Wang, Judy, Tayo, Bamidele O, Ahluwalia, Tarunveer S, Almgren, Peter, Bakker, Stephan JL, Banas, Bernhard, Bansal, Nisha, Biggs, Mary L, Boerwinkle, Eric, Bottinger, Erwin P, Brenner, Hermann, Carroll, Robert J, Chalmers, John, Chee, Miao-Li, Chee, Miao-Ling, Cheng, Ching-Yu, Coresh, Josef, de Borst, Martin H, Degenhardt, Frauke, Eckardt, Kai-Uwe, Endlich, Karlhans, Franke, Andre, Freitag-Wolf, Sandra, Gampawar, Piyush, Gansevoort, Ron T, Ghanbari, Mohsen, Gieger, Christian, Hamet, Pavel, Ho, Kevin, Hofer, Edith, Holleczek, Bernd, Foo, Valencia Hui Xian, Hutri-Kähönen, Nina, Hwang, Shih-Jen, Ikram, M Arfan, Josyula, Navya Shilpa, Kähönen, Mika, Khor, Chiea-Chuen, Koenig, Wolfgang, Kramer, Holly, Krämer, Bernhard K, Kühnel, Brigitte, Lange, Leslie A, Lehtimäki, Terho, Lieb, Wolfgang, Loos, Ruth JF, Lukas, Mary Ann, Lyytikäinen, Leo-Pekka, Meisinger, Christa, Meitinger, Thomas, Melander, Olle, Milaneschi, Yuri, Mishra, Pashupati P, Mononen, Nina, Mychaleckyj, Josyf C, Nadkarni, Girish N, Nauck, Matthias, Nikus, Kjell, Ning, Boting, Nolte, Ilja M, O’Donoghue, Michelle L, Orho-Melander, Marju, Pendergrass, Sarah A, Penninx, Brenda WJH, Preuss, Michael H, Psaty, Bruce M, Raffield, Laura M, Raitakari, Olli T, Rettig, Rainer, Rheinberger, Myriam, Rice, Kenneth M, Rosenkranz, Alexander R, Rossing, Peter, Rotter, Jerome I, Sabanayagam, Charumathi, Schmidt, Helena, and Schmidt, Reinhold

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Human Genome, Kidney Disease, Rare Diseases, Genetics, Aetiology, 2.1 Biological and endogenous factors, Renal and urogenital, AMP-Activated Protein Kinases, Creatinine, Genome-Wide Association Study, Glomerular Filtration Rate, Humans, Kidney, Protein Disulfide-Isomerases, United Kingdom, acute kidney injury, end-stage kidney disease, genome-wide association study, rapid eGFRcrea decline, Lifelines Cohort Study, Regeneron Genetics Center, Urology & Nephrology, Clinical sciences
Abstract

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Published
2021

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

Author

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

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

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

Published
2021

7. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Author

Noordam, Raymond, Bos, Maxime M, Wang, Heming, Winkler, Thomas W, Bentley, Amy R, Kilpeläinen, Tuomas O, de Vries, Paul S, Sung, Yun Ju, Schwander, Karen, Cade, Brian E, Manning, Alisa, Aschard, Hugues, Brown, Michael R, Chen, Han, Franceschini, Nora, Musani, Solomon K, Richard, Melissa, Vojinovic, Dina, Aslibekyan, Stella, Bartz, Traci M, de Las Fuentes, Lisa, Feitosa, Mary, Horimoto, Andrea R, Ilkov, Marjan, Kho, Minjung, Kraja, Aldi, Li, Changwei, Lim, Elise, Liu, Yongmei, Mook-Kanamori, Dennis O, Rankinen, Tuomo, Tajuddin, Salman M, van der Spek, Ashley, Wang, Zhe, Marten, Jonathan, Laville, Vincent, Alver, Maris, Evangelou, Evangelos, Graff, Maria E, He, Meian, Kühnel, Brigitte, Lyytikäinen, Leo-Pekka, Marques-Vidal, Pedro, Nolte, Ilja M, Palmer, Nicholette D, Rauramaa, Rainer, Shu, Xiao-Ou, Snieder, Harold, Weiss, Stefan, Wen, Wanqing, Yanek, Lisa R, Adolfo, Correa, Ballantyne, Christie, Bielak, Larry, Biermasz, Nienke R, Boerwinkle, Eric, Dimou, Niki, Eiriksdottir, Gudny, Gao, Chuan, Gharib, Sina A, Gottlieb, Daniel J, Haba-Rubio, José, Harris, Tamara B, Heikkinen, Sami, Heinzer, Raphaël, Hixson, James E, Homuth, Georg, Ikram, M Arfan, Komulainen, Pirjo, Krieger, Jose E, Lee, Jiwon, Liu, Jingmin, Lohman, Kurt K, Luik, Annemarie I, Mägi, Reedik, Martin, Lisa W, Meitinger, Thomas, Metspalu, Andres, Milaneschi, Yuri, Nalls, Mike A, O'Connell, Jeff, Peters, Annette, Peyser, Patricia, Raitakari, Olli T, Reiner, Alex P, Rensen, Patrick CN, Rice, Treva K, Rich, Stephen S, Roenneberg, Till, Rotter, Jerome I, Schreiner, Pamela J, Shikany, James, Sidney, Stephen S, Sims, Mario, Sitlani, Colleen M, Sofer, Tamar, Strauch, Konstantin, Swertz, Morris A, Taylor, Kent D, and Uitterlinden, André G

