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813 results on '"de Vries, Paul"'

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

Author

Rocheleau, Ghislain, Clarke, Shoa, Auguste, Gaëlle, Hasbani, Natalie, Morrison, Alanna, Heath, Adam, Bielak, Lawrence, Iyer, Kruthika, Young, Erica, Stitziel, Nathan, Jun, Goo, Laurie, Cecelia, Broome, Jai, Khan, Alyna, Arnett, Donna, Becker, Lewis, Bis, Joshua, Boerwinkle, Eric, Bowden, Donald, Carson, April, Ellinor, Patrick, Fornage, Myriam, Franceschini, Nora, Freedman, Barry, Heard-Costa, Nancy, Hou, Lifang, Chen, Yii-Der, Kenny, Eimear, Kooperberg, Charles, Kral, Brian, Loos, Ruth, Lutz, Sharon, Manson, JoAnn, Martin, Lisa, Mitchell, Braxton, Nassir, Rami, Palmer, Nicholette, Post, Wendy, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Regan, Elizabeth, Rich, Stephen, Smith, Jennifer, Taylor, Kent, Yanek, Lisa, Young, Kendra, Hilliard, Austin, Tcheandjieu, Catherine, Peyser, Patricia, Vasan, Ramachandran, Rotter, Jerome, Miller, Clint, Assimes, Themistocles, de Vries, Paul, and Do, Ron

Subjects
Humans, Coronary Artery Disease, Genetic Predisposition to Disease, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Male, Female, Gene Frequency, Genome-Wide Association Study, White People, Case-Control Studies, Whole Genome Sequencing, Genetic Variation, Middle Aged
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

2. Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification.

Author

de Vries, Paul, Conomos, Matthew, Singh, Kuldeep, Nicholson, Christopher, Jain, Deepti, Hasbani, Natalie, Jiang, Wanlin, Lee, Sujin, Cardenas, Christian, Lutz, Sharon, Wong, Doris, Guo, Xiuqing, Yao, Jie, Young, Erica, Tcheandjieu, Catherine, Hilliard, Austin, Bis, Joshua, Bielak, Lawrence, Brown, Michael, Musharoff, Shaila, Clarke, Shoa, Terry, James, Palmer, Nicholette, Yanek, Lisa, Xu, Huichun, Heard-Costa, Nancy, Wessel, Jennifer, Selvaraj, Margaret, Li, Rebecca, Sun, Xiao, Turner, Adam, Stilp, Adrienne, Khan, Alyna, Newman, Anne, Rasheed, Asif, Freedman, Barry, Kral, Brian, McHugh, Caitlin, Hodonsky, Chani, Saleheen, Danish, Herrington, David, Jacobs, David, Nickerson, Deborah, Boerwinkle, Eric, Wang, Fei, Heiss, Gerardo, Jun, Goo, Kinney, Greg, Sigurslid, Haakon, Doddapaneni, HarshaVardhan, Hall, Ira, Bensenor, Isabela, Broome, Jai, Crapo, James, Wilson, James, Smith, Jennifer, Blangero, John, Vargas, Jose, Mosquera, Jose, Smith, Joshua, Viaud-Martinez, Karine, Ryan, Kathleen, Young, Kendra, Taylor, Kent, Lange, Leslie, Emery, Leslie, Bittencourt, Marcio, Montasser, May, Yu, Miao, Mahaney, Michael, Mahamdeh, Mohammed, Fornage, Myriam, Franceschini, Nora, Lotufo, Paulo, Natarajan, Pradeep, Wong, Quenna, Mathias, Rasika, Gibbs, Richard, Do, Ron, Mehran, Roxana, Tracy, Russell, Kim, Ryan, Nelson, Sarah, Damrauer, Scott, Kardia, Sharon, Rich, Stephen, Fuster, Valentin, Napolioni, Valerio, Zhao, Wei, Tian, Wenjie, Yin, Xianyong, Min, Yuan-I, Manning, Alisa, Peloso, Gina, Kelly, Tanika, ODonnell, Christopher, Morrison, Alanna, Curran, Joanne, Zapol, Warren, and Bowden, Donald

