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1. Rare variant contribution to the heritability of coronary artery disease

Author: Ghislain Rocheleau, Shoa L. Clarke, Gaëlle Auguste, Natalie R. Hasbani, Alanna C. Morrison, Adam S. Heath, Lawrence F. Bielak, Kruthika R. Iyer, Erica P. Young, Nathan O. Stitziel, Goo Jun, Cecelia Laurie, Jai G. Broome, Alyna T. Khan, Donna K. Arnett, Lewis C. Becker, Joshua C. Bis, Eric Boerwinkle, Donald W. Bowden, April P. Carson, Patrick T. Ellinor, Myriam Fornage, Nora Franceschini, Barry I. Freedman, Nancy L. Heard-Costa, Lifang Hou, Yii-Der Ida Chen, Eimear E. Kenny, Charles Kooperberg, Brian G. Kral, Ruth J. F. Loos, Sharon M. Lutz, JoAnn E. Manson, Lisa W. Martin, Braxton D. Mitchell, Rami Nassir, Nicholette D. Palmer, Wendy S. Post, Michael H. Preuss, Bruce M. Psaty, Laura M. Raffield, Elizabeth A. Regan, Stephen S. Rich, Jennifer A. Smith, Kent D. Taylor, Lisa R. Yanek, Kendra A. Young, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Austin T. Hilliard, Catherine Tcheandjieu, Patricia A. Peyser, Ramachandran S. Vasan, Jerome I. Rotter, Clint L. Miller, Themistocles L. Assimes, Paul S. de Vries, and Ron Do
Subjects: Science
Abstract: Abstract Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.
Published: 2024
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2. An approach to identify gene-environment interactions and reveal new biological insight in complex traits

Author: Xiaofeng Zhu, Yihe Yang, Noah Lorincz-Comi, Gen Li, Amy R. Bentley, Paul S. de Vries, Michael Brown, Alanna C. Morrison, Charles N. Rotimi, W. James Gauderman, Dabeeru C. Rao, Hugues Aschard, and the CHARGE Gene-lifestyle Interactions Working Group
Subjects: Science
Abstract: Abstract There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P
Published: 2024
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3. Genetic insights into resting heart rate and its role in cardiovascular disease

Author: Yordi J. van de Vegte, Ruben N. Eppinga, M. Yldau van der Ende, Yanick P. Hagemeijer, Yuvaraj Mahendran, Elias Salfati, Albert V. Smith, Vanessa Y. Tan, Dan E. Arking, Ioanna Ntalla, Emil V. Appel, Claudia Schurmann, Jennifer A. Brody, Rico Rueedi, Ozren Polasek, Gardar Sveinbjornsson, Cecile Lecoeur, Claes Ladenvall, Jing Hua Zhao, Aaron Isaacs, Lihua Wang, Jian’an Luan, Shih-Jen Hwang, Nina Mononen, Kirsi Auro, Anne U. Jackson, Lawrence F. Bielak, Linyao Zeng, Nabi Shah, Maria Nethander, Archie Campbell, Tuomo Rankinen, Sonali Pechlivanis, Lu Qi, Wei Zhao, Federica Rizzi, Toshiko Tanaka, Antonietta Robino, Massimiliano Cocca, Leslie Lange, Martina Müller-Nurasyid, Carolina Roselli, Weihua Zhang, Marcus E. Kleber, Xiuqing Guo, Henry J. Lin, Francesca Pavani, Tessel E. Galesloot, Raymond Noordam, Yuri Milaneschi, Katharina E. Schraut, Marcel den Hoed, Frauke Degenhardt, Stella Trompet, Marten E. van den Berg, Giorgio Pistis, Yih-Chung Tham, Stefan Weiss, Xueling S. Sim, Hengtong L. Li, Peter J. van der Most, Ilja M. Nolte, Leo-Pekka Lyytikäinen, M. Abdullah Said, Daniel R. Witte, Carlos Iribarren, Lenore Launer, Susan M. Ring, Paul S. de Vries, Peter Sever, Allan Linneberg, Erwin P. Bottinger, Sandosh Padmanabhan, Bruce M. Psaty, Nona Sotoodehnia, Ivana Kolcic, The DCCT/EDIC Research Group, David O. Arnar, Daniel F. Gudbjartsson, Hilma Holm, Beverley Balkau, Claudia T. Silva, Christopher H. Newton-Cheh, Kjell Nikus, Perttu Salo, Karen L. Mohlke, Patricia A. Peyser, Heribert Schunkert, Mattias Lorentzon, Jari Lahti, Dabeeru C. Rao, Marilyn C. Cornelis, Jessica D. Faul, Jennifer A. Smith, Katarzyna Stolarz-Skrzypek, Stefania Bandinelli, Maria Pina Concas, Gianfranco Sinagra, Thomas Meitinger, Melanie Waldenberger, Moritz F. Sinner, Konstantin Strauch, Graciela E. Delgado, Kent D. Taylor, Jie Yao, Luisa Foco, Olle Melander, Jacqueline de Graaf, Renée de Mutsert, Eco J. C. de Geus, Åsa Johansson, Peter K. Joshi, Lars Lind, Andre Franke, Peter W. Macfarlane, Kirill V. Tarasov, Nicholas Tan, Stephan B. Felix, E-Shyong Tai, Debra Q. Quek, Harold Snieder, Johan Ormel, Martin Ingelsson, Cecilia Lindgren, Andrew P. Morris, Olli T. Raitakari, Torben Hansen, Themistocles Assimes, Vilmundur Gudnason, Nicholas J. Timpson, Alanna C. Morrison, Patricia B. Munroe, David P. Strachan, Niels Grarup, Ruth J. F. Loos, Susan R. Heckbert, Peter Vollenweider, Caroline Hayward, Kari Stefansson, Philippe Froguel, Leif Groop, Nicholas J. Wareham, Cornelia M. van Duijn, Mary F. Feitosa, Christopher J. O’Donnell, Mika Kähönen, Markus Perola, Michael Boehnke, Sharon L. R. Kardia, Jeanette Erdmann, Colin N. A. Palmer, Claes Ohlsson, David J. Porteous, Johan G. Eriksson, Claude Bouchard, Susanne Moebus, Peter Kraft, David R. Weir, Daniele Cusi, Luigi Ferrucci, Sheila Ulivi, Giorgia Girotto, Adolfo Correa, Stefan Kääb, Annette Peters, John C. Chambers, Jaspal S. Kooner, Winfried März, Jerome I. Rotter, Andrew A. Hicks, J. Gustav Smith, Lambertus A. L. M. Kiemeney, Dennis O. Mook-Kanamori, Brenda W. J. H. Penninx, Ulf Gyllensten, James F. Wilson, Stephen Burgess, Johan Sundström, Wolfgang Lieb, J. Wouter Jukema, Mark Eijgelsheim, Edward L. M. Lakatta, Ching-Yu Cheng, Marcus Dörr, Tien-Yin Wong, Charumathi Sabanayagam, Albertine J. Oldehinkel, Harriette Riese, Terho Lehtimäki, Niek Verweij, and Pim van der Harst
Subjects: Science
Abstract: Abstract Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.
Published: 2023
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4. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program

Author: Nicole D. Armstrong, Vinodh Srinivasasainagendra, Farah Ammous, Themistocles L. Assimes, Amber L. Beitelshees, Jennifer Brody, Brian E. Cade, Yii-Der Ida Chen, Han Chen, Paul S. de Vries, James S. Floyd, Nora Franceschini, Xiuqing Guo, Jacklyn N. Hellwege, John S. House, Chii-Min Hwu, Sharon L. R. Kardia, Ethan M. Lange, Leslie A. Lange, Caitrin W. McDonough, May E. Montasser, Jeffrey R. O’Connell, Megan M. Shuey, Xiao Sun, Rikki M. Tanner, Zhe Wang, Wei Zhao, April P. Carson, Todd L. Edwards, Tanika N. Kelly, Eimear E. Kenny, Charles Kooperberg, Ruth J. F. Loos, Alanna C. Morrison, Alison Motsinger-Reif, Bruce M. Psaty, Dabeeru C. Rao, Susan Redline, Stephen S. Rich, Jerome I. Rotter, Jennifer A. Smith, Albert V. Smith, Marguerite R. Irvin, and Donna K. Arnett
Subjects: blood pressure, antihypertensive response, whole genome sequencing, TOPMed, treatment resistant hypertension, Genetics, QH426-470
Abstract: Introduction: Apparent treatment-resistant hypertension (aTRH) is characterized by the use of four or more antihypertensive (AHT) classes to achieve blood pressure (BP) control. In the current study, we conducted single-variant and gene-based analyses of aTRH among individuals from 12 Trans-Omics for Precision Medicine cohorts with whole-genome sequencing data.Methods: Cases were defined as individuals treated for hypertension (HTN) taking three different AHT classes, with average systolic BP ≥ 140 or diastolic BP ≥ 90 mmHg, or four or more medications regardless of BP (n = 1,705). A normotensive control group was defined as individuals with BP < 140/90 mmHg (n = 22,079), not on AHT medication. A second control group comprised individuals who were treatment responsive on one AHT medication with BP < 140/ 90 mmHg (n = 5,424). Logistic regression with kinship adjustment using the Scalable and Accurate Implementation of Generalized mixed models (SAIGE) was performed, adjusting for age, sex, and genetic ancestry. We assessed variants using SKAT-O in rare-variant analyses. Single-variant and gene-based tests were conducted in a pooled multi-ethnicity stratum, as well as self-reported ethnic/racial strata (European and African American).Results: One variant in the known HTN locus, KCNK3, was a top finding in the multi-ethnic analysis (p = 8.23E-07) for the normotensive control group [rs12476527, odds ratio (95% confidence interval) = 0.80 (0.74–0.88)]. This variant was replicated in the Vanderbilt University Medical Center’s DNA repository data. Aggregate gene-based signals included the genes AGTPBP, MYL4, PDCD4, BBS9, ERG, and IER3.Discussion: Additional work validating these loci in larger, more diverse populations, is warranted to determine whether these regions influence the pathobiology of aTRH.
Published: 2023
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5. Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci

Author: Lisa de las Fuentes, Karen L. Schwander, Michael R. Brown, Amy R. Bentley, Thomas W. Winkler, Yun Ju Sung, Patricia B. Munroe, Clint L. Miller, Hugo Aschard, Stella Aslibekyan, Traci M. Bartz, Lawrence F. Bielak, Jin Fang Chai, Ching-Yu Cheng, Rajkumar Dorajoo, Mary F. Feitosa, Xiuqing Guo, Fernando P. Hartwig, Andrea Horimoto, Ivana Kolčić, Elise Lim, Yongmei Liu, Alisa K. Manning, Jonathan Marten, Solomon K. Musani, Raymond Noordam, Sandosh Padmanabhan, Tuomo Rankinen, Melissa A. Richard, Paul M. Ridker, Albert V. Smith, Dina Vojinovic, Alan B. Zonderman, Maris Alver, Mathilde Boissel, Kaare Christensen, Barry I. Freedman, Chuan Gao, Franco Giulianini, Sarah E. Harris, Meian He, Fang-Chi Hsu, Brigitte Kühnel, Federica Laguzzi, Xiaoyin Li, Leo-Pekka Lyytikäinen, Ilja M. Nolte, Alaitz Poveda, Rainer Rauramaa, Muhammad Riaz, Antonietta Robino, Tamar Sofer, Fumihiko Takeuchi, Bamidele O. Tayo, Peter J. van der Most, Niek Verweij, Erin B. Ware, Stefan Weiss, Wanqing Wen, Lisa R. Yanek, Yiqiang Zhan, Najaf Amin, Dan E. Arking, Christie Ballantyne, Eric Boerwinkle, Jennifer A. Brody, Ulrich Broeckel, Archie Campbell, Mickaël Canouil, Xiaoran Chai, Yii-Der Ida Chen, Xu Chen, Kumaraswamy Naidu Chitrala, Maria Pina Concas, Ulf de Faire, Renée de Mutsert, H. Janaka de Silva, Paul S. de Vries, Ahn Do, Jessica D. Faul, Virginia Fisher, James S. Floyd, Terrence Forrester, Yechiel Friedlander, Giorgia Girotto, C. Charles Gu, Göran Hallmans, Sami Heikkinen, Chew-Kiat Heng, Georg Homuth, Steven Hunt, M. Arfan Ikram, David R. Jacobs, Maryam Kavousi, Chiea Chuen Khor, Tuomas O. Kilpeläinen, Woon-Puay Koh, Pirjo Komulainen, Carl D. Langefeld, Jingjing Liang, Kiang Liu, Jianjun Liu, Kurt Lohman, Reedik Mägi, Ani W. Manichaikul, Colin A. McKenzie, Thomas Meitinger, Yuri Milaneschi, Matthias Nauck, Christopher P. Nelson, Jeffrey R. O’Connell, Nicholette D. Palmer, Alexandre C. Pereira, Thomas Perls, Annette Peters, Ozren Polašek, Olli T. Raitakari, Kenneth Rice, Treva K. Rice, Stephen S. Rich, Charumathi Sabanayagam, Pamela J. Schreiner, Xiao-Ou Shu, Stephen Sidney, Mario Sims, Jennifer A. Smith, John M. Starr, Konstantin Strauch, E. Shyong Tai, Kent D. Taylor, Michael Y. Tsai, André G. Uitterlinden, Diana van Heemst, Melanie Waldenberger, Ya-Xing Wang, Wen-Bin Wei, Gregory Wilson, Deng Xuan, Jie Yao, Caizheng Yu, Jian-Min Yuan, Wei Zhao, Diane M. Becker, Amélie Bonnefond, Donald W. Bowden, Richard S. Cooper, Ian J. Deary, Jasmin Divers, Tõnu Esko, Paul W. Franks, Philippe Froguel, Christian Gieger, Jost B. Jonas, Norihiro Kato, Timo A. Lakka, Karin Leander, Terho Lehtimäki, Patrik K. E. Magnusson, Kari E. North, Ioanna Ntalla, Brenda Penninx, Nilesh J. Samani, Harold Snieder, Beatrice Spedicati, Pim van der Harst, Henry Völzke, Lynne E. Wagenknecht, David R. Weir, Mary K. Wojczynski, Tangchun Wu, Wei Zheng, Xiaofeng Zhu, Claude Bouchard, Daniel I. Chasman, Michele K. Evans, Ervin R. Fox, Vilmundur Gudnason, Caroline Hayward, Bernardo L. Horta, Sharon L. R. Kardia, Jose Eduardo Krieger, Dennis O. Mook-Kanamori, Patricia A. Peyser, Michael M. Province, Bruce M. Psaty, Igor Rudan, Xueling Sim, Blair H. Smith, Rob M. van Dam, Cornelia M. van Duijn, Tien Yin Wong, Donna K. Arnett, Dabeeru C. Rao, James Gauderman, Ching-Ti Liu, Alanna C. Morrison, Jerome I. Rotter, and Myriam Fornage
Subjects: educational attainment, lipids, cholesterol, triglycerides, genome-wide association study, meta-analysis, Genetics, QH426-470
Abstract: Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes.Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10−8) and suggestive (p < 1 × 10−6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals).Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue.Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.
Published: 2023
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6. 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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7. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Author: Marsha M. Wheeler, Adrienne M. Stilp, Shuquan Rao, Bjarni V. Halldórsson, Doruk Beyter, Jia Wen, Anna V. Mihkaylova, Caitlin P. McHugh, John Lane, Min-Zhi Jiang, Laura M. Raffield, Goo Jun, Fritz J. Sedlazeck, Ginger Metcalf, Yao Yao, Joshua B. Bis, Nathalie Chami, Paul S. de Vries, Pinkal Desai, James S. Floyd, Yan Gao, Kai Kammers, Wonji Kim, Jee-Young Moon, Aakrosh Ratan, Lisa R. Yanek, Laura Almasy, Lewis C. Becker, John Blangero, Michael H. Cho, Joanne E. Curran, Myriam Fornage, Robert C. Kaplan, Joshua P. Lewis, Ruth J. F. Loos, Braxton D. Mitchell, Alanna C. Morrison, Michael Preuss, Bruce M. Psaty, Stephen S. Rich, Jerome I. Rotter, Hua Tang, Russell P. Tracy, Eric Boerwinkle, Goncalo R. Abecasis, Thomas W. Blackwell, Albert V. Smith, Andrew D. Johnson, Rasika A. Mathias, Deborah A. Nickerson, Matthew P. Conomos, Yun Li, Unnur Þorsteinsdóttir, Magnús K. Magnússon, Kari Stefansson, Nathan D. Pankratz, Daniel E. Bauer, Paul L. Auer, and Alex P. Reiner
Subjects: Science
Abstract: Most genetic association studies have been done on single nucleotide polymorphisms and small indels, while other types of variants have been less studied. Here, the authors use whole genome sequencing in a diverse population to identify and provide experimental evidence for associations between structural variants and blood-cell traits.
Published: 2022
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8. Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Author: Margaret Sunitha Selvaraj, Xihao Li, Zilin Li, Akhil Pampana, David Y. Zhang, Joseph Park, Stella Aslibekyan, Joshua C. Bis, Jennifer A. Brody, Brian E. Cade, Lee-Ming Chuang, Ren-Hua Chung, Joanne E. Curran, Lisa de las Fuentes, Paul S. de Vries, Ravindranath Duggirala, Barry I. Freedman, Mariaelisa Graff, Xiuqing Guo, Nancy Heard-Costa, Bertha Hidalgo, Chii-Min Hwu, Marguerite R. Irvin, Tanika N. Kelly, Brian G. Kral, Leslie Lange, Xiaohui Li, Martin Lisa, Steven A. Lubitz, Ani W. Manichaikul, Preuss Michael, May E. Montasser, Alanna C. Morrison, Take Naseri, Jeffrey R. O’Connell, Nicholette D. Palmer, Patricia A. Peyser, Muagututia S. Reupena, Jennifer A. Smith, Xiao Sun, Kent D. Taylor, Russell P. Tracy, Michael Y. Tsai, Zhe Wang, Yuxuan Wang, Wei Bao, John T. Wilkins, Lisa R. Yanek, Wei Zhao, Donna K. Arnett, John Blangero, Eric Boerwinkle, Donald W. Bowden, Yii-Der Ida Chen, Adolfo Correa, L. Adrienne Cupples, Susan K. Dutcher, Patrick T. Ellinor, Myriam Fornage, Stacey Gabriel, Soren Germer, Richard Gibbs, Jiang He, Robert C. Kaplan, Sharon L. R. Kardia, Ryan Kim, Charles Kooperberg, Ruth J. F. Loos, Karine A Viaud-Martinez, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, Deborah Nickerson, Kari E. North, Bruce M. Psaty, Susan Redline, Alexander P. Reiner, Ramachandran S. Vasan, Stephen S. Rich, Cristen Willer, Jerome I. Rotter, Daniel J. Rader, Xihong Lin, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Gina M. Peloso, and Pradeep Natarajan
Subjects: Science
Abstract: Although the common genetic variants contributing to blood lipid levels have been studied, the contribution of rare variants is less understood. Here, the authors perform a rare coding and noncoding variant association study of blood lipid levels using whole genome sequencing data.
Published: 2022
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9. Whole genome sequence association analysis of fasting glucose and fasting insulin levels in diverse cohorts from the NHLBI TOPMed program

Author: Daniel DiCorpo, Sheila M. Gaynor, Emily M. Russell, Kenneth E. Westerman, Laura M. Raffield, Timothy D. Majarian, Peitao Wu, Chloé Sarnowski, Heather M. Highland, Anne Jackson, Natalie R. Hasbani, Paul S. de Vries, Jennifer A. Brody, Bertha Hidalgo, Xiuqing Guo, James A. Perry, Jeffrey R. O’Connell, Samantha Lent, May E. Montasser, Brian E. Cade, Deepti Jain, Heming Wang, Ricardo D’Oliveira Albanus, Arushi Varshney, Lisa R. Yanek, Leslie Lange, Nicholette D. Palmer, Marcio Almeida, Juan M. Peralta, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Lawrence F. Bielak, Chung-Shiuan Chen, Yii-Der Ida Chen, Won Jung Choi, Mark O. Goodarzi, James S. Floyd, Marguerite R. Irvin, Rita R. Kalyani, Tanika N. Kelly, Seonwook Lee, Ching-Ti Liu, Douglas Loesch, JoAnn E. Manson, Ryan L. Minster, Take Naseri, James S. Pankow, Laura J. Rasmussen-Torvik, Alexander P. Reiner, Muagututi’a Sefuiva Reupena, Elizabeth Selvin, Jennifer A. Smith, Daniel E. Weeks, Huichun Xu, Jie Yao, Wei Zhao, Stephen Parker, Alvaro Alonso, Donna K. Arnett, John Blangero, Eric Boerwinkle, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Ravindranath Duggirala, Jiang He, Susan R. Heckbert, Sharon L. R. Kardia, Ryan W. Kim, Charles Kooperberg, Simin Liu, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, Alanna C. Morrison, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Alan R. Shuldiner, Kent D. Taylor, Ramachandran S. Vasan, Karine A. Viaud-Martinez, Jose C. Florez, James G. Wilson, Robert Sladek, Stephen S. Rich, Jerome I. Rotter, Xihong Lin, Josée Dupuis, James B. Meigs, Jennifer Wessel, and Alisa K. Manning
Subjects: Biology (General), QH301-705.5
Abstract: This study of 23,000 non-diabetic individuals highlights loci associated with fasting glucose and fasting insulin in diverse cohorts with whole genome sequence data.
Published: 2022
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10. Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author: David Stacey, Lingyan Chen, Paulina J. Stanczyk, Joanna M. M. Howson, Amy M. Mason, Stephen Burgess, Stephen MacDonald, Jonathan Langdown, Harriett McKinney, Kate Downes, Neda Farahi, James E. Peters, Saonli Basu, James S. Pankow, Weihong Tang, Nathan Pankratz, Maria Sabater-Lleal, Paul S. de Vries, Nicholas L. Smith, CHARGE Hemostasis Working Group, Amy D. Gelinas, Daniel J. Schneider, Nebojsa Janjic, Nilesh J. Samani, Shu Ye, Charlotte Summers, Edwin R. Chilvers, John Danesh, and Dirk S. Paul
Subjects: Science
Abstract: Many individual genetic risk loci associate with multiple diseases, but the molecular basis of these loci often remains unclear. Here, the authors provide a framework to reveal the genetic cross-disease associations at the PROCR vascular disease locus.
Published: 2022
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11. Rare coding variants in RCN3 are associated with blood pressure

Author: Karen Y. He, Tanika N. Kelly, Heming Wang, Jingjing Liang, Luke Zhu, Brian E. Cade, Themistocles L. Assimes, Lewis C. Becker, Amber L. Beitelshees, Lawrence F. Bielak, Adam P. Bress, Jennifer A. Brody, Yen-Pei Christy Chang, Yi-Cheng Chang, Paul S. de Vries, Ravindranath Duggirala, Ervin R. Fox, Nora Franceschini, Anna L. Furniss, Yan Gao, Xiuqing Guo, Jeffrey Haessler, Yi-Jen Hung, Shih-Jen Hwang, Marguerite Ryan Irvin, Rita R. Kalyani, Ching-Ti Liu, Chunyu Liu, Lisa Warsinger Martin, May E. Montasser, Paul M. Muntner, Stanford Mwasongwe, Take Naseri, Walter Palmas, Muagututi’a Sefuiva Reupena, Kenneth M. Rice, Wayne H.-H. Sheu, Daichi Shimbo, Jennifer A. Smith, Beverly M. Snively, Lisa R. Yanek, Wei Zhao, John Blangero, Eric Boerwinkle, Yii-Der Ida Chen, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Myriam Fornage, Jiang He, Lifang Hou, Robert C. Kaplan, Sharon L. R. Kardia, Eimear E. Kenny, Charles Kooperberg, Donald Lloyd-Jones, Ruth J. F. Loos, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, Kari E. North, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, D. C. Rao, Susan Redline, Alex P. Reiner, Stephen S. Rich, Jerome I. Rotter, Kent D. Taylor, Russell Tracy, Ramachandran S. Vasan, The Samoan Obesity, Lifestyle and Genetic Adaptations Study (OLaGA) Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Alanna C. Morrison, Daniel Levy, Aravinda Chakravarti, Donna K. Arnett, and Xiaofeng Zhu
Subjects: Rare variant analysis, Blood pressure, Whole genome sequencing, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract: Abstract Background While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. Results Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10− 7). Conclusions Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.
Published: 2022
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12. A multi-omics study of circulating phospholipid markers of blood pressure

Author: Jun Liu, Paul S. de Vries, Fabiola Del Greco M., Åsa Johansson, Katharina E. Schraut, Caroline Hayward, Ko Willems van Dijk, Oscar. H. Franco, Andrew A. Hicks, Veronique Vitart, Igor Rudan, Harry Campbell, Ozren Polašek, Peter P. Pramstaller, James F. Wilson, Ulf Gyllensten, Cornelia M. van Duijn, Abbas Dehghan, and Ayşe Demirkan
Subjects: Medicine, Science
Abstract: Abstract High-throughput techniques allow us to measure a wide-range of phospholipids which can provide insight into the mechanisms of hypertension. We aimed to conduct an in-depth multi-omics study of various phospholipids with systolic blood pressure (SBP) and diastolic blood pressure (DBP). The associations of blood pressure and 151 plasma phospholipids measured by electrospray ionization tandem mass spectrometry were performed by linear regression in five European cohorts (n = 2786 in discovery and n = 1185 in replication). We further explored the blood pressure-related phospholipids in Erasmus Rucphen Family (ERF) study by associating them with multiple cardiometabolic traits (linear regression) and predicting incident hypertension (Cox regression). Mendelian Randomization (MR) and phenome-wide association study (Phewas) were also explored to further investigate these association results. We identified six phosphatidylethanolamines (PE 38:3, PE 38:4, PE 38:6, PE 40:4, PE 40:5 and PE 40:6) and two phosphatidylcholines (PC 32:1 and PC 40:5) which together predicted incident hypertension with an area under the ROC curve (AUC) of 0.61. The identified eight phospholipids are strongly associated with triglycerides, obesity related traits (e.g. waist, waist-hip ratio, total fat percentage, body mass index, lipid-lowering medication, and leptin), diabetes related traits (e.g. glucose, insulin resistance and insulin) and prevalent type 2 diabetes. The genetic determinants of these phospholipids also associated with many lipoproteins, heart rate, pulse rate and blood cell counts. No significant association was identified by bi-directional MR approach. We identified eight blood pressure-related circulating phospholipids that have a predictive value for incident hypertension. Our cross-omics analyses show that phospholipid metabolites in the circulation may yield insight into blood pressure regulation and raise a number of testable hypothesis for future research.
Published: 2022
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13. Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption

Author: Timothy D. Majarian, Amy R. Bentley, Vincent Laville, Michael R. Brown, Daniel I. Chasman, Paul S. de Vries, Mary F. Feitosa, Nora Franceschini, W. James Gauderman, Casey Marchek, Daniel Levy, Alanna C. Morrison, Michael Province, Dabeeru C. Rao, Karen Schwander, Yun Ju Sung, Charles N. Rotimi, Hugues Aschard, C. Charles Gu, Alisa K. Manning, and on behalf of the CHARGE Gene-Lifestyle Interactions Working Group
Subjects: multi-omics, gene-lifestyle interactions, smoking, alcohol, serum lipids, blood pressure, Genetics, QH426-470
Abstract: Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’s Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and ‘omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.
Published: 2022
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14. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Author: Pradeep Natarajan, Akhil Pampana, Sarah E. Graham, Sanni E. Ruotsalainen, James A. Perry, Paul S. de Vries, Jai G. Broome, James P. Pirruccello, Michael C. Honigberg, Krishna Aragam, Brooke Wolford, Jennifer A. Brody, Lucinda Antonacci-Fulton, Moscati Arden, Stella Aslibekyan, Themistocles L. Assimes, Christie M. Ballantyne, Lawrence F. Bielak, Joshua C. Bis, Brian E. Cade, Ron Do, Harsha Doddapaneni, Leslie S. Emery, Yi-Jen Hung, Marguerite R. Irvin, Alyna T. Khan, Leslie Lange, Jiwon Lee, Rozenn N. Lemaitre, Lisa W. Martin, Ginger Metcalf, May E. Montasser, Jee-Young Moon, Donna Muzny, Jeffrey R. O’Connell, Nicholette D. Palmer, Juan M. Peralta, Patricia A. Peyser, Adrienne M. Stilp, Michael Tsai, Fei Fei Wang, Daniel E. Weeks, Lisa R. Yanek, James G. Wilson, Goncalo Abecasis, Donna K. Arnett, Lewis C. Becker, John Blangero, Eric Boerwinkle, Donald W. Bowden, Yi-Cheng Chang, Yii-Der I. Chen, Won Jung Choi, Adolfo Correa, Joanne E. Curran, Mark J. Daly, Susan K. Dutcher, Patrick T. Ellinor, Myriam Fornage, Barry I. Freedman, Stacey Gabriel, Soren Germer, Richard A. Gibbs, Jiang He, Kristian Hveem, Gail P. Jarvik, Robert C. Kaplan, Sharon L. R. Kardia, Eimear Kenny, Ryan W. Kim, Charles Kooperberg, Cathy C. Laurie, Seonwook Lee, Don M. Lloyd-Jones, Ruth J. F. Loos, Steven A. Lubitz, Rasika A. Mathias, Karine A. Viaud Martinez, Stephen T. McGarvey, Braxton D. Mitchell, Deborah A. Nickerson, Kari E. North, Aarno Palotie, Cheol Joo Park, Bruce M. Psaty, D. C. Rao, Susan Redline, Alexander P. Reiner, Daekwan Seo, Jeong-Sun Seo, Albert V. Smith, Russell P. Tracy, Ramachandran S. Vasan, Sekar Kathiresan, L. Adrienne Cupples, Jerome I. Rotter, Alanna C. Morrison, Stephen S. Rich, Samuli Ripatti, Cristen Willer, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, FinnGen, and Gina M. Peloso
Subjects: Science
Abstract: The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.
Published: 2021
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15. The choline transporter Slc44a2 controls platelet activation and thrombosis by regulating mitochondrial function

Author: J. Allen Bennett, Michael A. Mastrangelo, Sara K. Ture, Charles O. Smith, Shannon G. Loelius, Rachel A. Berg, Xu Shi, Ryan M. Burke, Sherry L. Spinelli, Scott J. Cameron, Thomas E. Carey, Paul S. Brookes, Robert E. Gerszten, Maria Sabater-Lleal, Paul S. de Vries, Jennifer E. Huffman, Nicholas L. Smith, Craig N. Morrell, and Charles J. Lowenstein
Subjects: Science
Abstract: Genetic association studies have identified loci including the choline transporter SLC44A2 as a potential regulator of thrombosis. Here the authors report that loss of SLC44A2 impairs platelet activation and thrombosis in mice via a reduction of mitochondrial ATP production.
Published: 2020
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16. Deriving stratified effects from joint models investigating gene-environment interactions

Author: Vincent Laville, Timothy Majarian, Paul S. de Vries, Amy R. Bentley, Mary F. Feitosa, Yun J. Sung, D. C. Rao, Alisa Manning, Hugues Aschard, and on behalf of the CHARGE Gene-Lifestyle Interactions Working Group
Subjects: Gene-environment interaction, Binary exposure, Stratified analysis, Summary statistics, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract: Abstract Background Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models. Results In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias. Conclusions These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at https://gitlab.pasteur.fr/statistical-genetics/j2s .
Published: 2020
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17. Publisher Correction: Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author: David Stacey, Lingyan Chen, Paulina J. Stanczyk, Joanna M. M. Howson, Amy M. Mason, Stephen Burgess, Stephen MacDonald, Jonathan Langdown, Harriett McKinney, Kate Downes, Neda Farahi, James E. Peters, Saonli Basu, James S. Pankow, Weihong Tang, Nathan Pankratz, Maria Sabater-Lleal, Paul S. de Vries, Nicholas L. Smith, CHARGE Hemostasis Working Group, Amy D. Gelinas, Daniel J. Schneider, Nebojsa Janjic, Nilesh J. Samani, Shu Ye, Charlotte Summers, Edwin R. Chilvers, John Danesh, and Dirk S. Paul
Subjects: Science
Published: 2022
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18. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration

Author: Raymond Noordam, Maxime M. Bos, Heming Wang, Thomas W. Winkler, Amy R. Bentley, Tuomas O. Kilpeläinen, Paul S. de Vries, Yun Ju Sung, Karen Schwander, Brian E. Cade, Alisa Manning, Hugues Aschard, Michael R. Brown, Han Chen, Nora Franceschini, Solomon K. Musani, Melissa Richard, Dina Vojinovic, Stella Aslibekyan, Traci M. Bartz, Lisa de las Fuentes, Mary Feitosa, Andrea R. Horimoto, Marjan Ilkov, Minjung Kho, Aldi Kraja, Changwei Li, Elise Lim, Yongmei Liu, Dennis O. Mook-Kanamori, Tuomo Rankinen, Salman M. Tajuddin, Ashley van der Spek, Zhe Wang, Jonathan Marten, Vincent Laville, Maris Alver, Evangelos Evangelou, Maria E. Graff, Meian He, Brigitte Kühnel, Leo-Pekka Lyytikäinen, Pedro Marques-Vidal, Ilja M. Nolte, Nicholette D. Palmer, Rainer Rauramaa, Xiao-Ou Shu, Harold Snieder, Stefan Weiss, Wanqing Wen, Lisa R. Yanek, Correa Adolfo, Christie Ballantyne, Larry Bielak, Nienke R. Biermasz, Eric Boerwinkle, Niki Dimou, Gudny Eiriksdottir, Chuan Gao, Sina A. Gharib, Daniel J. Gottlieb, José Haba-Rubio, Tamara B. Harris, Sami Heikkinen, Raphaël Heinzer, James E. Hixson, Georg Homuth, M. Arfan Ikram, Pirjo Komulainen, Jose E. Krieger, Jiwon Lee, Jingmin Liu, Kurt K. Lohman, Annemarie I. Luik, Reedik Mägi, Lisa W. Martin, Thomas Meitinger, Andres Metspalu, Yuri Milaneschi, Mike A. Nalls, Jeff O’Connell, Annette Peters, Patricia Peyser, Olli T. Raitakari, Alex P. Reiner, Patrick C. N. Rensen, Treva K. Rice, Stephen S. Rich, Till Roenneberg, Jerome I. Rotter, Pamela J. Schreiner, James Shikany, Stephen S. Sidney, Mario Sims, Colleen M. Sitlani, Tamar Sofer, Konstantin Strauch, Morris A. Swertz, Kent D. Taylor, André G. Uitterlinden, Cornelia M. van Duijn, Henry Völzke, Melanie Waldenberger, Robert B. Wallance, Ko Willems van Dijk, Caizheng Yu, Alan B. Zonderman, Diane M. Becker, Paul Elliott, Tõnu Esko, Christian Gieger, Hans J. Grabe, Timo A. Lakka, Terho Lehtimäki, Kari E. North, Brenda W. J. H. Penninx, Peter Vollenweider, Lynne E. Wagenknecht, Tangchun Wu, Yong-Bing Xiang, Wei Zheng, Donna K. Arnett, Claude Bouchard, Michele K. Evans, Vilmundur Gudnason, Sharon Kardia, Tanika N. Kelly, Stephen B. Kritchevsky, Ruth J. F. Loos, Alexandre C. Pereira, Mike Province, Bruce M. Psaty, Charles Rotimi, Xiaofeng Zhu, Najaf Amin, L. Adrienne Cupples, Myriam Fornage, Ervin F. Fox, Xiuqing Guo, W. James Gauderman, Kenneth Rice, Charles Kooperberg, Patricia B. Munroe, Ching-Ti Liu, Alanna C. Morrison, Dabeeru C. Rao, Diana van Heemst, and Susan Redline
Subjects: Science
Abstract: Sleep duration is associated with an adverse lipid profile. Here, the authors perform genome-wide gene-by-sleep interaction analysis and find 49 previously unreported lipid loci when considering short or long total sleep time.
Published: 2019
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19. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

Author: Tuomas O. Kilpeläinen, Amy R. Bentley, Raymond Noordam, Yun Ju Sung, Karen Schwander, Thomas W. Winkler, Hermina Jakupović, Daniel I. Chasman, Alisa Manning, Ioanna Ntalla, Hugues Aschard, Michael R. Brown, Lisa de las Fuentes, Nora Franceschini, Xiuqing Guo, Dina Vojinovic, Stella Aslibekyan, Mary F. Feitosa, Minjung Kho, Solomon K. Musani, Melissa Richard, Heming Wang, Zhe Wang, Traci M. Bartz, Lawrence F. Bielak, Archie Campbell, Rajkumar Dorajoo, Virginia Fisher, Fernando P. Hartwig, Andrea R. V. R. Horimoto, Changwei Li, Kurt K. Lohman, Jonathan Marten, Xueling Sim, Albert V. Smith, Salman M. Tajuddin, Maris Alver, Marzyeh Amini, Mathilde Boissel, Jin Fang Chai, Xu Chen, Jasmin Divers, Evangelos Evangelou, Chuan Gao, Mariaelisa Graff, Sarah E. Harris, Meian He, Fang-Chi Hsu, Anne U. Jackson, Jing Hua Zhao, Aldi T. Kraja, Brigitte Kühnel, Federica Laguzzi, Leo-Pekka Lyytikäinen, Ilja M. Nolte, Rainer Rauramaa, Muhammad Riaz, Antonietta Robino, Rico Rueedi, Heather M. Stringham, Fumihiko Takeuchi, Peter J. van der Most, Tibor V. Varga, Niek Verweij, Erin B. Ware, Wanqing Wen, Xiaoyin Li, Lisa R. Yanek, Najaf Amin, Donna K. Arnett, Eric Boerwinkle, Marco Brumat, Brian Cade, Mickaël Canouil, Yii-Der Ida Chen, Maria Pina Concas, John Connell, Renée de Mutsert, H. Janaka de Silva, Paul S. de Vries, Ayşe Demirkan, Jingzhong Ding, Charles B. Eaton, Jessica D. Faul, Yechiel Friedlander, Kelley P. Gabriel, Mohsen Ghanbari, Franco Giulianini, Chi Charles Gu, Dongfeng Gu, Tamara B. Harris, Jiang He, Sami Heikkinen, Chew-Kiat Heng, Steven C. Hunt, M. Arfan Ikram, Jost B. Jonas, Woon-Puay Koh, Pirjo Komulainen, Jose E. Krieger, Stephen B. Kritchevsky, Zoltán Kutalik, Johanna Kuusisto, Carl D. Langefeld, Claudia Langenberg, Lenore J. Launer, Karin Leander, Rozenn N. Lemaitre, Cora E. Lewis, Jingjing Liang, Lifelines Cohort Study, Jianjun Liu, Reedik Mägi, Ani Manichaikul, Thomas Meitinger, Andres Metspalu, Yuri Milaneschi, Karen L. Mohlke, Thomas H. Mosley, Alison D. Murray, Mike A. Nalls, Ei-Ei Khaing Nang, Christopher P. Nelson, Sotoodehnia Nona, Jill M. Norris, Chiamaka Vivian Nwuba, Jeff O’Connell, Nicholette D. Palmer, George J. Papanicolau, Raha Pazoki, Nancy L. Pedersen, Annette Peters, Patricia A. Peyser, Ozren Polasek, David J. Porteous, Alaitz Poveda, Olli T. Raitakari, Stephen S. Rich, Neil Risch, Jennifer G. Robinson, Lynda M. Rose, Igor Rudan, Pamela J. Schreiner, Robert A. Scott, Stephen S. Sidney, Mario Sims, Jennifer A. Smith, Harold Snieder, Tamar Sofer, John M. Starr, Barbara Sternfeld, Konstantin Strauch, Hua Tang, Kent D. Taylor, Michael Y. Tsai, Jaakko Tuomilehto, André G. Uitterlinden, M. Yldau van der Ende, Diana van Heemst, Trudy Voortman, Melanie Waldenberger, Patrik Wennberg, Gregory Wilson, Yong-Bing Xiang, Jie Yao, Caizheng Yu, Jian-Min Yuan, Wei Zhao, Alan B. Zonderman, Diane M. Becker, Michael Boehnke, Donald W. Bowden, Ulf de Faire, Ian J. Deary, Paul Elliott, Tõnu Esko, Barry I. Freedman, Philippe Froguel, Paolo Gasparini, Christian Gieger, Norihiro Kato, Markku Laakso, Timo A. Lakka, Terho Lehtimäki, Patrik K. E. Magnusson, Albertine J. Oldehinkel, Brenda W. J. H. Penninx, Nilesh J. Samani, Xiao-Ou Shu, Pim van der Harst, Jana V. Van Vliet-Ostaptchouk, Peter Vollenweider, Lynne E. Wagenknecht, Ya X. Wang, Nicholas J. Wareham, David R. Weir, Tangchun Wu, Wei Zheng, Xiaofeng Zhu, Michele K. Evans, Paul W. Franks, Vilmundur Gudnason, Caroline Hayward, Bernardo L. Horta, Tanika N. Kelly, Yongmei Liu, Kari E. North, Alexandre C. Pereira, Paul M. Ridker, E. Shyong Tai, Rob M. van Dam, Ervin R. Fox, Sharon L. R. Kardia, Ching-Ti Liu, Dennis O. Mook-Kanamori, Michael A. Province, Susan Redline, Cornelia M. van Duijn, Jerome I. Rotter, Charles B. Kooperberg, W. James Gauderman, Bruce M. Psaty, Kenneth Rice, Patricia B. Munroe, Myriam Fornage, L. Adrienne Cupples, Charles N. Rotimi, Alanna C. Morrison, Dabeeru C. Rao, and Ruth J. F. Loos
Subjects: Science
Abstract: GWAS have identified more than 500 genetic loci associated with blood lipid levels. Here, the authors report a genome-wide analysis of interactions between genetic markers and physical activity, and find that physical activity modifies the effects of four genetic loci on HDL or LDL cholesterol.
Published: 2019
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20. GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes

Author: Nora Franceschini, Claudia Giambartolomei, Paul S. de Vries, Chris Finan, Joshua C. Bis, Rachael P. Huntley, Ruth C. Lovering, Salman M. Tajuddin, Thomas W. Winkler, Misa Graff, Maryam Kavousi, Caroline Dale, Albert V. Smith, Edith Hofer, Elisabeth M. van Leeuwen, Ilja M. Nolte, Lingyi Lu, Markus Scholz, Muralidharan Sargurupremraj, Niina Pitkänen, Oscar Franzén, Peter K. Joshi, Raymond Noordam, Riccardo E. Marioni, Shih-Jen Hwang, Solomon K. Musani, Ulf Schminke, Walter Palmas, Aaron Isaacs, Adolfo Correa, Alan B. Zonderman, Albert Hofman, Alexander Teumer, Amanda J. Cox, André G. Uitterlinden, Andrew Wong, Andries J. Smit, Anne B. Newman, Annie Britton, Arno Ruusalepp, Bengt Sennblad, Bo Hedblad, Bogdan Pasaniuc, Brenda W. Penninx, Carl D. Langefeld, Christina L. Wassel, Christophe Tzourio, Cristiano Fava, Damiano Baldassarre, Daniel H. O’Leary, Daniel Teupser, Diana Kuh, Elena Tremoli, Elmo Mannarino, Enzo Grossi, Eric Boerwinkle, Eric E. Schadt, Erik Ingelsson, Fabrizio Veglia, Fernando Rivadeneira, Frank Beutner, Ganesh Chauhan, Gerardo Heiss, Harold Snieder, Harry Campbell, Henry Völzke, Hugh S. Markus, Ian J. Deary, J. Wouter Jukema, Jacqueline de Graaf, Jacqueline Price, Janne Pott, Jemma C. Hopewell, Jingjing Liang, Joachim Thiery, Jorgen Engmann, Karl Gertow, Kenneth Rice, Kent D. Taylor, Klodian Dhana, Lambertus A. L. M. Kiemeney, Lars Lind, Laura M. Raffield, Lenore J. Launer, Lesca M. Holdt, Marcus Dörr, Martin Dichgans, Matthew Traylor, Matthias Sitzer, Meena Kumari, Mika Kivimaki, Mike A. Nalls, Olle Melander, Olli Raitakari, Oscar H. Franco, Oscar L. Rueda-Ochoa, Panos Roussos, Peter H. Whincup, Philippe Amouyel, Philippe Giral, Pramod Anugu, Quenna Wong, Rainer Malik, Rainer Rauramaa, Ralph Burkhardt, Rebecca Hardy, Reinhold Schmidt, Renée de Mutsert, Richard W. Morris, Rona J. Strawbridge, S. Goya Wannamethee, Sara Hägg, Sonia Shah, Stela McLachlan, Stella Trompet, Sudha Seshadri, Sudhir Kurl, Susan R. Heckbert, Susan Ring, Tamara B. Harris, Terho Lehtimäki, Tessel E. Galesloot, Tina Shah, Ulf de Faire, Vincent Plagnol, Wayne D. Rosamond, Wendy Post, Xiaofeng Zhu, Xiaoling Zhang, Xiuqing Guo, Yasaman Saba, MEGASTROKE Consortium, Abbas Dehghan, Adrie Seldenrijk, Alanna C. Morrison, Anders Hamsten, Bruce M. Psaty, Cornelia M. van Duijn, Deborah A. Lawlor, Dennis O. Mook-Kanamori, Donald W. Bowden, Helena Schmidt, James F. Wilson, James G. Wilson, Jerome I. Rotter, Joanna M. Wardlaw, John Deanfield, Julian Halcox, Leo-Pekka Lyytikäinen, Markus Loeffler, Michele K. Evans, Stéphanie Debette, Steve E. Humphries, Uwe Völker, Vilmundur Gudnason, Aroon D. Hingorani, Johan L. M. Björkegren, Juan P. Casas, and Christopher J. O’Donnell
Subjects: Science
Abstract: Carotid intima-media thickness (cIMT) and plaque are associated with subclinical atherosclerosis and coronary heart disease (CHD). Here, the authors identify and prioritize genetic loci for cIMT and plaque by GWAS and colocalization approaches and further demonstrate genetic correlation with CHD and stroke.
Published: 2018
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21. Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Author: Daokun Sun, Melissa A. Richard, Solomon K. Musani, Yun Ju Sung, Thomas W. Winkler, Karen Schwander, Jin Fang Chai, Xiuqing Guo, Tuomas O. Kilpeläinen, Dina Vojinovic, Hugues Aschard, Traci M. Bartz, Lawrence F. Bielak, Michael R. Brown, Kumaraswamy Chitrala, Fernando P. Hartwig, Andrea R.V.R. Horimoto, Yongmei Liu, Alisa K. Manning, Raymond Noordam, Albert V. Smith, Sarah E. Harris, Brigitte Kühnel, Leo-Pekka Lyytikäinen, Ilja M. Nolte, Rainer Rauramaa, Peter J. van der Most, Rujia Wang, Erin B. Ware, Stefan Weiss, Wanqing Wen, Lisa R. Yanek, Dan E. Arking, Donna K. Arnett, Ana Barac, Eric Boerwinkle, Ulrich Broeckel, Aravinda Chakravarti, Yii-Der Ida Chen, L. Adrienne Cupples, Martha L. Davigulus, Lisa de las Fuentes, Renée de Mutsert, Paul S. de Vries, Joseph A.C. Delaney, Ana V. Diez Roux, Marcus Dörr, Jessica D. Faul, Amanda M. Fretts, Linda C. Gallo, Hans Jörgen Grabe, C. Charles Gu, Tamara B. Harris, Catharina C.A. Hartman, Sami Heikkinen, M. Arfan Ikram, Carmen Isasi, W. Craig Johnson, Jost Bruno Jonas, Robert C. Kaplan, Pirjo Komulainen, Jose E. Krieger, Daniel Levy, Jianjun Liu, Kurt Lohman, Annemarie I. Luik, Lisa W. Martin, Thomas Meitinger, Yuri Milaneschi, Jeff R. O’Connell, Walter R. Palmas, Annette Peters, Patricia A. Peyser, Laura Pulkki-Råback, Leslie J. Raffel, Alex P. Reiner, Kenneth Rice, Jennifer G. Robinson, Frits R. Rosendaal, Carsten Oliver Schmidt, Pamela J. Schreiner, Lars Schwettmann, James M. Shikany, Xiao-ou Shu, Stephen Sidney, Mario Sims, Jennifer A. Smith, Nona Sotoodehnia, Konstantin Strauch, E. Shyong Tai, Kent D. Taylor, André G. Uitterlinden, Cornelia M. van Duijn, Melanie Waldenberger, Hwee-Lin Wee, Wen-Bin Wei, Gregory Wilson, Deng Xuan, Jie Yao, Donglin Zeng, Wei Zhao, Xiaofeng Zhu, Alan B. Zonderman, Diane M. Becker, Ian J. Deary, Christian Gieger, Timo A. Lakka, Terho Lehtimäki, Kari E. North, Albertine J. Oldehinkel, Brenda W.J.H. Penninx, Harold Snieder, Ya-Xing Wang, David R. Weir, Wei Zheng, Michele K. Evans, W. James Gauderman, Vilmundur Gudnason, Bernardo L. Horta, Ching-Ti Liu, Dennis O. Mook-Kanamori, Alanna C. Morrison, Alexandre C. Pereira, Bruce M. Psaty, Najaf Amin, Ervin R. Fox, Charles Kooperberg, Xueling Sim, Laura Bierut, Jerome I. Rotter, Sharon L.R. Kardia, Nora Franceschini, Dabeeru C. Rao, and Myriam Fornage
Subjects: gene-environment interaction, genome-wide association study, blood pressure, hypertension, psychosocial factors, depressive symptoms, Genetics, QH426-470
Abstract: Summary: 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 five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value < 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations.
Published: 2021
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22. GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk

Author: Nicola Pirastu, Peter K. Joshi, Paul S. de Vries, Marilyn C. Cornelis, Paul M. McKeigue, NaNa Keum, Nora Franceschini, Marco Colombo, Edward L. Giovannucci, Athina Spiliopoulou, Lude Franke, Kari E. North, Peter Kraft, Alanna C. Morrison, Tõnu Esko, and James F. Wilson
Subjects: Science
Abstract: Male pattern baldness (MBP) is a complex trait that has genetic associations. Here, Pirastu and colleagues perform a genome-wide association study to show 71 susceptibility loci associated with MBP — 30 of which are novel — and that these loci can explain 38% of heritability.
Published: 2017
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23. Reply to ‘Misestimation of heritability and prediction accuracy of male-pattern baldness’

Author: Nicola Pirastu, Peter K. Joshi, Paul S. de Vries, Marilyn C. Cornelis, NaNa Keum, Nora Franceschini, Marco Colombo, Edward L. Giovannucci, Athina Spiliopoulou, Lude Franke, Kari E. North, Peter Kraft, Alanna C. Morrison, Tõnu Esko, and James F. Wilson
Subjects: Science
Published: 2018
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24. Author Correction: GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk

Author: Nicola Pirastu, Peter K. Joshi, Paul S. de Vries, Marilyn C. Cornelis, Paul M. McKeigue, NaNa Keum, Nora Franceschini, Marco Colombo, Edward L. Giovannucci, Athina Spiliopoulou, Lude Franke, Kari E. North, Peter Kraft, Alanna C. Morrison, Tõnu Esko, and James F. Wilson
Subjects: Science
Abstract: We have been alerted that in our recent Article the calculations used to transform the heritability from the observed scale to the liability scale did not take into account the individuals in category 2 of the baldness scale, who were removed in our original analysis. This led to an overestimation of the heritability on the liability scale, which should have been 0.62 instead of 0.94. Moreover, in the Title and in the Abstract, we report that we can explain 38% of the risk, while in fact that is the proportion of heritability explained by the loci we discovered. These errors do not substantially change the paper or its conclusions apart from the statement MBP is therefore probably one of the most heritable complex traits. Genome-wide significant associations and pathway analyses are not affected in any way and male-pattern baldness remains less genetically complex than other complex traits. We wish to thank Yap et al. for bringing this to our attention.
Published: 2018
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25. DNA methylation analysis is used to identify novel genetic loci associated with circulating fibrinogen levels in blood

Author: Julie Hahn, Jan Bressler, Arce Domingo-Relloso, Ming-Huei Chen, Daniel L. McCartney, Alexander Teumer, Jenny van Dongen, Marcus E. Kleber, Dylan Aïssi, Brenton R. Swenson, Jie Yao, Wei Zhao, Jian Huang, Yujing Xia, Michael R. Brown, Ricardo Costeira, Eco J.C. de Geus, Graciela E. Delgado, Dre'Von A. Dobson, Paul Elliott, Hans J. Grabe, Xiuqing Guo, Sarah E. Harris, Jennifer E. Huffman, Sharon L.R. Kardia, Yongmei Liu, Stefan Lorkowski, Riccardo E. Marioni, Matthias Nauck, Scott M. Ratliff, Maria Sabater-Lleal, Tim D. Spector, Pierre Suchon, Kent D. Taylor, Florian Thibord, David-Alexandre Trégouët, Kerri L. Wiggins, Gonneke Willemsen, Jordana T. Bell, Dorret I. Boomsma, Shelley A. Cole, Simon R. Cox, Abbas Dehghan, Andreas Greinacher, Karin Haack, Winfried März, Pierre-Emmanuel Morange, Jerome I. Rotter, Nona Sotoodehnia, Maria Tellez-Plaza, Ana Navas-Acien, Jennifer A. Smith, Andrew D. Johnson, Myriam Fornage, Nicholas L. Smith, Alisa S. Wolberg, Alanna C. Morrison, Paul S. de Vries, Biological Psychology, Amsterdam Reproduction & Development, APH - Mental Health, APH - Personalized Medicine, AMS - Ageing & Vitality, AMS - Sports, APH - Health Behaviors & Chronic Diseases, and APH - Methodology
Subjects: DNA methylation, Hematology, genetics [Fibrinogen], methods [Genome-Wide Association Study], epigenome-wide association study, Epigenesis, Genetic, genetics [Inflammation], SDG 3 - Good Health and Well-being, Genetic Loci, inflammation, Mendelian randomization, Humans, CpG Islands, ddc:610, fibrinogen
Abstract: Background: Fibrinogen plays an essential role in blood coagulation and inflammation. Circulating fibrinogen levels may be determined based on interindividual differences in DNA methylation at cytosine-phosphate-guanine (CpG) sites and vice versa. Objectives: To perform an EWAS to examine an association between blood DNA methylation levels and circulating fibrinogen levels to better understand its biological and pathophysiological actions. Methods: We performed an epigenome-wide association study of circulating fibrinogen levels in 18 037 White, Black, American Indian, and Hispanic participants, representing 14 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium. Circulating leukocyte DNA methylation was measured using the Illumina 450K array in 12 904 participants and using the EPIC array in 5133 participants. In each study, an epigenome-wide association study of fibrinogen was performed using linear mixed models adjusted for potential confounders. Study-specific results were combined using array-specific meta-analysis, followed by cross-replication of epigenome-wide significant associations. We compared models with and without CRP adjustment to examine the role of inflammation. Results: We identified 208 and 87 significant CpG sites associated with fibrinogen levels from the 450K (p < 1.03 × 10−7) and EPIC arrays (p < 5.78 × 10−8), respectively. There were 78 associations from the 450K array that replicated in the EPIC array and 26 vice versa. After accounting for overlapping sites, there were 83 replicated CpG sites located in 61 loci, of which only 4 have been previously reported for fibrinogen. The examples of genes located near these CpG sites were SOCS3 and AIM2, which are involved in inflammatory pathways. The associations of all 83 replicated CpG sites were attenuated after CRP adjustment, although many remained significant. Conclusion: We identified 83 CpG sites associated with circulating fibrinogen levels. These associations are partially driven by inflammatory pathways shared by both fibrinogen and CRP.
Published: 2023

26. Novel genetic regulators of fibrinogen synthesis identified by an in vitro experimental platform

Author: Dre’Von A. Dobson, Lori A. Holle, Feng-Chang Lin, Jennifer E. Huffman, James P. Luyendyk, Matthew J. Flick, Nicholas L. Smith, Paul S. de Vries, Alanna C. Morrison, and Alisa S. Wolberg
Subjects: Hematology
Published: 2023

27. Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors

Author: Florian, Thibord, Derek, Klarin, Jennifer A, Brody, Ming-Huei, Chen, Michael G, Levin, Daniel I, Chasman, Ellen L, Goode, Kristian, Hveem, Maris, Teder-Laving, Angel, Martinez-Perez, Dylan, Aïssi, Delphine, Daian-Bacq, Kaoru, Ito, Pradeep, Natarajan, Pamela L, Lutsey, Girish N, Nadkarni, Paul S, de Vries, Gabriel, Cuellar-Partida, Brooke N, Wolford, Jack W, Pattee, Charles, Kooperberg, Sigrid K, Braekkan, Ruifang, Li-Gao, Noemie, Saut, Corriene, Sept, Marine, Germain, Renae L, Judy, Kerri L, Wiggins, Darae, Ko, Christopher J, O'Donnell, Kent D, Taylor, Franco, Giulianini, Mariza, De Andrade, Therese H, Nøst, Anne, Boland, Jean-Philippe, Empana, Satoshi, Koyama, Thomas, Gilliland, Ron, Do, Jennifer E, Huffman, Xin, Wang, Wei, Zhou, Jose, Manuel Soria, Juan, Carlos Souto, Nathan, Pankratz, Jeffery, Haessler, Kristian, Hindberg, Frits R, Rosendaal, Constance, Turman, Robert, Olaso, Rachel L, Kember, Traci M, Bartz, Julie A, Lynch, Susan R, Heckbert, Sebastian M, Armasu, Ben, Brumpton, David M, Smadja, Xavier, Jouven, Issei, Komuro, Katharine R, Clapham, Ruth J F, Loos, Cristen J, Willer, Maria, Sabater-Lleal, James S, Pankow, Alexander P, Reiner, Vania M, Morelli, Paul M, Ridker, Astrid van Hylckama, Vlieg, Jean-François, Deleuze, Peter, Kraft, Daniel J, Rader, Kyung, Min Lee, Bruce M, Psaty, Anne, Heidi Skogholt, Joseph, Emmerich, Pierre, Suchon, Stephen S, Rich, Ha My T, Vy, Weihong, Tang, Rebecca D, Jackson, John-Bjarne, Hansen, Pierre-Emmanuel, Morange, Christopher, Kabrhel, David-Alexandre, Trégouët, Scott M, Damrauer, Andrew D, Johnson, Nicholas L, Smith, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), and Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: genome-wide association study, venous thromboembolism, Quantitative Trait Loci, Thrombosis, Genomics, Venous Thromboembolism, Polymorphism, Single Nucleotide, meta-analysis, Meta-analysis, Physiology (medical), Venous thrombosis, Genetics, Humans, genetics, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Genetic Predisposition to Disease, venous thrombosis, Cardiology and Cardiovascular Medicine, Genome-Wide Association Study
Abstract: Background: Venous thromboembolism (VTE) is a life-threatening vascular event with environmental and genetic determinants. Recent VTE genome-wide association studies (GWAS) meta-analyses involved nearly 30 000 VTE cases and identified up to 40 genetic loci associated with VTE risk, including loci not previously suspected to play a role in hemostasis. The aim of our research was to expand discovery of new genetic loci associated with VTE by using cross-ancestry genomic resources. Methods: We present new cross-ancestry meta-analyzed GWAS results involving up to 81 669 VTE cases from 30 studies, with replication of novel loci in independent populations and loci characterization through in silico genomic interrogations. Results: In our genetic discovery effort that included 55 330 participants with VTE (47 822 European, 6320 African, and 1188 Hispanic ancestry), we identified 48 novel associations, of which 34 were replicated after correction for multiple testing. In our combined discovery-replication analysis (81 669 VTE participants) and ancestry-stratified meta-analyses (European, African, and Hispanic), we identified another 44 novel associations, which are new candidate VTE-associated loci requiring replication. In total, across all GWAS meta-analyses, we identified 135 independent genomic loci significantly associated with VTE risk. A genetic risk score of the significantly associated loci in Europeans identified a 6-fold increase in risk for those in the top 1% of scores compared with those with average scores. We also identified 31 novel transcript associations in transcriptome-wide association studies and 8 novel candidate genes with protein quantitative-trait locus Mendelian randomization analyses. In silico interrogations of hemostasis and hematology traits and a large phenome-wide association analysis of the 135 GWAS loci provided insights to biological pathways contributing to VTE, with some loci contributing to VTE through well-characterized coagulation pathways and others providing new data on the role of hematology traits, particularly platelet function. Many of the replicated loci are outside of known or currently hypothesized pathways to thrombosis. Conclusions: Our cross-ancestry GWAS meta-analyses identified new loci associated with VTE. These findings highlight new pathways to thrombosis and provide novel molecules that may be useful in the development of improved antithrombosis treatments.
Published: 2022

28. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

Author: Tanika N, Kelly, Xiao, Sun, Karen Y, He, Michael R, Brown, Sarah A Gagliano, Taliun, Jacklyn N, Hellwege, Marguerite R, Irvin, Xuenan, Mi, Jennifer A, Brody, Nora, Franceschini, Xiuqing, Guo, Shih-Jen, Hwang, Paul S, de Vries, Yan, Gao, Arden, Moscati, Girish N, Nadkarni, Lisa R, Yanek, Tali, Elfassy, Jennifer A, Smith, Ren-Hua, Chung, Amber L, Beitelshees, Amit, Patki, Stella, Aslibekyan, Brandon M, Blobner, Juan M, Peralta, Themistocles L, Assimes, Walter R, Palmas, Chunyu, Liu, Adam P, Bress, Zhijie, Huang, Lewis C, Becker, Chii-Min, Hwa, Jeffrey R, O'Connell, Jenna C, Carlson, Helen R, Warren, Sayantan, Das, Ayush, Giri, Lisa W, Martin, W, Craig Johnson, Ervin R, Fox, Erwin P, Bottinger, Alexander C, Razavi, Dhananjay, Vaidya, Lee-Ming, Chuang, Yen-Pei C, Chang, Take, Naseri, Deepti, Jain, Hyun Min, Kang, Adriana M, Hung, Vinodh, Srinivasasainagendra, Beverly M, Snively, Dongfeng, Gu, May E, Montasser, Muagututi'a Sefuiva, Reupena, Benjamin D, Heavner, Jonathon, LeFaive, James E, Hixson, Kenneth M, Rice, Fei Fei, Wang, Jonas B, Nielsen, Jianfeng, Huang, Alyna T, Khan, Wei, Zhou, Jovia L, Nierenberg, Cathy C, Laurie, Nicole D, Armstrong, Mengyao, Shi, Yang, Pan, Adrienne M, Stilp, Leslie, Emery, Quenna, Wong, Nicola L, Hawley, Ryan L, Minster, Joanne E, Curran, Patricia B, Munroe, Daniel E, Weeks, Kari E, North, Russell P, Tracy, Eimear E, Kenny, Daichi, Shimbo, Aravinda, Chakravarti, Stephen S, Rich, Alex P, Reiner, John, Blangero, Susan, Redline, Braxton D, Mitchell, Dabeeru C, Rao, Yii-Der, Ida Chen, Sharon L R, Kardia, Robert C, Kaplan, Rasika A, Mathias, Jiang, He, Bruce M, Psaty, Myriam, Fornage, Ruth J F, Loos, Adolfo, Correa, Eric, Boerwinkle, Jerome I, Rotter, Charles, Kooperberg, Todd L, Edwards, Gonçalo R, Abecasis, Xiaofeng, Zhu, Daniel, Levy, Donna K, Arnett, and Alanna C, Morrison
Subjects: Hypertension, Internal Medicine, Humans, Blood Pressure, Genomics, Precision Medicine, Polymorphism, Single Nucleotide, Genome-Wide Association Study
Abstract: Background: The availability of whole-genome sequencing data in large studies has enabled the assessment of coding and noncoding variants across the allele frequency spectrum for their associations with blood pressure. Methods: We conducted a multiancestry whole-genome sequencing analysis of blood pressure among 51 456 Trans-Omics for Precision Medicine and Centers for Common Disease Genomics program participants (stage-1). Stage-2 analyses leveraged array data from UK Biobank (N=383 145), Million Veteran Program (N=318 891), and Reasons for Geographic and Racial Differences in Stroke (N=10 643) participants, along with whole-exome sequencing data from UK Biobank (N=199 631) participants. Results: Two blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings ( P -8 ). Among them, a rare intergenic variant at novel locus, LOC100506274 , was associated with lower systolic blood pressure in stage-1 (beta [SE]=−32.6 [6.0]; P =4.99×10 -8 ) but not stage-2 analysis ( P =0.11). Furthermore, a novel common variant at the known INSR locus was suggestively associated with diastolic blood pressure in stage-1 (beta [SE]=−0.36 [0.07]; P =4.18×10 -7 ) and attained genome-wide significance in stage-2 (beta [SE]=−0.29 [0.03]; P =7.28×10 -23 ). Nineteen additional signals suggestively associated with blood pressure in meta-analysis of single and aggregate rare variant findings ( P -6 and P -4 , respectively). Discussion: We report one promising but unconfirmed rare variant for blood pressure and, more importantly, contribute insights for future blood pressure sequencing studies. Our findings suggest promise of aggregate analyses to complement single variant analysis strategies and the need for larger, diverse samples, and family studies to enable robust rare variant identification.
Published: 2023

29. Polygenic risk, lifestyle, and the lifetime risk of coronary artery disease

Author: Paul S de Vries
Subjects: Cardiology and Cardiovascular Medicine, Article
Published: 2023

30. Lowering of circulating sclerostin may increase risk of atherosclerosis and its risk factors: evidence from a genome‐wide association meta‐analysis followed by Mendelian randomization

Author: Jie Zheng, Eleanor Wheeler, Maik Pietzner, Till F.M. Andlauer, Michelle S. Yau, April E. Hartley, Ben Michael Brumpton, Humaira Rasheed, John P Kemp, Monika Frysz, Jamie Robinson, Sjur Reppe, Vid Prijatelj, Kaare M. Gautvik, Louise Falk, Winfried Maerz, Ingrid Gergei, Patricia A Peyser, Maryam Kavousi, Paul S. de Vries, Clint L. Miller, Maxime Bos, Sander W. van der Laan, Rajeev Malhotra, Markus Herrmann, Hubert Scharnagl, Marcus Kleber, George Dedoussis, Eleftheria Zeggini, Maria Nethander, Claes Ohlsson, Mattias Lorentzon, Nick Wareham, Claudia Langenberg, Michael V. Holmes, George Davey Smith, and Jonathan H. Tobias
Subjects: Rheumatology, Immunology, Immunology and Allergy
Published: 2023

31. Whole-exome sequencing of 14 389 individuals from the ESP and CHARGE consortia identifies novel rare variation associated with hemostatic factors

Author: Nathan Pankratz, Peng Wei, Jennifer A Brody, Ming-Huei Chen, Paul S de Vries, Jennifer E Huffman, Mary Rachel Stimson, Paul L Auer, Eric Boerwinkle, Mary Cushman, Moniek P M de Maat, Aaron R Folsom, Oscar H Franco, Richard A Gibbs, Kelly K Haagenson, Albert Hofman, Jill M Johnsen, Christie L Kovar, Robert Kraaij, Barbara McKnight, Ginger A Metcalf, Donna Muzny, Bruce M Psaty, Weihong Tang, André G Uitterlinden, Jeroen G J van Rooij, Abbas Dehghan, Christopher J O'Donnell, Alex P Reiner, Alanna C Morrison, and Nicholas L Smith
Subjects: Factor VIII, Exome Sequencing, von Willebrand Factor, Genetics, Fibrinogen, Humans, Original Article, General Medicine, Factor VII, Polymorphism, Single Nucleotide, Molecular Biology, Hemostatics, Genetics (clinical)
Abstract: Plasma levels of fibrinogen, coagulation factors VII and VIII and von Willebrand factor (vWF) are four intermediate phenotypes that are heritable and have been associated with the risk of clinical thrombotic events. To identify rare and low-frequency variants associated with these hemostatic factors, we conducted whole-exome sequencing in 10 860 individuals of European ancestry (EA) and 3529 African Americans (AAs) from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and the National Heart, Lung and Blood Institute’s Exome Sequencing Project. Gene-based tests demonstrated significant associations with rare variation (minor allele frequency
Published: 2022

32. Associations of carotid intima media thickness with gene expression in whole blood and genetically predicted gene expression across 48 tissues

Author: Joshua C. Bis, Holger Kirsten, Wolfgang Rathmann, Albert Hofman, Shih-Jen Hwang, Stefan Weiss, Annette Peters, Oscar H. Franco, Klodian Dhana, Brenda W J H Penninx, Katharina Schramm, Christian Herder, Joseph F. Polak, Adrie Seldenrijk, Wolfgang Koenig, Markus Loeffler, Marcus Dörr, Andy B Castaneda, Petra Wolf, Frank Beutner, Christopher J. O’Donnell, Xiaoling Zhang, Jochen Seissler, Christa Meisinger, Joachim Thiery, Holger Prokisch, Rick Jansen, Joyce B. J. van Meurs, André G. Uitterlinden, Nora Franceshini, Jennifer E. Below, Georg Homuth, Paul S. de Vries, Daniel Levy, Marjolein J. Peters, Cohorts for Heart, Gerard van Grootheest, Abbas Dehghan, Markus Scholz, Ulf Schminke, Carola Marzi, Melanie Waldenberger, Knut Krohn, Claudia Giambartolomei, Lauren E. Petty, Maryam Kavousi, Henry Völzke, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Digital Health, Department of Marketing Management, Urology, Internal Medicine, and Epidemiology
Subjects: Oncology, Candidate gene, medicine.medical_specialty, Gene Expression, Biology, Carotid Intima-Media Thickness, Risk Factors, Internal medicine, Gene expression, Genetics, medicine, Humans, cardiovascular diseases, Association Studies Article, 610 Medicine & health, Molecular Biology, Gene, Genetics (clinical), Confounding, General Medicine, Atherosclerosis, Phenotype, Intima-media thickness, cardiovascular system, Biomarker (medicine), DNA microarray, 360 Social problems & social services, Genome-Wide Association Study
Abstract: Carotid intima media thickness (cIMT) is a biomarker of subclinical atherosclerosis and a predictor of future cardiovascular events. Identifying associations between gene expression levels and cIMT may provide insight to atherosclerosis etiology. Here, we use two approaches to identify associations between mRNA levels and cIMT: differential gene expression analysis in whole blood and S-PrediXcan. We used microarrays to measure genome-wide whole blood mRNA levels of 5647 European individuals from four studies. We examined the association of mRNA levels with cIMT adjusted for various potential confounders. Significant associations were tested for replication in three studies totaling 3943 participants. Next, we applied S-PrediXcan to summary statistics from a cIMT genome-wide association study (GWAS) of 71 128 individuals to estimate the association between genetically determined mRNA levels and cIMT and replicated these analyses using S-PrediXcan on an independent GWAS on cIMT that included 22 179 individuals from the UK Biobank. mRNA levels of TNFAIP3, CEBPD and METRNL were inversely associated with cIMT, but these associations were not significant in the replication analysis. S-PrediXcan identified associations between cIMT and genetically determined mRNA levels for 36 genes, of which six were significant in the replication analysis, including TLN2, which had not been previously reported for cIMT. There was weak correlation between our results using differential gene expression analysis and S-PrediXcan. Differential expression analysis and S-PrediXcan represent complementary approaches for the discovery of associations between phenotypes and gene expression. Using these approaches, we prioritize TNFAIP3, CEBPD, METRNL and TLN2 as new candidate genes whose differential expression might modulate cIMT.
Published: 2022

33. American Heart Association’s Life’s Simple 7: Lifestyle Recommendations, Polygenic Risk, and Lifetime Risk of Coronary Heart Disease

Author: Natalie R. Hasbani, Symen Ligthart, Michael R. Brown, Adam S. Heath, Allison Bebo, Kellan E. Ashley, Eric Boerwinkle, Alanna C. Morrison, Aaron R. Folsom, David Aguilar, Paul S. de Vries, and Intensive Care
Subjects: Cohort Studies, SDG 3 - Good Health and Well-being, Cardiovascular Diseases, Risk Factors, Physiology (medical), Humans, Coronary Disease, Genetic Predisposition to Disease, American Heart Association, Cardiology and Cardiovascular Medicine, Life Style, United States
Abstract: Background: Understanding the effect of lifestyle and genetic risk on the lifetime risk of coronary heart disease (CHD) is important to improving public health initiatives. Our objective was to quantify remaining lifetime risk and years free of CHD according to polygenic risk and the American Heart Association’s Life’s Simple 7 (LS7) guidelines in a population-based cohort study. Methods: Our analysis included data from participants of the ARIC (Atherosclerosis Risk in Communities) study: 8372 White and 2314 Black participants; 45 years of age and older; and free of CHD at baseline examination. A polygenic risk score (PRS) comprised more than 6 million genetic variants was categorized into low (80th percentile). An overall LS7 score was calculated at baseline and categorized into “poor,” “intermediate,” and “ideal” cardiovascular health. Lifetime risk and CHD-free years were computed according to polygenic risk and LS7 categories. Results: The overall remaining lifetime risk was 27%, ranging from 16.6% in individuals with an ideal LS7 score to 43.1% for individuals with a poor LS7 score. The association of PRS with lifetime risk differed according to ancestry. In White participants, remaining lifetime risk ranged from 19.8% to 39.3% according to increasing PRS categories. Individuals with a high PRS and poor LS7 had a remaining lifetime risk of 67.1% and 15.9 fewer CHD-free years than did those with intermediate polygenic risk and LS7 scores. In the high-PRS group, ideal LS7 was associated with 20.2 more CHD-free years compared with poor LS7. In Black participants, remaining lifetime risk ranged from 19.1% to 28.6% according to increasing PRS category. Similar lifetime risk estimates were observed for individuals of poor LS7 regardless of PRS category. In the high-PRS group, an ideal LS7 score was associated with only 4.5 more CHD-free years compared with a poor LS7 score. Conclusions: Ideal adherence to LS7 recommendations was associated with lower lifetime risk of CHD for all individuals, especially in those with high genetic susceptibility. In Black participants, adherence to LS7 guidelines contributed to lifetime risk of CHD more so than current PRSs. Improved PRSs are needed to properly evaluate genetic susceptibility for CHD in diverse populations.
Published: 2022