Subjects
Humans, Lipids, Chromosome Mapping, Sleep, Phylogeny, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Young Adult, Genetic Loci, Polymorphism, Single Nucleotide, and over
Abstract

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

Published
2019

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

Author

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

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

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

Published
2019

9. 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

10. 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
Lifelines Cohort Study, Humans, Cholesterol, Lipids, Triglycerides, Calcium-Binding Proteins, Muscle Proteins, Microtubule-Associated Proteins, Membrane Proteins, Nerve Tissue Proteins, Transcription Factors, Exercise, Genotype, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, African Continental Ancestry Group, Asian Continental Ancestry Group, European Continental Ancestry Group, Hispanic Americans, Brazil, Female, Male, Lipid Metabolism, Cholesterol, LDL, Cholesterol, HDL, Genome-Wide Association Study, Young Adult, Genetic Loci, LIM-Homeodomain Proteins, and over, HDL, LDL
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

11. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.

Author

Franceschini, Nora, Giambartolomei, Claudia, de Vries, Paul S, Finan, Chris, Bis, Joshua C, Huntley, Rachael P, Lovering, Ruth C, Tajuddin, Salman M, Winkler, Thomas W, Graff, Misa, Kavousi, Maryam, Dale, Caroline, Smith, Albert V, Hofer, Edith, van Leeuwen, Elisabeth M, Nolte, Ilja M, Lu, Lingyi, Scholz, Markus, Sargurupremraj, Muralidharan, Pitkänen, Niina, Franzén, Oscar, Joshi, Peter K, Noordam, Raymond, Marioni, Riccardo E, Hwang, Shih-Jen, Musani, Solomon K, Schminke, Ulf, Palmas, Walter, Isaacs, Aaron, Correa, Adolfo, Zonderman, Alan B, Hofman, Albert, Teumer, Alexander, Cox, Amanda J, Uitterlinden, André G, Wong, Andrew, Smit, Andries J, Newman, Anne B, Britton, Annie, Ruusalepp, Arno, Sennblad, Bengt, Hedblad, Bo, Pasaniuc, Bogdan, Penninx, Brenda W, Langefeld, Carl D, Wassel, Christina L, Tzourio, Christophe, Fava, Cristiano, Baldassarre, Damiano, O'Leary, Daniel H, Teupser, Daniel, Kuh, Diana, Tremoli, Elena, Mannarino, Elmo, Grossi, Enzo, Boerwinkle, Eric, Schadt, Eric E, Ingelsson, Erik, Veglia, Fabrizio, Rivadeneira, Fernando, Beutner, Frank, Chauhan, Ganesh, Heiss, Gerardo, Snieder, Harold, Campbell, Harry, Völzke, Henry, Markus, Hugh S, Deary, Ian J, Jukema, J Wouter, de Graaf, Jacqueline, Price, Jacqueline, Pott, Janne, Hopewell, Jemma C, Liang, Jingjing, Thiery, Joachim, Engmann, Jorgen, Gertow, Karl, Rice, Kenneth, Taylor, Kent D, Dhana, Klodian, Kiemeney, Lambertus ALM, Lind, Lars, Raffield, Laura M, Launer, Lenore J, Holdt, Lesca M, Dörr, Marcus, Dichgans, Martin, Traylor, Matthew, Sitzer, Matthias, Kumari, Meena, Kivimaki, Mika, Nalls, Mike A, Melander, Olle, Raitakari, Olli, Franco, Oscar H, Rueda-Ochoa, Oscar L, Roussos, Panos, Whincup, Peter H, Amouyel, Philippe, and Giral, Philippe