Abstract

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Published
2023

3. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Author

Wang, Yuxuan, Selvaraj, Margaret, Li, Xihao, Li, Zilin, Holdcraft, Jacob, Arnett, Donna, Bis, Joshua, Blangero, John, Boerwinkle, Eric, Bowden, Donald, Cade, Brian, Carlson, Jenna, Carson, April, Chen, Yii-Der, Curran, Joanne, de Vries, Paul, Dutcher, Susan, Ellinor, Patrick, Floyd, James, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, Guo, Xiuqing, He, Jiang, Heard-Costa, Nancy, Hildalgo, Bertha, Hou, Lifang, Irvin, Marguerite, Joehanes, Roby, Kaplan, Robert, Kardia, Sharon, Kelly, Tanika, Kim, Ryan, Kooperberg, Charles, Kral, Brian, Levy, Daniel, Li, Changwei, Liu, Chunyu, Lloyd-Jone, Don, Loos, Ruth, Mahaney, Michael, Martin, Lisa, Mathias, Rasika, Minster, Ryan, Mitchell, Braxton, Montasser, May, Morrison, Alanna, Murabito, Joanne, Naseri, Take, OConnell, Jeffrey, Palmer, Nicholette, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Rao, Dabeeru, Redline, Susan, Reiner, Alexander, Rich, Stephen, Ruepena, Muagututia, Sheu, Wayne, Smith, Jennifer, Smith, Albert, Tiwari, Hemant, Tsai, Michael, Viaud-Martinez, Karine, Wang, Zhe, Yanek, Lisa, Zhao, Wei, Lin, Xihong, Natarajan, Pradeep, Peloso, Gina, and Rotter, Jerome

Subjects
association, blood lipid, cholesterol, lncRNA, rare variants, whole-genome sequencing, Humans, RNA, Long Noncoding, Genome-Wide Association Study, Precision Medicine, Whole Genome Sequencing, Lipids, Polymorphism, Single Nucleotide
Abstract

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Published
2023

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

Author

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

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

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

Published
2023

6. Polygenic Risk Scores and Extreme Coronary Artery Calcium Phenotypes (CAC=0 and CAC≥1000) in Adults ≥75 Years Old: The ARIC Study

Author

Dzaye, Omar, Razavi, Alexander C., Dardari, Zeina A., Wang, Frances M., Honda, Yasuyuki, Nasir, Khurram, Coresh, Josef, Howard-Claudio, Candace M., Jin, Jin, Yu, Bing, de Vries, Paul S., Wagenknecht, Lynne, Folsom, Aaron R., Blankstein, Ron, Kelly, Tanika N., Whelton, Seamus P., Mortensen, Martin Bødtker, Wang, Ziqiao, Chatterjee, Nilanjan, Matsushita, Kunihiro, and Blaha, Michael J.

Published
2024
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7. Genetic insights into resting heart rate and its role in cardiovascular disease.

Author

van de Vegte, Yordi, Eppinga, Ruben, van der Ende, M, Hagemeijer, Yanick, Mahendran, Yuvaraj, Salfati, Elias, Smith, Albert, Tan, Vanessa, Arking, Dan, Ntalla, Ioanna, Appel, Emil, Schurmann, Claudia, Brody, Jennifer, Rueedi, Rico, Polasek, Ozren, Sveinbjornsson, Gardar, Lecoeur, Cecile, Ladenvall, Claes, Zhao, Jing, Isaacs, Aaron, Wang, Lihua, Luan, Jianan, Hwang, Shih-Jen, Mononen, Nina, Auro, Kirsi, Jackson, Anne, Bielak, Lawrence, Zeng, Linyao, Shah, Nabi, Nethander, Maria, Campbell, Archie, Rankinen, Tuomo, Pechlivanis, Sonali, Qi, Lu, Zhao, Wei, Rizzi, Federica, Tanaka, Toshiko, Robino, Antonietta, Cocca, Massimiliano, Lange, Leslie, Müller-Nurasyid, Martina, Roselli, Carolina, Zhang, Weihua, Kleber, Marcus, Guo, Xiuqing, Lin, Henry, Pavani, Francesca, Galesloot, Tessel, Noordam, Raymond, Milaneschi, Yuri, Schraut, Katharina, den Hoed, Marcel, Degenhardt, Frauke, Trompet, Stella, van den Berg, Marten, Pistis, Giorgio, Tham, Yih-Chung, Weiss, Stefan, Sim, Xueling, Li, Hengtong, van der Most, Peter, Nolte, Ilja, Lyytikäinen, Leo-Pekka, Said, M, Witte, Daniel, Iribarren, Carlos, Launer, Lenore, Ring, Susan, de Vries, Paul, Sever, Peter, Linneberg, Allan, Bottinger, Erwin, Padmanabhan, Sandosh, Psaty, Bruce, Sotoodehnia, Nona, Kolcic, Ivana, Arnar, David, Gudbjartsson, Daniel, Holm, Hilma, Balkau, Beverley, Silva, Claudia, Newton-Cheh, Christopher, Nikus, Kjell, Salo, Perttu, Mohlke, Karen, Peyser, Patricia, Schunkert, Heribert, Lorentzon, Mattias, Lahti, Jari, Rao, Dabeeru, Cornelis, Marilyn, Faul, Jessica, Smith, Jennifer, Stolarz-Skrzypek, Katarzyna, Bandinelli, Stefania, Concas, Maria, Sinagra, Gianfranco, Meitinger, Thomas, Waldenberger, Melanie, and Sinner, Moritz

Subjects
Humans, Cardiovascular Diseases, Risk Factors, Heart Rate, Genetic Predisposition to Disease, Atrial Fibrillation, Mendelian Randomization Analysis, Genome-Wide Association Study, Polymorphism, Single Nucleotide
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

8. Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits.

Author

Westerman, Kenneth E, Walker, Maura E, Gaynor, Sheila M, Wessel, Jennifer, DiCorpo, Daniel, Ma, Jiantao, Alonso, Alvaro, Aslibekyan, Stella, Baldridge, Abigail S, Bertoni, Alain G, Biggs, Mary L, Brody, Jennifer A, Chen, Yii-Der Ida, Dupuis, Joseé, Goodarzi, Mark O, Guo, Xiuqing, Hasbani, Natalie R, Heath, Adam, Hidalgo, Bertha, Irvin, Marguerite R, Johnson, W Craig, Kalyani, Rita R, Lange, Leslie, Lemaitre, Rozenn N, Liu, Ching-Ti, Liu, Simin, Moon, Jee-Young, Nassir, Rami, Pankow, James S, Pettinger, Mary, Raffield, Laura M, Rasmussen-Torvik, Laura J, Selvin, Elizabeth, Senn, Mackenzie K, Shadyab, Aladdin H, Smith, Albert V, Smith, Nicholas L, Steffen, Lyn, Talegakwar, Sameera, Taylor, Kent D, de Vries, Paul S, Wilson, James G, Wood, Alexis C, Yanek, Lisa R, Yao, Jie, Zheng, Yinan, Boerwinkle, Eric, Morrison, Alanna C, Fornage, Miriam, Russell, Tracy P, Psaty, Bruce M, Levy, Daniel, Heard-Costa, Nancy L, Ramachandran, Vasan S, Mathias, Rasika A, Arnett, Donna K, Kaplan, Robert, North, Kari E, Correa, Adolfo, Carson, April, Rotter, Jerome I, Rich, Stephen S, Manson, JoAnn E, Reiner, Alexander P, Kooperberg, Charles, Florez, Jose C, Meigs, James B, Merino, Jordi, Tobias, Deirdre K, Chen, Han, and Manning, Alisa K

Subjects
Biomedical and Clinical Sciences, Nutrition and Dietetics, Nutrition, Diabetes, Human Genome, Minority Health, Genetics, Cardiovascular, Health Disparities, Prevention, Precision Medicine, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Humans, Glycated Hemoglobin, Diet, Diabetes Mellitus, Eating, Guanine Nucleotide Dissociation Inhibitors, Genome-Wide Association Study, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences
Abstract

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.Article highlightsWe aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Published
2023

9. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

Published
2024
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10. Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies

Author

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

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

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

Published
2023

11. Whole genome sequence analysis of apparent treatment resistant hypertension status in participants from the Trans-Omics for Precision Medicine program.

Author

Armstrong, Nicole, Srinivasasainagendra, Vinodh, Ammous, Farah, Assimes, Themistocles, Beitelshees, Amber, Brody, Jennifer, Cade, Brian, Ida Chen, Yii-Der, Chen, Han, de Vries, Paul, Floyd, James, Franceschini, Nora, Guo, Xiuqing, Hellwege, Jacklyn, House, John, Hwu, Chii-Min, Kardia, Sharon, Lange, Ethan, Lange, Leslie, McDonough, Caitrin, Montasser, May, OConnell, Jeffrey, Shuey, Megan, Sun, Xiao, Tanner, Rikki, Wang, Zhe, Zhao, Wei, Carson, April, Edwards, Todd, Kelly, Tanika, Kenny, Eimear, Kooperberg, Charles, Loos, Ruth, Morrison, Alanna, Motsinger-Reif, Alison, Psaty, Bruce, Rao, Dabeeru, Redline, Susan, Rich, Stephen, Rotter, Jerome, Smith, Jennifer, Smith, Albert, Irvin, Marguerite, and Arnett, Donna

Subjects
TOPMed, antihypertensive response, blood pressure, treatment resistant hypertension, whole genome sequencing
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 Centers 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

12. Coronary heart disease and ischemic stroke polygenic risk scores and atherosclerotic cardiovascular disease in a diverse, population-based cohort study.

Author

Bebo, Allison, Jarmul, Jamie A, Pletcher, Mark J, Hasbani, Natalie R, Couper, David, Nambi, Vijay, Ballantyne, Christie M, Fornage, Myriam, Morrison, Alanna C, Avery, Christy L, and de Vries, Paul S

Subjects
Humans, Cardiovascular Diseases, Coronary Disease, Diabetes Mellitus, Type 2, Risk Assessment, Risk Factors, Cohort Studies, Atherosclerosis, Plaque, Atherosclerotic, Carotid Intima-Media Thickness, Ischemic Stroke, Heart Disease - Coronary Heart Disease, Prevention, Stroke, Clinical Research, Cardiovascular, Patient Safety, Aging, Heart Disease, Good Health and Well Being, General Science & Technology
Abstract

The predictive ability of coronary heart disease (CHD) and ischemic stroke (IS) polygenic risk scores (PRS) have been evaluated individually, but whether they predict the combined outcome of atherosclerotic cardiovascular disease (ASCVD) remains insufficiently researched. It is also unclear whether associations of the CHD and IS PRS with ASCVD are independent of subclinical atherosclerosis measures. 7,286 White and 2,016 Black participants from the population-based Atherosclerosis Risk in Communities study who were free of cardiovascular disease and type 2 diabetes at baseline were included. We computed previously validated CHD and IS PRS consisting of 1,745,179 and 3,225,583 genetic variants, respectively. Cox proportional hazards models were used to test the association between each PRS and ASCVD, adjusting for traditional risk factors, ankle-brachial index, carotid intima media thickness, and carotid plaque. The hazard ratios (HR) for the CHD and IS PRS were significant with HR of 1.50 (95% CI: 1.36-1.66) and 1.31 (95% CI: 1.18-1.45) respectively for the risk of incident ASCVD per standard deviation increase in CHD and IS PRS among White participants after adjusting for traditional risk factors. The HR for the CHD PRS was not significant with an HR of 0.95 (95% CI: 0.79-1.13) for the risk of incident ASCVD in Black participants. The HR for the IS PRS was significant with an HR of 1.26 (95%CI: 1.05-1.51) for the risk of incident ASCVD in Black participants. The association of the CHD and IS PRS with ASCVD was not attenuated in White participants after adjustment for ankle-brachial index, carotid intima media thickness, and carotid plaque. The CHD and IS PRS do not cross-predict well, and predict better the outcome for which they were created than the composite ASCVD outcome. Thus, the use of the composite outcome of ASCVD may not be ideal for genetic risk prediction.

Published
2023

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

Author

Elgart, Michael, Goodman, Matthew O, Isasi, Carmen, Chen, Han, Morrison, Alanna C, de Vries, Paul S, Xu, Huichun, Manichaikul, Ani W, Guo, Xiuqing, Franceschini, Nora, Psaty, Bruce M, Rich, Stephen S, Rotter, Jerome I, Lloyd-Jones, Donald M, Fornage, Myriam, Correa, Adolfo, Heard-Costa, Nancy L, Vasan, Ramachandran S, Hernandez, Ryan, Kaplan, Robert C, Redline, Susan, Consortium, The Trans-Omics for Precision Medicine, and Sofer, Tamar

Subjects
Biomedical and Clinical Sciences, Genetic Testing, Clinical Research, Genetics, Good Health and Well Being, Humans, Male, Female, Phenotype, Genetic Background, Trans-Omics for Precision Medicine (TOPMed) Consortium, Haseman-Elston regression, Hispanic Community Health Study/Study of Latinos, Trans-Omics in Precision Medicine, admixed population, genetic architecture, genetic background, genetic correlation, heritability, household correlation, multi-ethnic, Biomedical and clinical sciences
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

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

Author

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

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

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

Published
2022

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

Author

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

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

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

Published
2022

16. Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles

Author

Huffman, Jennifer E., Nicholas, Jayna, Hahn, Julie, Heath, Adam S., Raffield, Laura M., Yanek, Lisa R., Brody, Jennifer A., Thibord, Florian, Almasy, Laura, Bartz, Traci M., Bielak, Lawrence F., Bowler, Russell P., Carrasquilla, Germán D., Chasman, Daniel I., Chen, Ming-Huei, Emmert, David B., Ghanbari, Mohsen, Haessler, Jeffrey, Hottenga, Jouke-Jan, Kleber, Marcus E., Le, Ngoc-Quynh, Lee, Jiwon, Lewis, Joshua P., Li-Gao, Ruifang, Luan, Jian'an, Malmberg, Anni, Mangino, Massimo, Marioni, Riccardo E., Martinez-Perez, Angel, Pankratz, Nathan, Polasek, Ozren, Richmond, Anne, Rodriguez, Benjamin A. T., Rotter, Jerome I., Steri, Maristella, Suchon, Pierre, Trompet, Stella, Weiss, Stefan, Zare, Marjan, Auer, Paul, Cho, Michael H., Christofidou, Paraskevi, Davies, Gail, de Geus, Eco, Deleuze, Jean-François, Delgado, Graciela E., Ekunwe, Lynette, Faraday, Nauder, Gögele, Martin, Greinacher, Andreas, Gao, He, Howard, Tom, Joshi, Peter K., Kilpeläinen, Tuomas O., Lahti, Jari, Linneberg, Allan, Naitza, Silvia, Noordam, Raymond, Paüls-Vergés, Ferran, Rich, Stephen S., Rosendaal, Frits R., Rudan, Igor, Ryan, Kathleen A., Souto, Juan Carlos, van Rooij, Frank J. A., Wang, Heming, Zhao, Wei, Becker, Lewis C., Beswick, Andrew, Brown, Michael R., Cade, Brian E., Campbell, Harry, Cho, Kelly, Crapo, James D., Curran, Joanne E., de Maat, Moniek P. M., Doyle, Margaret, Elliott, Paul, Floyd, James S., Fuchsberger, Christian, Grarup, Niels, Guo, Xiuqing, Harris, Sarah E., Hou, Lifang, Kolcic, Ivana, Kooperberg, Charles, Menni, Cristina, Nauck, Matthias, O'Connell, Jeffrey R., Orrù, Valeria, Psaty, Bruce M., Räikkönen, Katri, Smith, Jennifer A., Soria, Jose Manuel, Stott, David J., van Hylckama Vlieg, Astrid, Watkins, Hugh, Willemsen, Gonneke, Wilson, Peter W. F., Ben-Shlomo, Yoav, Blangero, John, Boomsma, Dorret, Cox, Simon R., Dehghan, Abbas, Eriksson, Johan G., Fiorillo, Edoardo, Fornage, Myriam, Hansen, Torben, Hayward, Caroline, Ikram, M. Arfan, Jukema, J. Wouter, Kardia, Sharon L. R., Lange, Leslie A., März, Winfried, Mathias, Rasika A., Mitchell, Braxton D., Mook-Kanamori, Dennis O., Morange, Pierre-Emmanuel, Pedersen, Oluf, Pramstaller, Peter P., Redline, Susan, Reiner, Alexander, Ridker, Paul M., Silverman, Edwin K., Spector, Tim D., Völker, Uwe, Wareham, Nicholas J., Wilson, James F., Yao, Jie, Trégouët, David-Alexandre, Johnson, Andrew D., Wolberg, Alisa S., de Vries, Paul S., Sabater-Lleal, Maria, Morrison, Alanna C., and Smith, Nicholas L.

Published
2024
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18. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension

Author

Kelly, Tanika N, Sun, Xiao, He, Karen Y, Brown, Michael R, Taliun, Sarah A Gagliano, Hellwege, Jacklyn N, Irvin, Marguerite R, Mi, Xuenan, Brody, Jennifer A, Franceschini, Nora, Guo, Xiuqing, Hwang, Shih-Jen, de Vries, Paul S, Gao, Yan, Moscati, Arden, Nadkarni, Girish N, Yanek, Lisa R, Elfassy, Tali, Smith, Jennifer A, Chung, Ren-Hua, Beitelshees, Amber L, Patki, Amit, Aslibekyan, Stella, Blobner, Brandon M, Peralta, Juan M, Assimes, Themistocles L, Palmas, Walter R, Liu, Chunyu, Bress, Adam P, Huang, Zhijie, Becker, Lewis C, Hwa, Chii-Min, O’Connell, Jeffrey R, Carlson, Jenna C, Warren, Helen R, Das, Sayantan, Giri, Ayush, Martin, Lisa W, Johnson, W Craig, Fox, Ervin R, Bottinger, Erwin P, Razavi, Alexander C, Vaidya, Dhananjay, Chuang, Lee-Ming, Chang, Yen-Pei C, Naseri, Take, Jain, Deepti, Kang, Hyun Min, Hung, Adriana M, Srinivasasainagendra, Vinodh, Snively, Beverly M, Gu, Dongfeng, Montasser, May E, Reupena, Muagututi A Sefuiva, Heavner, Benjamin D, LeFaive, Jonathon, Hixson, James E, Rice, Kenneth M, Wang, Fei Fei, Nielsen, Jonas B, Huang, Jianfeng, Khan, Alyna T, Zhou, Wei, Nierenberg, Jovia L, Laurie, Cathy C, Armstrong, Nicole D, Shi, Mengyao, Pan, Yang, Stilp, Adrienne M, Emery, Leslie, Wong, Quenna, Hawley, Nicola L, Minster, Ryan L, Curran, Joanne E, Munroe, Patricia B, Weeks, Daniel E, North, Kari E, Tracy, Russell P, Kenny, Eimear E, Shimbo, Daichi, Chakravarti, Aravinda, Rich, Stephen S, Reiner, Alex P, Blangero, John, Redline, Susan, Mitchell, Braxton D, Rao, Dabeeru C, Chen, Yii-Der Ida, Kardia, Sharon LR, Kaplan, Robert C, Mathias, Rasika A, He, Jiang, Psaty, Bruce M, Fornage, Myriam, Loos, Ruth JF, Correa, Adolfo, Boerwinkle, Eric, Rotter, Jerome I, Kooperberg, Charles, and Edwards, Todd L

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Good Health and Well Being, Blood Pressure, Genome-Wide Association Study, Genomics, Humans, Hypertension, Polymorphism, Single Nucleotide, Precision Medicine, allele, blood pressure, genome, hypertension, whole genome sequencing, Samoan Obesity, Lifestyle, and Genetic Adaptations Study (OLaGA) Group, ‡ NHLBI Trans-Omics for Precision Medicine TOPMed) Consortium, Cardiorespiratory Medicine and Haematology, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundThe 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.MethodsWe 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.ResultsTwo blood pressure signals achieved genome-wide significance in meta-analyses of stage-1 and stage-2 single variant findings (P

Published
2022

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

Author

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

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

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

Published
2022

20. Elucidating mechanisms of genetic cross-disease associations at the PROCR vascular disease locus

Author

Stacey, David, Chen, Lingyan, Stanczyk, Paulina J, Howson, Joanna MM, Mason, Amy M, Burgess, Stephen, MacDonald, Stephen, Langdown, Jonathan, McKinney, Harriett, Downes, Kate, Farahi, Neda, Peters, James E, Basu, Saonli, Pankow, James S, Tang, Weihong, Pankratz, Nathan, Sabater-Lleal, Maria, de Vries, Paul S, Smith, Nicholas L, Gelinas, Amy D, Schneider, Daniel J, Janjic, Nebojsa, Samani, Nilesh J, Ye, Shu, Summers, Charlotte, Chilvers, Edwin R, Danesh, John, and Paul, Dirk S

Subjects
Biological Sciences, Genetics, Prevention, Cardiovascular, Atherosclerosis, Hematology, Heart Disease - Coronary Heart Disease, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Antigens, CD, Crosses, Genetic, Endothelial Cells, Endothelial Protein C Receptor, Humans, Protein C, Receptors, Cell Surface, Thrombosis, Venous Thromboembolism, CHARGE Hemostasis Working Group
Abstract

Many individual genetic risk loci have been associated with multiple common human diseases. However, the molecular basis of this pleiotropy often remains unclear. We present an integrative approach to reveal the molecular mechanism underlying the PROCR locus, associated with lower coronary artery disease (CAD) risk but higher venous thromboembolism (VTE) risk. We identify PROCR-p.Ser219Gly as the likely causal variant at the locus and protein C as a causal factor. Using genetic analyses, human recall-by-genotype and in vitro experimentation, we demonstrate that PROCR-219Gly increases plasma levels of (activated) protein C through endothelial protein C receptor (EPCR) ectodomain shedding in endothelial cells, attenuating leukocyte-endothelial cell adhesion and vascular inflammation. We also associate PROCR-219Gly with an increased pro-thrombotic state via coagulation factor VII, a ligand of EPCR. Our study, which links PROCR-219Gly to CAD through anti-inflammatory mechanisms and to VTE through pro-thrombotic mechanisms, provides a framework to reveal the mechanisms underlying similar cross-phenotype associations.

Published
2022

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

Author

DiCorpo, Daniel, LeClair, Jessica, Cole, Joanne B, Sarnowski, Chloé, Ahmadizar, Fariba, Bielak, Lawrence F, Blokstra, Anneke, Bottinger, Erwin P, Chaker, Layal, Chen, Yii-Der I, Chen, Ye, de Vries, Paul S, Faquih, Tariq, Ghanbari, Mohsen, Gudmundsdottir, Valborg, Guo, Xiuqing, Hasbani, Natalie R, Ibi, Dorina, Ikram, M Arfan, Kavousi, Maryam, Leonard, Hampton L, Leong, Aaron, Mercader, Josep M, Morrison, Alanna C, Nadkarni, Girish N, Nalls, Mike A, Noordam, Raymond, Preuss, Michael, Smith, Jennifer A, Trompet, Stella, Vissink, Petra, Yao, Jie, Zhao, Wei, Boerwinkle, Eric, Goodarzi, Mark O, Gudnason, Vilmundur, Jukema, J Wouter, Kardia, Sharon LR, Loos, Ruth JF, Liu, Ching-Ti, Manning, Alisa K, Mook-Kanamori, Dennis, Pankow, James S, Picavet, H Susan J, Sattar, Naveed, Simonsick, Eleanor M, Verschuren, WM Monique, van Dijk, Ko Willems, Florez, Jose C, Rotter, Jerome I, Meigs, James B, Dupuis, Josée, and Udler, Miriam S

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Genetics, Obesity, Heart Disease - Coronary Heart Disease, Liver Disease, Heart Disease, Cardiovascular, Digestive Diseases, Diabetes, Nutrition, Prevention, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Good Health and Well Being, Alleles, Cross-Sectional Studies, Diabetes Mellitus, Type 2, Genetic Loci, Humans, Pharmaceutical Preparations, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveType 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 methodsHere 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).ResultsDespite 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.ConclusionsOur findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.

Published
2022

22. A multitrait genetic study of hemostatic factors and hemorrhagic transformation after stroke treatment

Author

Dehghan, Abbas, Heath, Adam S., Morrison, Alanna C., Reiner, Alex P., Johnson, Andrew, Richmond, Anne, Peters, Annette, van Hylckama Vlieg, Astrid, McKnight, Barbara, Psaty, Bruce M., Hayward, Caroline, Ward-Caviness, Cavin, O’Donnell, Christopher, Chasman, Daniel, Strachan, David P., Tregouet, David A., Mook-Kanamori, Dennis, Gill, Dipender, Thibord, Florian, Asselbergs, Folkert W., Leebeek, Frank W.G., Rosendaal, Frits R., Davies, Gail, Homuth, Georg, Temprano, Gerard, Campbell, Harry, Taylor, Herman A., Bressler, Jan, Huffman, Jennifer E., Rotter, Jerome I., Yao, Jie, Wilson, James F., Bis, Joshua C., Hahn, Julie M., Desch, Karl C., Wiggins, Kerri L., Díez-Ahijado, Laia, Raffield, Laura M., Bielak, Lawrence F., Yanek, Lisa R., Kleber, Marcus E., Sabater-Lleal, Maria, Mueller, Martina, Kavousi, Maryam, Mangino, Massimo, Conomos, Matthew P., Liu, Melissa, Brown, Michael R., Jhun, Min-A, Chen, Ming-Huei, de Maat, Moniek P.M., Pankratz, Nathan, Smith, Nicholas L., Peyser, Patricia A., Elliot, Paul, de Vries, Paul S., Wei, Peng, Wild, Philipp S., Morange, Pierre E., van der Harst, Pim, Yang, Qiong, Marioni, Riccardo, Li, Ruifang, Damrauer, Scott M., Cox, Simon R., Trompet, Stella, Felix, Stephan B., Völker, Uwe, Tang, Weihong, Koenig, Wolfgang, Jukema, J. Wouter, Guo, Xiuqing, Gallego-Fabrega, Cristina, Temprano-Sagrera, Gerard, Cárcel-Márquez, Jara, Muiño, Elena, Cullell, Natalia, Lledós, Miquel, Llucià-Carol, Laia, Martin-Campos, Jesús M., Sobrino, Tomás, Castillo, José, Millán, Mònica, Muñoz-Narbona, Lucía, López-Cancio, Elena, Ribó, Marc, Alvarez-Sabin, Jose, Jiménez-Conde, Jordi, Roquer, Jaume, Tur, Silvia, Obach, Victor, Arenillas, Juan F., Segura, Tomas, Serrano-Heras, Gemma, Marti-Fabregas, Joan, Freijo-Guerrero, Marimar, Moniche, Francisco, Castellanos, Maria del Mar, and Fernández-Cadenas, Israel

Published
2024
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23. Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Author

Little, Amarise, Hu, Yao, Sun, Quan, Jain, Deepti, Broome, Jai, Chen, Ming-Huei, Thibord, Florian, McHugh, Caitlin, Surendran, Praveen, Blackwell, Thomas W, Brody, Jennifer A, Bhan, Arunoday, Chami, Nathalie, de Vries, Paul S, Ekunwe, Lynette, Heard-Costa, Nancy, Hobbs, Brian D, Manichaikul, Ani, Moon, Jee-Young, Preuss, Michael H, Ryan, Kathleen, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Abecasis, Goncalo R, Almasy, Laura, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Choquet, Hélène, Correa, Adolfo, Curran, Joanne E, Faraday, Nauder, Fornage, Myriam, Glahn, David C, Hou, Lifang, Jorgenson, Eric, Kooperberg, Charles, Lewis, Joshua P, Lloyd-Jones, Donald M, Loos, Ruth JF, Min, Yuan-I, Mitchell, Braxton D, Morrison, Alanna C, Nickerson, Deborah A, North, Kari E, O'Connell, Jeffrey R, Pankratz, Nathan, Psaty, Bruce M, Vasan, Ramachandran S, Rich, Stephen S, Rotter, Jerome I, Smith, Albert V, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Conomos, Matthew P, Laurie, Cecelia A, Mathias, Rasika A, Li, Yun, Auer, Paul L, Consortium, NHLBI Trans-Omics for Precision Medicine, Thornton, Timothy, Reiner, Alexander P, Johnson, