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1. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

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

Subjects
Cholesterol, Lipids, Genetics, Genome-wide association study, GWAS, Biology (General), QH301-705.5, QH426-470
Abstract

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Published
2022
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2. Evaluation of a phenotype imputation approach using GAW20 simulated data

Author

Yuning Chen, Gina M. Peloso, and Josée Dupuis

Subjects
Medicine, Science
Abstract

Abstract Statistical power, which is the probability of correctly rejecting a false null hypothesis, is a limitation of genome-wide association studies (GWAS). Sample size is a major component of statistical power that can be easily affected by missingness in phenotypic data and restrain the ability to detect associated single-nucleotide polymorphisms (SNPs) with small effect sizes. Although some phenotypes are hard to collect because of cost and loss to follow-up, correlated phenotypes that are easily collected can be leveraged for association analysis. In this paper, we evaluate a phenotype imputation method that incorporates family structure and correlation between multiple phenotypes using GAW20 simulated data. The distribution of missing values is derived using information contained in the missing sample’s relatives and additional correlated phenotypes. We show that this imputation method can improve power in the association analysis compared with excluding observations with missing data, while achieving the correct Type I error rate. We also examine factors that may affect the imputation accuracy.

Published
2018
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3. Network analysis of drug effect on triglyceride-associated DNA methylation

Author

Elise Lim, Hanfei Xu, Peitao Wu, Daniel Posner, Jiayi Wu, Gina M. Peloso, Achilleas N. Pitsillides, Anita L. DeStefano, L. Adrienne Cupples, and Ching-Ti Liu

Subjects
Medicine, Science
Abstract

Abstract Background DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites. Methods We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules. Results Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways (p values

Published
2018
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4. Genome-wide association study for multiple phenotype analysis

Author

Gina M. Peloso, Ching-Ti Liu, Virginia Fisher, Adrienne Cupples, Xuan Deng, and Biqi Wang

Subjects
0301 basic medicine, Multivariate statistics, lcsh:R, Multiple traits, lcsh:Medicine, Genome-wide association study, General Medicine, Computational biology, 030105 genetics & heredity, Biology, Phenotype, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 030104 developmental biology, Marginal Analysis, Chromosome (genetic algorithm), lcsh:Q, lcsh:Science, Genetic association, Type I and type II errors
Abstract

Genome-wide association studies often collect multiple phenotypes for complex diseases. Multivariate joint analyses have higher power to detect genetic variants compared with the marginal analysis of each phenotype and are also able to identify loci with pleiotropic effects. We extend the unified score-based association test to incorporate family structure, apply different approaches to analyze multiple traits in GAW20 real samples, and compare the results. Through simulation studies, we confirm that the Type I error rate of the pedigree-based unified score association test is appropriately controlled. In marginalanalysis of triglyceride levels, we found 1 subgenome-wide significant variant on chromosome 6. Joint analyses identified several suggestive genome-wide significant signals, with the pedigree-based unified score association test yielding the greatest number of significant results.

Published
2018
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5. Choice of population structure informative principal components for adjustment in a case-control study

Author

Kathryn L. Lunetta and Gina M. Peloso

Subjects
lcsh:QH426-470, Genome-wide association study, Biology, Logistic regression, Polymorphism, Single Nucleotide, Gene Frequency, Covariate, Odds Ratio, Genetics, Humans, Computer Simulation, Genetics(clinical), Genetics (clinical), Selection (genetic algorithm), Genetic association, Principal Component Analysis, Models, Statistical, Confounding, Body Height, humanities, lcsh:Genetics, Genetics, Population, Logistic Models, Phenotype, ComputingMethodologies_PATTERNRECOGNITION, Case-Control Studies, Principal component analysis, Research Article, Genome-Wide Association Study, Type I and type II errors
Abstract

Background There are many ways to perform adjustment for population structure. It remains unclear what the optimal approach is and whether the optimal approach varies by the type of samples and substructure present. The simplest and most straightforward approach is to adjust for the continuous principal components (PCs) that capture ancestry. Through simulation, we explored the issue of which ancestry informative PCs should be adjusted for in an association model to control for the confounding nature of population structure while maintaining maximum power. A thorough examination of selecting PCs for adjustment in a case-control study across the possible structure scenarios that could occur in a genome-wide association study has not been previously reported. Results We found that when the SNP and phenotype frequencies do not vary over the sub-populations, all methods of selection provided similar power and appropriate Type I error for association. When the SNP is not structured and the phenotype has large structure, then selection methods that do not select PCs for inclusion as covariates generally provide the most power. When there is a structured SNP and a non-structured phenotype, selection methods that include PCs in the model have greater power. When both the SNP and the phenotype are structured, all methods of selection have similar power. Conclusions Standard practice is to include a fixed number of PCs in genome-wide association studies. Based on our findings, we conclude that if power is not a concern, then selecting the same set of top PCs for adjustment for all SNPs in logistic regression is a strategy that achieves appropriate Type I error. However, standard practice is not optimal in all scenarios and to optimize power for structured SNPs in the presence of unstructured phenotypes, PCs that are associated with the tested SNP should be included in the logistic model.

Published
2011

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