Subjects
MEGASTROKE Consortium, Humans, Coronary Disease, Genetic Predisposition to Disease, Amino Acid Oxidoreductases, Protein-Lysine 6-Oxidase, Risk Factors, Lod Score, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Plaque, Atherosclerotic, Carotid Intima-Media Thickness, ADAMTS9 Protein, Plaque, Atherosclerotic, Polymorphism, Single Nucleotide
Abstract

Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.

Published
2018

12. 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

13. 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
InterAct Consortium, Humans, Hypertension, Genetic Predisposition to Disease, Cohort Studies, Pedigree, Alcohol Drinking, Blood Pressure, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Continental Population Groups, Female, Male, Genome-Wide Association Study, Young Adult, Gene-Environment Interaction, 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

14. 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

15. 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
CHARGE Consortium, EPIC-InterAct Consortium, PAGE Consortium, Humans, Obesity, Genetic Predisposition to Disease, Body Mass Index, Waist-Hip Ratio, Exercise, Genotype, Female, Male, Adiposity, Waist Circumference, Genome-Wide Association Study, Epigenomics, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Genetics, 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

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

Author

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

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

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

Published
2017

17. 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

18. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study.

Author

Winkler, Thomas W, Justice, Anne E, Graff, Mariaelisa, Barata, Llilda, Feitosa, Mary F, Chu, Su, Czajkowski, Jacek, Esko, Tõnu, Fall, Tove, Kilpeläinen, Tuomas O, Lu, Yingchang, Mägi, Reedik, Mihailov, Evelin, Pers, Tune H, Rüeger, Sina, Teumer, Alexander, Ehret, Georg B, Ferreira, Teresa, Heard-Costa, Nancy L, Karjalainen, Juha, Lagou, Vasiliki, Mahajan, Anubha, Neinast, Michael D, Prokopenko, Inga, Simino, Jeannette, Teslovich, Tanya M, Jansen, Rick, Westra, Harm-Jan, White, Charles C, Absher, Devin, Ahluwalia, Tarunveer S, Ahmad, Shafqat, Albrecht, Eva, Alves, Alexessander Couto, Bragg-Gresham, Jennifer L, de Craen, Anton JM, Bis, Joshua C, Bonnefond, Amélie, Boucher, Gabrielle, Cadby, Gemma, Cheng, Yu-Ching, Chiang, Charleston WK, Delgado, Graciela, Demirkan, Ayse, Dueker, Nicole, Eklund, Niina, Eiriksdottir, Gudny, Eriksson, Joel, Feenstra, Bjarke, Fischer, Krista, Frau, Francesca, Galesloot, Tessel E, Geller, Frank, Goel, Anuj, Gorski, Mathias, Grammer, Tanja B, Gustafsson, Stefan, Haitjema, Saskia, Hottenga, Jouke-Jan, Huffman, Jennifer E, Jackson, Anne U, Jacobs, Kevin B, Johansson, Åsa, Kaakinen, Marika, Kleber, Marcus E, Lahti, Jari, Mateo Leach, Irene, Lehne, Benjamin, Liu, Youfang, Lo, Ken Sin, Lorentzon, Mattias, Luan, Jian'an, Madden, Pamela AF, Mangino, Massimo, McKnight, Barbara, Medina-Gomez, Carolina, Monda, Keri L, Montasser, May E, Müller, Gabriele, Müller-Nurasyid, Martina, Nolte, Ilja M, Panoutsopoulou, Kalliope, Pascoe, Laura, Paternoster, Lavinia, Rayner, Nigel W, Renström, Frida, Rizzi, Federica, Rose, Lynda M, Ryan, Kathy A, Salo, Perttu, Sanna, Serena, Scharnagl, Hubert, Shi, Jianxin, Smith, Albert Vernon, Southam, Lorraine, Stančáková, Alena, Steinthorsdottir, Valgerdur, Strawbridge, Rona J, Sung, Yun Ju, and Tachmazidou, Ioanna

Subjects
CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Humans, Genetic Predisposition to Disease, Body Mass Index, Body Size, Waist-Hip Ratio, Chromosome Mapping, Age Factors, Sex Characteristics, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Male, Genome-Wide Association Study, Developmental Biology, Genetics
Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR

Published
2015

19. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

Author

Horikoshi, Momoko, Mӓgi, Reedik, van de Bunt, Martijn, Surakka, Ida, Sarin, Antti-Pekka, Mahajan, Anubha, Marullo, Letizia, Thorleifsson, Gudmar, Hӓgg, Sara, Hottenga, Jouke-Jan, Ladenvall, Claes, Ried, Janina S, Winkler, Thomas W, Willems, Sara M, Pervjakova, Natalia, Esko, Tõnu, Beekman, Marian, Nelson, Christopher P, Willenborg, Christina, Wiltshire, Steven, Ferreira, Teresa, Fernandez, Juan, Gaulton, Kyle J, Steinthorsdottir, Valgerdur, Hamsten, Anders, Magnusson, Patrik KE, Willemsen, Gonneke, Milaneschi, Yuri, Robertson, Neil R, Groves, Christopher J, Bennett, Amanda J, Lehtimӓki, Terho, Viikari, Jorma S, Rung, Johan, Lyssenko, Valeriya, Perola, Markus, Heid, Iris M, Herder, Christian, Grallert, Harald, Müller-Nurasyid, Martina, Roden, Michael, Hypponen, Elina, Isaacs, Aaron, van Leeuwen, Elisabeth M, Karssen, Lennart C, Mihailov, Evelin, Houwing-Duistermaat, Jeanine J, de Craen, Anton JM, Deelen, Joris, Havulinna, Aki S, Blades, Matthew, Hengstenberg, Christian, Erdmann, Jeanette, Schunkert, Heribert, Kaprio, Jaakko, Tobin, Martin D, Samani, Nilesh J, Lind, Lars, Salomaa, Veikko, Lindgren, Cecilia M, Slagboom, P Eline, Metspalu, Andres, van Duijn, Cornelia M, Eriksson, Johan G, Peters, Annette, Gieger, Christian, Jula, Antti, Groop, Leif, Raitakari, Olli T, Power, Chris, Penninx, Brenda WJH, de Geus, Eco, Smit, Johannes H, Boomsma, Dorret I, Pedersen, Nancy L, Ingelsson, Erik, Thorsteinsdottir, Unnur, Stefansson, Kari, Ripatti, Samuli, Prokopenko, Inga, McCarthy, Mark I, Morris, Andrew P, and ENGAGE Consortium

Subjects
ENGAGE Consortium, Humans, Obesity, Genetic Predisposition to Disease, Glucose-6-Phosphatase, Protein-Serine-Threonine Kinases, Thrombospondins, Body Mass Index, Chromosome Mapping, Glycemic Index, Gene Frequency, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Germinal Center Kinases, Polymorphism, Single Nucleotide, Genetics, Developmental Biology
Abstract

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Published
2015

20. New genetic loci link adipose and insulin biology to body fat distribution

Author

Shungin, Dmitry, Winkler, Thomas W, Croteau-Chonka, Damien C, Ferreira, Teresa, Locke, Adam E, Mägi, Reedik, Strawbridge, Rona J, Pers, Tune H, Fischer, Krista, Justice, Anne E, Workalemahu, Tsegaselassie, Wu, Joseph MW, Buchkovich, Martin L, Heard-Costa, Nancy L, Roman, Tamara S, Drong, Alexander W, Song, Ci, Gustafsson, Stefan, Day, Felix R, Esko, Tonu, Fall, Tove, Kutalik, Zoltán, Luan, Jian’an, Randall, Joshua C, Scherag, André, Vedantam, Sailaja, Wood, Andrew R, Chen, Jin, Fehrmann, Rudolf, Karjalainen, Juha, Kahali, Bratati, Liu, Ching-Ti, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Ehret, Georg B, Feitosa, Mary F, Goel, Anuj, Jackson, Anne U, Johnson, Toby, Kleber, Marcus E, Kristiansson, Kati, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Stančáková, Alena, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Van Vliet-Ostaptchouk, Jana V, Yengo, Loïc, Zhang, Weihua, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Böhringer, Stefan, Bonnet, Fabrice, Böttcher, Yvonne, Bruinenberg, Marcel, Carba, Delia B, Caspersen, Ida H, Clarke, Robert, Warwick Daw, E, Deelen, Joris, Deelman, Ewa, Delgado, Graciela, Doney, Alex SF, Eklund, Niina, Erdos, Michael R, Estrada, Karol, Eury, Elodie, Friedrich, Nele, Garcia, Melissa E, Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S, Golay, Alain, Grallert, Harald, Grammer, Tanja B, Gräßler, Jürgen, Grewal, Jagvir, Groves, Christopher J, Haller, Toomas, and Hallmans, Goran