34. Multi-phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

Author: Gerard Temprano‐Sagrera, Colleen M. Sitlani, William P. Bone, Miguel Martin‐Bornez, Benjamin F. Voight, Alanna C. Morrison, Scott M. Damrauer, Paul S. de Vries, Nicholas L. Smith, Maria Sabater‐Lleal, Abbas Dehghan, Adam S Heath, Alanna C Morrison, Alex P Reiner, Andrew Johnson, Anne Richmond, Annette Peters, Astrid van Hylckama Vlieg, Barbara McKnight, Bruce M Psaty, Caroline Hayward, Cavin Ward‐Caviness, Christopher O’Donnell, Daniel Chasman, David P Strachan, David A Tregouet, Dennis Mook‐Kanamori, Dipender Gill, Florian Thibord, Folkert W Asselbergs, Frank W.G. Leebeek, Frits R Rosendaal, Gail Davies, Georg Homuth, Gerard Temprano, Harry Campbell, Herman A Taylor, Jan Bressler, Jennifer E Huffman, Jerome I Rotter, Jie Yao, James F Wilson, Joshua C Bis, Julie M Hahn, Karl C Desch, Kerri L Wiggins, Laura M Raffield, Lawrence F Bielak, Lisa R Yanek, Marcus E Kleber, Martina Mueller, Maryam Kavousi, Massimo Mangino, Melissa Liu, Michael R Brown, Matthew P Conomos, Min‐A Jhun, Ming‐Huei Chen, Moniek P.M. de Maat, Nathan Pankratz, Nicholas L Smith, Patricia A Peyser, Paul Elliot, Paul S de Vries, Peng Wei, Philipp S Wild, Pierre E Morange, Pim van der Harst, Qiong Yang, Ngoc‐Quynh Le, Riccardo Marioni, Ruifang Li, Scott M Damrauer, Simon R Cox, Stella Trompet, Stephan B Felix, Uwe Völker, Weihong Tang, Wolfgang Koenig, J. Wouter Jukema, Xiuqing Guo, Sara Lindstrom, Lu Wang, Erin N Smith, William Gordon, Mariza de Andrade, Jennifer A Brody, Jack W Pattee, Jeffrey Haessler, Ben M Brumpton, Daniel I Chasman, Pierre Suchon, Constance Turman, Marine Germain, James MacDonald, Sigrid K Braekkan, Sebastian M Armasu, Rabecca D Jackson, Jonas B Nielsen, Franco Giulianini, Marja K Puurunen, Manal Ibrahim, Susan R Heckbert, Theo K Bammler, Kelly A Frazer, Bryan M McCauley, Kent Taylor, James S Pankow, Alexander P Reiner, Maiken E Gabrielsen, Jean‐François Deleuze, Chris J O’Donnell, Jihye Kim, Peter Kraft, John‐Bjarne Hansen, John A Heit, Charles Kooperberg, Kristian Hveem, Paul M Ridker, Pierre‐Emmanuel Morange, Andrew D Johnson, Christopher Kabrhel, David‐Alexandre Trégouët, Rainer Malik, Ganesh Chauhan, Matthew Traylor, Muralidharan Sargurupremraj, Yukinori Okada, Aniket Mishra, Loes Rutten‐Jacobs, Anne‐Katrin Giese, Sander W van der Laan, Solveig Gretarsdottir, Christopher D Anderson, Michael Chong, Hieab HH Adams, Tetsuro Ago, Peter Almgren, Philippe Amouyel, Hakan Ay, Traci M Bartz, Oscar R Benavente, Steve Bevan, Giorgio B Boncoraglio, Robert D Brown, Adam S Butterworth, Caty Carrera, Cara L Carty, Wei‐Min Chen, John W Cole, Adolfo Correa, Ioana Cotlarciuc, Carlos Cruchaga, John Danesh, Paul IW de Bakker, Anita L DeStefano, Marcel den Hoed, Qing Duan, Stefan T Engelter, Guido J Falcone, Rebecca F Gottesman, Raji P Grewal, Vilmundur Gudnason, Stefan Gustafsson, Tamara B Harris, Ahamad Hassan, Aki S Havulinna, Elizabeth G Holliday, George Howard, Fang‐Chi Hsu, Hyacinth I Hyacinth, M Arfan Ikram, Erik Ingelsson, Marguerite R Irvin, Xueqiu Jian, Jordi Jiménez‐Conde, Julie A Johnson, J Wouter Jukema, Masahiro Kanai, Keith L Keene, Brett M Kissela, Dawn O Kleindorfer, Michiaki Kubo, Leslie A Lange, Carl D Langefeld, Claudia Langenberg, Lenore J Launer, Jin‐Moo Lee, Robin Lemmens, Didier Leys, Cathryn M Lewis, Wei‐Yu Lin, Arne G Lindgren, Erik Lorentzen, Patrik K Magnusson, Jane Maguire, Ani Manichaikul, Patrick F McArdle, James F Meschia, Braxton D Mitchell, Thomas H Mosley, Michael A Nalls, Toshiharu Ninomiya, Martin J O’Donnell, Sara L Pulit, Kristiina Rannikmäe, Kathryn M Rexrode, Kenneth Rice, Stephen S Rich, Natalia S Rost, Peter M Rothwell, Tatjana Rundek, Ralph L Sacco, Saori Sakaue, Michele M Sale, Veikko Salomaa, Bishwa R Sapkota, Reinhold Schmidt, Carsten O Schmidt, Ulf Schminke, Pankaj Sharma, Agnieszka Slowik, Cathie LM Sudlow, Christian Tanislav, Turgut Tatlisumak, Kent D Taylor, Vincent NS Thijs, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Steffen Tiedt, Christophe Tzourio, Cornelia M van Duijn, Matthew Walters, Nicholas J Wareham, Sylvia Wassertheil‐Smoller, James G Wilson, Salim Yusuf, Najaf Amin, Hugo S Aparicio, Donna K Arnett, John Attia, Alexa S Beiser, Claudine Berr, Julie E Buring, Mariana Bustamante, Valeria Caso, Yu‐Ching Cheng, Seung Hoan Choi, Ayesha Chowhan, Natalia Cullell, Jean‐François Dartigues, Hossein Delavaran, Pilar Delgado, Marcus Dörr, Gunnar Engström, Ian Ford, Wander S Gurpreet, Anders Hamsten, Laura Heitsch, Atsushi Hozawa, Laura Ibanez, Andreea Ilinca, Martin Ingelsson, Motoki Iwasaki, Rebecca D Jackson, Katarina Jood, Pekka Jousilahti, Sara Kaffashian, Lalit Kalra, Masahiro Kamouchi, Takanari Kitazono, Olafur Kjartansson, Manja Kloss, Peter J Koudstaal, Jerzy Krupinski, Daniel L Labovitz, Cathy C Laurie, Christopher R Levi, Linxin Li, Lars Lind, Cecilia M Lindgren, Vasileios Lioutas, Yong Mei Liu, Oscar L Lopez, Hirata Makoto, Nicolas Martinez‐Majander, Koichi Matsuda, Naoko Minegishi, Joan Montaner, Andrew P Morris, Elena Muiño, Martina Müller‐Nurasyid, Bo Norrving, Soichi Ogishima, Eugenio A Parati, Leema Reddy Peddareddygari, Nancy L Pedersen, Joanna Pera, Markus Perola, Alessandro Pezzini, Silvana Pileggi, Raquel Rabionet, Iolanda Riba‐Llena, Marta Ribasés, Jose R Romero, Jaume Roquer, Anthony G Rudd, Antti‐Pekka Sarin, Ralhan Sarju, Chloe Sarnowski, Makoto Sasaki, Claudia L Satizabal, Mamoru Satoh, Naveed Sattar, Norie Sawada, Gerli Sibolt, Ásgeir Sigurdsson, Albert Smith, Kenji Sobue, Carolina Soriano‐Tárraga, Tara Stanne, O Colin Stine, David J Stott, Konstantin Strauch, Takako Takai, Hideo Tanaka, Kozo Tanno, Alexander Teumer, Liisa Tomppo, Nuria P Torres‐Aguila, Emmanuel Touze, Shoichiro Tsugane, Andre G Uitterlinden, Einar M Valdimarsson, Sven J van der Lee, Henry Völzke, Kenji Wakai, David Weir, Stephen R Williams, Charles DA Wolfe, Quenna Wong, Huichun Xu, Taiki Yamaji, Dharambir K Sanghera, Olle Melander, Christina Jern, Daniel Strbian, Israel Fernandez‐Cadenas, W T Longstreth, Arndt Rolfs, Jun Hata, Daniel Woo, Jonathan Rosand, Guillaume Pare, Jemma C Hopewell, Danish Saleheen, Kari Stefansson, Bradford B Worrall, Steven J Kittner, Sudha Seshadri, Myriam Fornage, Hugh S Markus, Joanna MM Howson, Yoichiro Kamatani, Stephanie Debette, Martin Dichgans, and VU University medical center
Subjects: Hemostasis, genome-wide association study, genetic pleiotropy, Hematology, Polymorphism, Single Nucleotide, Hemostatics, blood coagulation, cardiovascular diseases, Phenotype, Cardiovascular Diseases, Tissue Plasminogen Activator, hemostasis, Humans, Genetic Predisposition to Disease, Factor XI, Genome-Wide Association Study
Abstract: Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.
Published: 2022

35. No prospective association of a polygenic risk score for coronary artery disease with venous thromboembolism incidence

Author: Mary Cushman, Aaron R. Folsom, and Paul S. de Vries
Subjects: medicine.medical_specialty, business.industry, Incidence, Hazard ratio, Coronary Artery Disease, Venous Thromboembolism, Hematology, medicine.disease, Article, Pulmonary embolism, Coronary artery disease, Venous thrombosis, Risk Factors, Internal medicine, Cohort, Cardiology, Humans, Medicine, Prospective Studies, Risk factor, business, Prospective cohort study, Body mass index, Proportional Hazards Models
Abstract: Background Previous studies are inconsistent about whether chronic coronary artery disease or generalized atherosclerosis is a causal risk factor for venous thromboembolism. No study seems to have taken a genomic approach to this question. Objective To test in a prospective study whether a polygenic risk score for coronary artery disease is associated with risk of venous thromboembolism. Participants/methods Within the Atherosclerosis Risk in Communities Study cohort, we computed a previously validated polygenic risk score for coronary artery disease among 9144 White participants at baseline in 1987-1989. We followed the participants through 2015 for incident hospitalized venous thromboembolism events, validated by physician review. We used Cox proportional hazards regression to associate quintiles of the polygenic risk score to venous thromboembolism incidence rates. Results Over the median of 26 years of follow-up, 476 participants had a venous thromboembolism event. There was no apparent association between the coronary artery disease polygenic risk score and incident venous thromboembolism, with age, sex, body mass index adjusted hazard ratios across quintiles being 1 (reference), 0.87 (0.65, 1.15), 1.08 (0.82, 1.42), 0.96 (0.72, 1.27), and 1.03 (0.78, 1.37). Conclusions A genetic disposition to coronary artery disease did not confer an increased risk of venous thromboembolism in this prospective study.
Published: 2021

36. Whole-genome sequencing in diverse subjects identifies genetic correlates of leukocyte traits: The NHLBI TOPMed program

Author: Nicholas L. Smith, James A. Perry, Cecelia A. Laurie, Nancy J. Cox, Gonçalo R. Abecasis, Jerome I. Rotter, Laura Almasy, Zhe Wang, Michelle Daya, Yun Li, Eric Jorgenson, Adolfo Correa, Jai G. Broome, Nancy Min, Lisa R. Yanek, Alanna C. Morrison, Lynette Ekunwe, Debby Ngo, Victor E. Ortega, Klaudia Walter, John Blangero, Laura M. Raffield, Corey Cox, Terri H. Beaty, Deborah A. Meyers, Hua Tang, Marsha M. Wheeler, Kari E. North, Xue Zhong, Yann Ilboudo, Andrew D. Johnson, Caitlin P. McHugh, Jeffrey R. O'Connell, Ming-Huei Chen, Russell P. Tracy, Ramachandran S. Vasan, Nathan Pankratz, Joshua P. Lewis, Dawn L. DeMeo, Linda M. Polfus, Leslie A. Lange, Nancy L. Heard-Costa, Robert C. Kaplan, Meher Preethi Boorgula, Robert E. Gerszten, Albert V. Smith, Paul L. Auer, Sameer Chavan, Jennifer A. Brody, Charles Kooperberg, Michael Preuss, David C. Glahn, Rasika A. Mathias, Paul S. de Vries, Jonathon Rosen, Anna V. Mikhaylova, Joe Zein, Eric Boerwinkle, Nathalie Chami, Kathleen C. Barnes, Joanne E. Curran, Edwin K. Silverman, Matthew P. Conomos, Stephen S. Rich, Nicole Soranzo, Heather M. Highland, Michael H. Cho, Donald M. Lloyd-Jones, Myriam Fornage, Guillaume Lettre, Tyne W Miller-Fleming, Kathleen A. Ryan, Thomas W. Blackwell, Bruce M. Psaty, Lewis C. Becker, Nauder Faraday, Hélène Choquet, Alexander P. Reiner, Adam S. Butterworth, Kousik Kundu, Deepti Jain, Timothy A. Thornton, Brian D. Hobbs, Braxton D. Mitchell, Jee-Young Moon, Lifang Hou, Ani Manichaikul, Praveen Surendran, Suraj S. Nongmaithem, Quan Sun, Bingshan Li, Deborah A. Nickerson, and Ruth J. F. Loos
Subjects: Proteome, Quantitative Trait Loci, Genome-wide association study, Biology, Polymorphism, Single Nucleotide, Article, Dermatitis, Atopic, Pulmonary Disease, Chronic Obstructive, Leukocytes, Genetics, medicine, Humans, Genetic Predisposition to Disease, Gene, Genetics (clinical), X chromosome, Genetic association, Whole genome sequencing, Autosome, Whole Genome Sequencing, Genome, Human, Monocyte, Prognosis, Asthma, United Kingdom, United States, Phenotype, medicine.anatomical_structure, National Heart, Lung, and Blood Institute (U.S.), Biomarkers, Imputation (genetics), Genome-Wide Association Study
Abstract: Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.
Published: 2021

37. APOL1 Genetic Variants Are Associated With Increased Risk of Coronary Atherosclerotic Plaque Rupture in the Black Population

Author: Marco Delsante, Raquel Fernandez, Maarten Hoek, Masayuki Mori, Robert Kutys, Clint L. Miller, Teruhiko Yoshida, Khun Zaw Latt, Rika Kawakami, Xiaoqing Zhao, Avi Z. Rosenberg, Ka Hyun Paek, Daniela T. Fuller, Kenji Kawai, Jeffrey B. Kopp, Atsushi Sakamoto, Myung K. Shin, Aloke V. Finn, Renu Virmani, Paul S. de Vries, Harry R. Davis, Frank D. Kolodgie, Yu Sato, Jurgen Heymann, Liang Guo, Anne Cornelissen, and Elizabeth Binns-Roemer
Subjects: Kidney, education.field_of_study, Apolipoprotein B, biology, business.industry, Population, 030232 urology & nephrology, Genetic variants, Plaque rupture, Disease, 030204 cardiovascular system & hematology, medicine.disease, Bioinformatics, Coronary artery disease, 03 medical and health sciences, 0302 clinical medicine, Increased risk, medicine.anatomical_structure, biology.protein, Medicine, Cardiology and Cardiovascular Medicine, business, education
Abstract: Objective: Reported associations between kidney risk variants (G1 and G2) in APOL1 (apolipoprotein L1), encoding APOL1, and cardiovascular disease have been conflicting. We sought to explore associations of APOL1 risk variants with cause of sudden death using the CVPath Sudden Death Autopsy Registry. Approach and Results: APOL1 haplotypes and causes of sudden death, as determined through autopsy and histopathology, were obtained for 764 Black subjects. Genotyping revealed APOL1 risk alleles in 452 of 764 (59%) subjects with 347 (77%) subjects carrying one risk allele and 105 (23%) subjects harboring 2 risk alleles. APOL1 risk allele carrier status was associated with a significantly increased risk of coronary thrombosis due to plaque rupture, versus noncarriers (odds ratio for rupture, 1.655 [95% CI, 1.079–2.539]; P =0.021). Histological examinations showed coronary plaques in carriers of 2 APOL1 risk alleles had larger necrotic cores compared with noncarriers (necrotic core area/total plaque area: 46.79%±6.47% versus 20.57%±5.11%; P =0.0343 in ruptured plaques, and 41.48%±7.49% versus 18.93%±3.97%; P =0.0342 in nonruptured plaques), and immunohistochemical and immunofluorescent staining revealed APOL1-positive areas localized primarily to the necrotic core. Conclusions: APOL1 risk alleles were independently associated with an increased risk of thrombotic coronary death due to plaque rupture. Our results suggest that carriers of both 1 and 2 APOL1 risk alleles have greater accumulation of APOL1 protein within culprit plaques and greater necrotic core sizes than noncarriers. These findings suggest that APOL1 plays a role in determining plaque stability.
Published: 2021

38. Investigating gene-diet interactions impacting the association between macronutrient intake and glycemic traits

Author: Kenneth E. Westerman, Maura E. Walker, Sheila M. Gaynor, Jennifer Wessel, Daniel DiCorpo, Jiantao Ma, Alvaro Alonso, Stella Aslibekyan, Abigail S. Baldridge, Alain G. Bertoni, Mary L. Biggs, Jennifer A. Brody, Yii-Der Ida Chen, Joseé Dupuis, Mark O. Goodarzi, Xiuqing Guo, Natalie R. Hasbani, Adam Heath, Bertha Hidalgo, Marguerite R. Irvin, W. Craig Johnson, Rita R. Kalyani, Leslie Lange, Rozenn N. Lemaitre, Ching-Ti Liu, Simin Liu, Jee-Young Moon, Rami Nassir, James S. Pankow, Mary Pettinger, Laura M. Raffield, Laura J. Rasmussen-Torvik, Elizabeth Selvin, Mackenzie K. Senn, Aladdin H. Shadyab, Albert V. Smith, Nicholas L. Smith, Lyn Steffen, Sameera Talegakwar, Kent D. Taylor, Paul S. de Vries, James G. Wilson, Alexis C. Wood, Lisa R. Yanek, Jie Yao, Yinan Zheng, Eric Boerwinkle, Alanna C. Morrison, Miriam Fornage, Tracy P. Russell, Bruce M. Psaty, Daniel Levy, Nancy L. Heard-Costa, Vasan S. Ramachandran, Rasika A. Mathias, Donna K. Arnett, Robert Kaplan, Kari E. North, Adolfo Correa, April Carson, Jerome I. Rotter, Stephen S. Rich, JoAnn E. Manson, Alexander P. Reiner, Charles Kooperberg, Jose C. Florez, James B. Meigs, Jordi Merino, Deirdre K. Tobias, Han Chen, and Alisa K. Manning
Subjects: Endocrinology, Diabetes and Metabolism, Internal Medicine
Abstract: BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.
Published: 2022

39. Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

Author: Adolfo Correa, Jai G. Broome, Chunyan Ren, Kari E. North, Nancy L. Heard-Costa, Yao Yao, Brian D. Hobbs, Mary Cushman, Leslie A. Lange, Daniel E. Bauer, Xiuwen Zheng, Braxton D. Mitchell, Yun Li, Quan Sun, Sébastian Méric de Bellefon, Terri H. Beaty, Paul S. de Vries, Ruth J. F. Loos, Adrienne M. Stilp, Albert V. Smith, Paul L. Auer, Deepti Jain, Lifang Hou, Robert C. Kaplan, Jee-Young Moon, Michael Preuss, Stephen S. Rich, Guillaume Lettre, Nicole Soranzo, Eric Boerwinkle, Kousik Kundu, Laura Almasy, Marsha M. Wheeler, Thomas W. Blackwell, Nancy Min, Nicholas L. Smith, Bruce M. Psaty, Lisa R. Yanek, Joanne E. Curran, Stacey Gabriel, Kathleen A. Ryan, Alanna C. Morrison, Lynette Ekunwe, Caitlin P. McHugh, Laura M. Raffield, Adam S. Butterworth, Deborah A. Nickerson, Ravindranath Duggirala, Gonçalo R. Abecasis, John Lane, Hélène Choquet, Andrew D. Johnson, Nauder Faraday, Russell T. Walton, Praveen Surendran, Jennifer A. Brody, Yao Hu, Alexander P. Reiner, Jerome I. Rotter, Donald M. Lloyd-Jones, Cathy C. Laurie, Zhe Wang, Hua Tang, Charles Kooperberg, Eric Jorgenson, Jeffrey R. O'Connell, Shuquan Rao, Nathalie Chami, Rasika A. Mathias, Matthew P. Conomos, Myriam Fornage, Ramachandran S. Vasan, Nathan Pankratz, Joshua P. Lewis, Lewis C. Becker, Benjamin P. Kleinstiver, Cecelia A. Laurie, Ming-Huei Chen, and John Blangero
Subjects: Adult, Male, Quality Control, 0301 basic medicine, Erythrocytes, Population, Datasets as Topic, Genome-wide association study, Biology, Quantitative trait locus, Article, 03 medical and health sciences, 0302 clinical medicine, Genetics, Humans, Indel, education, Gene, Genetics (clinical), Aged, Genetic association, Gene Editing, Whole genome sequencing, education.field_of_study, Genetic Variation, Reproducibility of Results, Correction, Middle Aged, United States, Genetic architecture, HEK293 Cells, Phenotype, 030104 developmental biology, 030220 oncology & carcinogenesis, Female, National Heart, Lung, and Blood Institute (U.S.), Chromosomes, Human, Pair 16, Genome-Wide Association Study
Abstract: Summary Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380 ), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.
Published: 2021

40. Association of Rare Protein-Truncating DNA Variants in APOB or PCSK9 With Low-density Lipoprotein Cholesterol Level and Risk of Coronary Heart Disease

Author: Jacqueline S. Dron, Aniruddh P. Patel, Yiyi Zhang, Sean J. Jurgens, Dimitri J. Maamari, Minxian Wang, Eric Boerwinkle, Alanna C. Morrison, Paul S. de Vries, Myriam Fornage, Lifang Hou, Donald M. Lloyd-Jones, Bruce M. Psaty, Russell P. Tracy, Joshua C. Bis, Ramachandran S. Vasan, Daniel Levy, Nancy Heard-Costa, Stephen S. Rich, Xiuqing Guo, Kent D. Taylor, Richard A. Gibbs, Jerome I. Rotter, Cristen J. Willer, Elizabeth C. Oelsner, Andrew E. Moran, Gina M. Peloso, Pradeep Natarajan, and Amit V. Khera
Subjects: Cardiology and Cardiovascular Medicine
Abstract: ImportanceProtein-truncating variants (PTVs) in apolipoprotein B (APOB) and proprotein convertase subtilisin/kexin type 9 (PCSK9) are associated with significantly lower low-density lipoprotein (LDL) cholesterol concentrations. The association of these PTVs with coronary heart disease (CHD) warrants further characterization in large, multiracial prospective cohort studies.ObjectiveTo evaluate the association of PTVs in APOB and PCSK9 with LDL cholesterol concentrations and CHD risk.Design, Setting, and ParticipantsThis studied included participants from 5 National Heart, Lung, and Blood Institute (NHLBI) studies and the UK Biobank. NHLBI study participants aged 5 to 84 years were recruited between 1971 and 2002 across the US and underwent whole-genome sequencing. UK Biobank participants aged 40 to 69 years were recruited between 2006 and 2010 in the UK and underwent whole-exome sequencing. Data were analyzed from June 2021 to October 2022.ExposuresPTVs in APOB and PCSK9.Main Outcomes and MeasuresEstimated untreated LDL cholesterol levels and CHD.ResultsAmong 19 073 NHLBI participants (10 598 [55.6%] female; mean [SD] age, 52 [17] years), 139 (0.7%) carried an APOB or PCSK9 PTV, which was associated with 49 mg/dL (95% CI, 43-56) lower estimated untreated LDL cholesterol level. Over a median (IQR) follow-up of 21.5 (13.9-29.4) years, incident CHD was observed in 12 of 139 carriers (8.6%) vs 3029 of 18 934 noncarriers (16.0%), corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.28-0.89; P = .02). Among 190 464 UK Biobank participants (104 831 [55.0%] female; mean [SD] age, 57 [8] years), 662 (0.4%) carried a PTV, which was associated with 45 mg/dL (95% CI, 42-47) lower estimated untreated LDL cholesterol level. Estimated CHD risk by age 75 years was 3.7% (95% CI, 2.0-5.3) in carriers vs 7.0% (95% CI, 6.9-7.2) in noncarriers, corresponding to an adjusted hazard ratio of 0.51 (95% CI, 0.32-0.81; P = .004).Conclusions and RelevanceAmong 209 537 individuals in this study, 0.4% carried an APOB or PCSK9 PTV that was associated with less exposure to LDL cholesterol and a 49% lower risk of CHD.
Published: 2023

41. Lifestyle Risk Score: handling missingness of individual lifestyle components in meta-analysis of gene-by-lifestyle interactions

Author: Diana van Heemst, Oyomoare Osazuwa-Peters, RJ Waken, Ching-Ti Liu, Paul S de Vries, Lisa de las Fuentes, Xiaoyu Zhang, D. C. Rao, Alanna C. Morrison, Wenyi Wang, Ko Willems van Dijk, Michael R. Brown, Yun Ju Sung, Karen Schwander, Eric Boerwinkle, L. Adrienne Cupples, Hanfei Xu, and Raymond Noordam
Subjects: 0303 health sciences, Framingham Risk Score, business.industry, 030305 genetics & heredity, MEDLINE, Cardiometabolic Risk Factors, Environmental exposure, Disease, Moderation, Missing data, Article, 03 medical and health sciences, Sample size determination, Meta-analysis, Hypertension, Genetics, Humans, Medicine, Gene-Environment Interaction, Healthy Lifestyle, Obesity, business, Genetics (clinical), Genome-Wide Association Study, Demography
Abstract: Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.
Published: 2021

42. Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts

Author: Daniel DiCorpo, Jessica LeClair, Joanne B. Cole, Chloé Sarnowski, Fariba Ahmadizar, Lawrence F. Bielak, Anneke Blokstra, Erwin P. Bottinger, Layal Chaker, Yii-Der I. Chen, Ye Chen, Paul S. de Vries, Tariq Faquih, Mohsen Ghanbari, Valborg Gudmundsdottir, Xiuqing Guo, Natalie R. Hasbani, Dorina Ibi, M. Arfan Ikram, Maryam Kavousi, Hampton L. Leonard, Aaron Leong, Josep M. Mercader, Alanna C. Morrison, Girish N. Nadkarni, Mike A. Nalls, Raymond Noordam, Michael Preuss, Jennifer A. Smith, Stella Trompet, Petra Vissink, Jie Yao, Wei Zhao, Eric Boerwinkle, Mark O. Goodarzi, Vilmundur Gudnason, J. Wouter Jukema, Sharon L.R. Kardia, Ruth J.F. Loos, Ching-Ti Liu, Alisa K. Manning, Dennis Mook-Kanamori, James S. Pankow, H. Susan J. Picavet, Naveed Sattar, Eleanor M. Simonsick, W.M. Monique Verschuren, Ko Willems van Dijk, Jose C. Florez, Jerome I. Rotter, James B. Meigs, Josée Dupuis, Miriam S. Udler, Epidemiology, and Internal Medicine
Subjects: Endocrinology, Diabetes and Metabolism, Cardiovascular, Medical and Health Sciences, Endocrinology & Metabolism, SDG 3 - Good Health and Well-being, Internal Medicine, Diabetes Mellitus, Genetics, Humans, 2.1 Biological and endogenous factors, Obesity, Aetiology, Alleles, Heart Disease - Coronary Heart Disease, Metabolic and endocrine, Nutrition, Advanced and Specialized Nursing, Liver Disease, Prevention, Diabetes, Cross-Sectional Studies, Heart Disease, Good Health and Well Being, Diabetes Mellitus, Type 2, Pharmaceutical Preparations, Genetic Loci, Digestive Diseases, Type 2
Abstract: OBJECTIVE Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.
Published: 2022

43. Rare coding variants in 35 genes associate with circulating lipid levels-A multi-ancestry analysis of 170,000 exomes

Author: George Hindy, Peter Dornbos, Mark D. Chaffin, Dajiang J. Liu, Minxian Wang, Margaret Sunitha Selvaraj, David Zhang, Joseph Park, Carlos A. Aguilar-Salinas, Lucinda Antonacci-Fulton, Diego Ardissino, Donna K. Arnett, Stella Aslibekyan, Gil Atzmon, Christie M. Ballantyne, Francisco Barajas-Olmos, Nir Barzilai, Lewis C. Becker, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Eric Boerwinkle, Lori L. Bonnycastle, Erwin Bottinger, Donald W. Bowden, Matthew J. Bown, Jennifer A. Brody, Jai G. Broome, Noël P. Burtt, Brian E. Cade, Federico Centeno-Cruz, Edmund Chan, Yi-Cheng Chang, Yii-Der I. Chen, Ching-Yu Cheng, Won Jung Choi, Rajiv Chowdhury, Cecilia Contreras-Cubas, Emilio J. Córdova, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, John Danesh, Paul S. de Vries, Ralph A. DeFronzo, Harsha Doddapaneni, Ravindranath Duggirala, Susan K. Dutcher, Patrick T. Ellinor, Leslie S. Emery, Jose C. Florez, Myriam Fornage, Barry I. Freedman, Valentin Fuster, Ma. Eugenia Garay-Sevilla, Humberto García-Ortiz, Soren Germer, Richard A. Gibbs, Christian Gieger, Benjamin Glaser, Clicerio Gonzalez, Maria Elena Gonzalez-Villalpando, Mariaelisa Graff, Sarah E. Graham, Niels Grarup, Leif C. Groop, Xiuqing Guo, Namrata Gupta, Sohee Han, Craig L. Hanis, Torben Hansen, Jiang He, Nancy L. Heard-Costa, Yi-Jen Hung, Mi Yeong Hwang, Marguerite R. Irvin, Sergio Islas-Andrade, Gail P. Jarvik, Hyun Min Kang, Sharon L.R. Kardia, Tanika Kelly, Eimear E. Kenny, Alyna T. Khan, Bong-Jo Kim, Ryan W. Kim, Young Jin Kim, Heikki A. Koistinen, Charles Kooperberg, Johanna Kuusisto, Soo Heon Kwak, Markku Laakso, Leslie A. Lange, Jiwon Lee, Juyoung Lee, Seonwook Lee, Donna M. Lehman, Rozenn N. Lemaitre, Allan Linneberg, Jianjun Liu, Ruth J.F. Loos, Steven A. Lubitz, Valeriya Lyssenko, Ronald C.W. Ma, Lisa Warsinger Martin, Angélica Martínez-Hernández, Rasika A. Mathias, Stephen T. McGarvey, Ruth McPherson, James B. Meigs, Thomas Meitinger, Olle Melander, Elvia Mendoza-Caamal, Ginger A. Metcalf, Xuenan Mi, Karen L. Mohlke, May E. Montasser, Jee-Young Moon, Hortensia Moreno-Macías, Alanna C. Morrison, Donna M. Muzny, Sarah C. Nelson, Peter M. Nilsson, Jeffrey R. O’Connell, Marju Orho-Melander, Lorena Orozco, Colin N.A. Palmer, Nicholette D. Palmer, Cheol Joo Park, Kyong Soo Park, Oluf Pedersen, Juan M. Peralta, Patricia A. Peyser, Wendy S. Post, Michael Preuss, Bruce M. Psaty, Qibin Qi, D.C. Rao, Susan Redline, Alexander P. Reiner, Cristina Revilla-Monsalve, Stephen S. Rich, Nilesh Samani, Heribert Schunkert, Claudia Schurmann, Daekwan Seo, Jeong-Sun Seo, Xueling Sim, Rob Sladek, Kerrin S. Small, Wing Yee So, Adrienne M. Stilp, E. Shyong Tai, Claudia H.T. Tam, Kent D. Taylor, Yik Ying Teo, Farook Thameem, Brian Tomlinson, Michael Y. Tsai, Tiinamaija Tuomi, Jaakko Tuomilehto, Teresa Tusié-Luna, Miriam S. Udler, Rob M. van Dam, Ramachandran S. Vasan, Karine A. Viaud Martinez, Fei Fei Wang, Xuzhi Wang, Hugh Watkins, Daniel E. Weeks, James G. Wilson, Daniel R. Witte, Tien-Yin Wong, Lisa R. Yanek, Sekar Kathiresan, Daniel J. Rader, Jerome I. Rotter, Michael Boehnke, Mark I. McCarthy, Cristen J. Willer, Pradeep Natarajan, Jason A. Flannick, Amit V. Khera, Gina M. Peloso, NIH - National Heart, Lung, and Blood Institute (NHLBI) (Estados Unidos), Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Leif Groop Research Group, Clinicum, Department of Medicine, HUS Internal Medicine and Rehabilitation, University of Helsinki, and Department of Public Health
Subjects: APOC3, Blood Glucose, Multifactorial Inheritance, gene-based association, Polymorphism, Single Nucleotide, Article, Open Reading Frames, ANGPTL3, lipid, OF-FUNCTION MUTATIONS, BINDING, Databases, Genetic, SEQUENCE VARIANTS, Genetics, Humans, Exome, Genetic Predisposition to Disease, SINUSOIDAL ENDOTHELIAL-CELLS, Genetics (clinical), Alleles, RISK, IDENTIFICATION, 1184 Genetics, developmental biology, physiology, association, cholesterol, Genetic Variation, Computational Biology, Molecular Sequence Annotation, PERILIPIN, Lipid Metabolism, Lipids, Genetics, Population, Phenotype, Diabetes Mellitus, Type 2, Liver, Case-Control Studies, exome sequencing, TRIGLYCERIDES, Genome-Wide Association Study
Abstract: Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí
Published: 2022

44. Correlations between complex human phenotypes vary by genetic background, gender, and environment

Author: Michael Elgart, Matthew O. Goodman, Carmen Isasi, Han Chen, Alanna C. Morrison, Paul S. de Vries, Huichun Xu, Ani W. Manichaikul, Xiuqing Guo, Nora Franceschini, Bruce M. Psaty, Stephen S. Rich, Jerome I. Rotter, Donald M. Lloyd-Jones, Myriam Fornage, Adolfo Correa, Nancy L. Heard-Costa, Ramachandran S. Vasan, Ryan Hernandez, Robert C. Kaplan, Susan Redline, and Tamar Sofer
Subjects: General Biochemistry, Genetics and Molecular Biology
Abstract: We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
Published: 2022

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

Author: Adolfo Correa, Corbin Quick, Jennifer A. Brody, Daniel E. Weeks, Rounak Dey, Joanne E. Curran, Charles Kooperberg, Wei Zhao, Brian G. Kral, Lisa W. Martin, Christen J. Willer, Donald W. Bowden, Eric Boerwinkle, Theodore Arapoglou, Joshua C. Bis, Barry I. Freedman, Leslie A. Lange, Ryan Sun, James G. Wilson, Lawrence F. Bielak, May E. Montasser, Kent D. Taylor, Jerome I. Rotter, Ramachandran S. Vasan, L. Adrienne Cupples, Rita R. Kalyani, Hufeng Zhou, Ani Manichaikul, John Blangero, Han Chen, Patricia A. Peyser, Stephen S. Rich, Brian E. Cade, Sheila M. Gaynor, Paul S. de Vries, Xihong Lin, Susan Redline, Thomas W. Blackwell, Margaret Sunitha Selvaraj, Jeffrey R. O'Connell, Xihao Li, Bruce M. Psaty, Ravindranath Duggirala, Matthew P. Conomos, Kenneth Rice, Donna K. Arnett, Muagututi‘a Sefuiva Reupena, Alanna C. Morrison, Nicholette D. Palmer, Jennifer A. Smith, Harald H H Göring, Alexander P. Reiner, Lisa R. Yanek, Braxton D. Mitchell, Laura M. Raffield, Yaowu Liu, Xiuqing Guo, Gina M. Peloso, Zilin Li, Pradeep Natarajan, and Take Naseri
Subjects: Whole genome sequencing, Technology, Genome, Whole Genome Sequencing, Human Genome, Genetic Variation, Scale (descriptive set theory), Computational biology, Replicate, TOPMed Lipids Working Group, Biological Sciences, Biology, Medical and Health Sciences, Annotation, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Phenotype, Good Health and Well Being, Genetics, Humans, Generic health relevance, Genome-Wide Association Study, Biotechnology, Developmental Biology, Genetic association
Abstract: Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants’ (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.
Published: 2021

46. Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Author: Akhil Pampana, Xiuqing Guo, Tanika N. Kelly, Yii-Der Ida Chen, Charles Kooperberg, Chii-Min Hwu, Mariaelisa Graff, Jiang He, Xihao Li, Patrick T. Ellinor, Joshua C. Bis, Kari E. North, Nancy L. Heard-Costa, Joseph Park, Stacey Gabriel, Joanne E. Curran, Braxton D. Mitchell, Lee-Ming Chuang, Ravindranath Duggirala, Jerome I. Rotter, Robert C. Kaplan, Soren Germer, Pradeep Natarajan, Take Naseri, Xihong Lin, Susan K. Dutcher, Stella Aslibekyan, Ryan W. Kim, Daniel J. Rader, Richard A. Gibbs, Myriam Fornage, Eric Boerwinkle, Bertha Hidalgo, Muagututi’a S. Reupena, Deborah A. Nickerson, Zhe Wang, Donald W. Bowden, Yuxuan Wang, Alanna C. Morrison, Stephen S. Rich, David Zhang, Gina M. Peloso, Xiaohui Li, Martin Lisa, Lisa de las Fuentes, Zilin Li, Alexander P. Reiner, Jennifer A. Brody, Lisa R. Yanek, Marguerite R. Irvin, Bruce M. Psaty, Bao Wei, Preuss Michael, Leslie A. Lange, John T. Wilkins, Russell P. Tracy, Paul S. de Vries, Wei Zhao, Rasika A. Mathias, Susan Redline, Xiao Sun, Kent D. Taylor, Barry I. Freedman, Ani Manichaikul, Donna K. Arnett, Nicholette D. Palmer, Cristen J. Willer, Steven A. Lubitz, Sharon L.R. Kardia, L. Adrienne Cupples, Ramachandran S. Vasan, May E. Montasser, Ren-Hua Chung, Margaret Sunitha Selvaraj, Jeffrey R. O'Connell, Ruth J. F. Loos, Jennifer A. Smith, John Blangero, Brian G. Kral, Karine A. Viaud Martinez, Stephen T. McGarvey, Adolfo Correa, Michael Y. Tsai, Patricia A. Peyser, and Brian E. Cade
Subjects: Genetics, Whole genome sequencing, Genome Scan, Blood lipids, Biology, medicine.disease, Genome, symbols.namesake, Mendelian inheritance, symbols, medicine, lipids (amino acids, peptides, and proteins), Allele, Gene, Dyslipidemia
Abstract: Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rareLDLRintronic variants associated with markedly increased LDL-C, similar to rareLDLRexonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.
Published: 2021

47. Whole genome sequence analysis of blood lipid levels in66,000 individuals

Author: Margaret Sunitha, Selvaraj, Xihao, Li, Zilin, Li, Akhil, Pampana, David Y, Zhang, Joseph, Park, Stella, Aslibekyan, Joshua C, Bis, Jennifer A, Brody, Brian E, Cade, Lee-Ming, Chuang, Ren-Hua, Chung, Joanne E, Curran, Lisa, de Las Fuentes, Paul S, de Vries, Ravindranath, Duggirala, Barry I, Freedman, Mariaelisa, Graff, Xiuqing, Guo, Nancy, Heard-Costa, Bertha, Hidalgo, Chii-Min, Hwu, Marguerite R, Irvin, Tanika N, Kelly, Brian G, Kral, Leslie, Lange, Xiaohui, Li, Martin, Lisa, Steven A, Lubitz, Ani W, Manichaikul, Preuss, Michael, May E, Montasser, Alanna C, Morrison, Take, Naseri, Jeffrey R, O'Connell, Nicholette D, Palmer, Patricia A, Peyser, Muagututia S, Reupena, Jennifer A, Smith, Xiao, Sun, Kent D, Taylor, Russell P, Tracy, Michael Y, Tsai, Zhe, Wang, Yuxuan, Wang, Wei, Bao, John T, Wilkins, Lisa R, Yanek, Wei, Zhao, Donna K, Arnett, John, Blangero, Eric, Boerwinkle, Donald W, Bowden, Yii-Der Ida, Chen, Adolfo, Correa, L Adrienne, Cupples, Susan K, Dutcher, Patrick T, Ellinor, Myriam, Fornage, Stacey, Gabriel, Soren, Germer, Richard, Gibbs, Jiang, He, Robert C, Kaplan, Sharon L R, Kardia, Ryan, Kim, Charles, Kooperberg, Ruth J F, Loos, Karine A, Viaud-Martinez, Rasika A, Mathias, Stephen T, McGarvey, Braxton D, Mitchell, Deborah, Nickerson, Kari E, North, Bruce M, Psaty, Susan, Redline, Alexander P, Reiner, Ramachandran S, Vasan, Stephen S, Rich, Cristen, Willer, Jerome I, Rotter, Daniel J, Rader, Xihong, Lin, Gina M, Peloso, and Sebastian, Zoellner
Subjects: Multidisciplinary, Whole Genome Sequencing, General Physics and Astronomy, Humans, General Chemistry, Cholesterol, LDL, Lipids, General Biochemistry, Genetics and Molecular Biology, Alleles, Genome-Wide Association Study
Abstract: Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.
Published: 2021

48. Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop

Author: Gary M. Hunninghake, Ulrich Broeckel, Udo Hoffmann, James Luo, Li Shen, Elizabeth M. McNally, Huiqing Li, Matthew Nayor, Ramachandran S. Vasan, R. Graham Barr, Susan Cheng, David A. Bluemke, George Thanassoulis, Márton Kolossváry, Donna K. Arnett, Peter Caravan, Paul S. de Vries, and Peter Kochunov
Subjects: Potential impact, business.industry, Genomic data, Genomics, Disease, Data science, Article, Lung disease, Medicine, Radiology, Nuclear Medicine and imaging, Genomic information, Cardiology and Cardiovascular Medicine, business, Imaging genomics
Abstract: Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.
Published: 2021

49. Multi-omics insights into the biological mechanisms underlying gene-by-lifestyle interactions with smoking and alcohol consumption detected by genome-wide trans-ancestry meta-analysis

Author: Nora Franceschini, W. James Gauderman, L. Adrienne Cupples, Alisa K. Manning, Paul S. de Vries, Karen Schwander, Michael A. Province, C. Charles Gu, Vincent Laville, Daniel Levy, Daniel I. Chasman, Alanna C. Morrison, Timothy D. Majarian, Dabeeru C. Rao, Charles N. Rotimi, Michael R. Brown, Yun Ju Sung, Hugues Aschard, Amy R. Bentley, and Mary F. Feitosa
Subjects: Concordance, Meta-analysis, Genotype, Trait, Epigenetics, Computational biology, Biology, Phenotype, Gene, Genome
Abstract: Gene-lifestyle interaction analyses have identified genetic variants whose effect on cardiovascular risk-raising traits is modified by alcohol consumption and smoking behavior. The biological mechanisms of these interactions remain largely unknown, but may involve epigenetic modification linked to perturbation of gene expression. Diverse, individual-level datasets including genotypes, methylation and gene expression conditional on lifestyle factors, are ideally suited to study this hypothesis, yet are often unavailable for large numbers of individuals. Summary-level data, such as effect sizes of genetic variants on a phenotype, present an opportunity for multi-omic study of the biological mechanisms underlying gene-lifestyle interactions. We propose a method that unifies disparate, publicly available summary datasets to build mechanistic hypotheses in models of smoking behavior and alcohol consumption with blood lipid levels and blood pressure measures. Of 897 observed genetic interactions, discovered through genome-wide analysis in diverse multi-ethnic cohorts, 48 were identified with lifestyle-related differentially methylated sites within close proximity and linked to target genes. Smoking behavior and blood lipids account for 37 and 28 of these signals respectively. Five genes also showed differential expression conditional on lifestyle factors within these loci with mechanisms supported in the literature. Our analysis demonstrates the utility of summary data in characterizing observed gene-lifestyle interactions and prioritizes genetic loci for experimental follow up related to blood lipids, blood pressure, and cigarette smoking. We show concordance between multiple trait-or exposure-related associations from diverse assays, driving hypothesis generation for better understanding gene-lifestyle interactions.
Published: 2021

50. Polygenic Risk Prediction using Gradient Boosted Trees Captures Non-Linear Genetic Effects and Allele Interactions in Complex Phenotypes

Author: Jennifer A. Brody, Stephen S. Rich, Bruce M. Psaty, Myriam Fornage, Santiago Romero-Brufau, Genevieve Lyons, Michael Elgart, Daniel Levy, Gina M. Peloso, Henry J. Lin, Laura M. Raffield, Xiuqing Guo, Jerome I. Rotter, Han Chen, Yan Gao, Leslie A. Lange, Tamar Sofer, Donald M. Lloyd-Jones, Paul S. de Vries, Alanna C. Morrison, Susan Redline, and Nuzulul Kurniansyah
Subjects: education.field_of_study, Lasso (statistics), Sample size determination, Population, Statistics, Feature selection, Single-nucleotide polymorphism, Allele, Biology, education, Explained variation, Interaction
Abstract: Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a given trait. However, the standard PRS fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). Machine learning algorithms can be used to account for such non-linearities and interactions. We trained and validated polygenic prediction models for five complex phenotypes in a multi-ancestry population: total cholesterol, triglycerides, systolic blood pressure, sleep duration, and height. We used an ensemble method of LASSO for feature selection and gradient boosted trees (XGBoost) for non-linearities and interaction effects. In an independent test set, we found that combining a standard PRS as a feature in the XGBoost model increases the percentage variance explained (PVE) of the prediction model compared to the standard PRS by 25% for sleep duration, 26% for height, 44% for systolic blood pressure, 64% for triglycerides, and 85% for total cholesterol. Machine learning models trained in specific racial/ethnic groups performed similarly in multi-ancestry trained models, despite smaller sample sizes. The predictions of the machine learning models were superior to the standard PRS in each of the racial/ethnic groups in our study. However, among Blacks the PVE was substantially lower than for other groups. For example, the PVE for total cholesterol was 8.1%, 12.9%, and 17.4% for Blacks, Whites, and Hispanics/Latinos, respectively. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.
Published: 2021

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