Subjects
Genetics, Human Genome, Diabetes, Obesity, Nutrition, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Cardiovascular, Stroke, Adipocytes, Adipogenesis, Adipose Tissue, Age Factors, Body Fat Distribution, Body Mass Index, Epigenesis, Genetic, Europe, Female, Genome, Human, Genome-Wide Association Study, Humans, Insulin, Insulin Resistance, Male, Models, Biological, Neovascularization, Physiologic, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Sex Characteristics, Transcription, Genetic, Waist-Hip Ratio, ADIPOGen Consortium, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GEFOS Consortium, GENIE Consortium, GLGC, ICBP, International Endogene Consortium, LifeLines Cohort Study, MAGIC Investigators, MuTHER Consortium, PAGE Consortium, ReproGen Consortium, General Science & Technology
Abstract

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P

Published
2015

21. Genetic studies of body mass index yield new insights for obesity biology

Author

Locke, Adam E, Kahali, Bratati, Berndt, Sonja I, Justice, Anne E, Pers, Tune H, Day, Felix R, Powell, Corey, Vedantam, Sailaja, Buchkovich, Martin L, Yang, Jian, Croteau-Chonka, Damien C, Esko, Tonu, Fall, Tove, Ferreira, Teresa, Gustafsson, Stefan, Kutalik, Zoltán, Luan, Jian’an, Mägi, Reedik, Randall, Joshua C, Winkler, Thomas W, Wood, Andrew R, Workalemahu, Tsegaselassie, Faul, Jessica D, Smith, Jennifer A, Hua Zhao, Jing, Zhao, Wei, Chen, Jin, Fehrmann, Rudolf, Hedman, Åsa K, Karjalainen, Juha, Schmidt, Ellen M, Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bolton, Jennifer L, Bragg-Gresham, Jennifer L, Buyske, Steven, Demirkan, Ayse, Deng, Guohong, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Goel, Anuj, Gong, Jian, Jackson, Anne U, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Medland, Sarah E, Nalls, Michael A, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J, Prokopenko, Inga, Shungin, Dmitry, Stančáková, Alena, Strawbridge, Rona J, Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Isaacs, Aaron, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M, Attwood, Antony P, Bandinelli, Stefania, Barrett, Amy, Bas, Isabelita N, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blagieva, Roza, Blüher, Matthias, Böhringer, Stefan, Bonnycastle, Lori L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Caspersen, Ida H, Ida Chen, Yii-Der, Clarke, Robert, Warwick Daw, E, de Craen, Anton JM, Delgado, Graciela, and Dimitriou, Maria

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Nutrition, Obesity, Prevention, Aetiology, 2.1 Biological and endogenous factors, Stroke, Cardiovascular, Metabolic and endocrine, Oral and gastrointestinal, Cancer, Adipogenesis, Adiposity, Age Factors, Body Mass Index, Energy Metabolism, Europe, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Glutamic Acid, Humans, Insulin, Insulin Secretion, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Racial Groups, Synapses, LifeLines Cohort Study, ADIPOGen Consortium, AGEN-BMI Working Group, CARDIOGRAMplusC4D Consortium, CKDGen Consortium, GLGC, ICBP, MAGIC Investigators, MuTHER Consortium, MIGen Consortium, PAGE Consortium, ReproGen Consortium, GENIE Consortium, International Endogene Consortium, General Science & Technology
Abstract

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Published
2015

22. Defining the role of common variation in the genomic and biological architecture of adult human height

Author

Wood, Andrew R, Esko, Tonu, Yang, Jian, Vedantam, Sailaja, Pers, Tune H, Gustafsson, Stefan, Chu, Audrey Y, Estrada, Karol, Luan, Jian'an, Kutalik, Zoltán, Amin, Najaf, Buchkovich, Martin L, Croteau-Chonka, Damien C, Day, Felix R, Duan, Yanan, Fall, Tove, Fehrmann, Rudolf, Ferreira, Teresa, Jackson, Anne U, Karjalainen, Juha, Lo, Ken Sin, Locke, Adam E, Mägi, Reedik, Mihailov, Evelin, Porcu, Eleonora, Randall, Joshua C, Scherag, André, Vinkhuyzen, Anna AE, Westra, Harm-Jan, Winkler, Thomas W, Workalemahu, Tsegaselassie, Zhao, Jing Hua, Absher, Devin, Albrecht, Eva, Anderson, Denise, Baron, Jeffrey, Beekman, Marian, Demirkan, Ayse, Ehret, Georg B, Feenstra, Bjarke, Feitosa, Mary F, Fischer, Krista, Fraser, Ross M, Goel, Anuj, Gong, Jian, Justice, Anne E, Kanoni, Stavroula, Kleber, Marcus E, Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Lui, Julian C, Mangino, Massimo, Leach, Irene Mateo, Medina-Gomez, Carolina, Nalls, Michael A, Nyholt, Dale R, Palmer, Cameron D, Pasko, Dorota, Pechlivanis, Sonali, Prokopenko, Inga, Ried, Janina S, Ripke, Stephan, Shungin, Dmitry, Stancáková, Alena, Strawbridge, Rona J, Sung, Yun Ju, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W, van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V, Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Afzal, Uzma, Ärnlöv, Johan, Arscott, Gillian M, Bandinelli, Stefania, Barrett, Amy, Bellis, Claire, Bennett, Amanda J, Berne, Christian, Blüher, Matthias, Bolton, Jennifer L, Böttcher, Yvonne, Boyd, Heather A, Bruinenberg, Marcel, Buckley, Brendan M, Buyske, Steven, Caspersen, Ida H, Chines, Peter S, Clarke, Robert, Claudi-Boehm, Simone, Cooper, Matthew, Daw, E Warwick, De Jong, Pim A, Deelen, Joris, and Delgado, Graciela

Subjects
Biological Sciences, Genetics, Biotechnology, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Adult, Analysis of Variance, Body Height, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, White People, Electronic Medical Records and Genomics (eMEMERGEGE) Consortium, MIGen Consortium, PAGEGE Consortium, LifeLines Cohort Study, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability. The 697 variants clustered in 423 loci were enriched for genes, pathways and tissue types known to be involved in growth and together implicated genes and pathways not highlighted in earlier efforts, such as signaling by fibroblast growth factors, WNT/β-catenin and chondroitin sulfate-related genes. We identified several genes and pathways not previously connected with human skeletal growth, including mTOR, osteoglycin and binding of hyaluronic acid. Our results indicate a genetic architecture for human height that is characterized by a very large but finite number (thousands) of causal variants.

Published
2014

23. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals.

Author

Dastani, Zari, Hivert, Marie-France, Timpson, Nicholas, Perry, John RB, Yuan, Xin, Scott, Robert A, Henneman, Peter, Heid, Iris M, Kizer, Jorge R, Lyytikäinen, Leo-Pekka, Fuchsberger, Christian, Tanaka, Toshiko, Morris, Andrew P, Small, Kerrin, Isaacs, Aaron, Beekman, Marian, Coassin, Stefan, Lohman, Kurt, Qi, Lu, Kanoni, Stavroula, Pankow, James S, Uh, Hae-Won, Wu, Ying, Bidulescu, Aurelian, Rasmussen-Torvik, Laura J, Greenwood, Celia MT, Ladouceur, Martin, Grimsby, Jonna, Manning, Alisa K, Liu, Ching-Ti, Kooner, Jaspal, Mooser, Vincent E, Vollenweider, Peter, Kapur, Karen A, Chambers, John, Wareham, Nicholas J, Langenberg, Claudia, Frants, Rune, Willems-Vandijk, Ko, Oostra, Ben A, Willems, Sara M, Lamina, Claudia, Winkler, Thomas W, Psaty, Bruce M, Tracy, Russell P, Brody, Jennifer, Chen, Ida, Viikari, Jorma, Kähönen, Mika, Pramstaller, Peter P, Evans, David M, St Pourcain, Beate, Sattar, Naveed, Wood, Andrew R, Bandinelli, Stefania, Carlson, Olga D, Egan, Josephine M, Böhringer, Stefan, van Heemst, Diana, Kedenko, Lyudmyla, Kristiansson, Kati, Nuotio, Marja-Liisa, Loo, Britt-Marie, Harris, Tamara, Garcia, Melissa, Kanaya, Alka, Haun, Margot, Klopp, Norman, Wichmann, H-Erich, Deloukas, Panos, Katsareli, Efi, Couper, David J, Duncan, Bruce B, Kloppenburg, Margreet, Adair, Linda S, Borja, Judith B, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, Wilson, James G, Musani, Solomon, Guo, Xiuqing, Johnson, Toby, Semple, Robert, Teslovich, Tanya M, Allison, Matthew A, Redline, Susan, Buxbaum, Sarah G, Mohlke, Karen L, Meulenbelt, Ingrid, Ballantyne, Christie M, Dedoussis, George V, Hu, Frank B, Liu, Yongmei, Paulweber, Bernhard, Spector, Timothy D, Slagboom, P Eline, Ferrucci, Luigi, and Jula, Antti

Subjects
DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Humans, Diabetes Mellitus, Type 2, Insulin Resistance, Genetic Predisposition to Disease, Glucose Tolerance Test, Waist-Hip Ratio, Gene Expression, Polymorphism, Single Nucleotide, African Americans, Asian Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiponectin, Cholesterol, HDL, Metabolic Networks and Pathways, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Cholesterol, HDL, Genetics, Developmental Biology
Abstract

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p

Published
2012

24. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Allen, Hana Lango, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Åsa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Body Height, Body Mass Index, Body Size, Body Weight, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Obesity, Polymorphism, Single Nucleotide, White People, MAGIC, Procardis Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

25. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Lango Allen, Hana, Estrada, Karol, Lettre, Guillaume, Berndt, Sonja I, Weedon, Michael N, Rivadeneira, Fernando, Willer, Cristen J, Jackson, Anne U, Vedantam, Sailaja, Raychaudhuri, Soumya, Ferreira, Teresa, Wood, Andrew R, Weyant, Robert J, Segrè, Ayellet V, Speliotes, Elizabeth K, Wheeler, Eleanor, Soranzo, Nicole, Park, Ju-Hyun, Yang, Jian, Gudbjartsson, Daniel, Heard-Costa, Nancy L, Randall, Joshua C, Qi, Lu, Vernon Smith, Albert, Mägi, Reedik, Pastinen, Tomi, Liang, Liming, Heid, Iris M, Luan, Jian’an, Thorleifsson, Gudmar, Winkler, Thomas W, Goddard, Michael E, Sin Lo, Ken, Palmer, Cameron, Workalemahu, Tsegaselassie, Aulchenko, Yurii S, Johansson, Åsa, Carola Zillikens, M, Feitosa, Mary F, Esko, Tõnu, Johnson, Toby, Ketkar, Shamika, Kraft, Peter, Mangino, Massimo, Prokopenko, Inga, Absher, Devin, Albrecht, Eva, Ernst, Florian, Glazer, Nicole L, Hayward, Caroline, Hottenga, Jouke-Jan, Jacobs, Kevin B, Knowles, Joshua W, Kutalik, Zoltán, Monda, Keri L, Polasek, Ozren, Preuss, Michael, Rayner, Nigel W, Robertson, Neil R, Steinthorsdottir, Valgerdur, Tyrer, Jonathan P, Voight, Benjamin F, Wiklund, Fredrik, Xu, Jianfeng, Hua Zhao, Jing, Nyholt, Dale R, Pellikka, Niina, Perola, Markus, Perry, John RB, Surakka, Ida, Tammesoo, Mari-Liis, Altmaier, Elizabeth L, Amin, Najaf, Aspelund, Thor, Bhangale, Tushar, Boucher, Gabrielle, Chasman, Daniel I, Chen, Constance, Coin, Lachlan, Cooper, Matthew N, Dixon, Anna L, Gibson, Quince, Grundberg, Elin, Hao, Ke, Juhani Junttila, M, Kaplan, Lee M, Kettunen, Johannes, König, Inke R, Kwan, Tony, Lawrence, Robert W, Levinson, Douglas F, Lorentzon, Mattias, McKnight, Barbara, Morris, Andrew P, Müller, Martina, Suh Ngwa, Julius, Purcell, Shaun, Rafelt, Suzanne, Salem, Rany M, and Salvi, Erika

Subjects
Biological Sciences, Genetics, Health Sciences, Mathematical Sciences, Statistics, Human Genome, Clinical Research, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Body Height, Chromosomes, Human, Pair 3, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Metabolic Networks and Pathways, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P

Published
2010

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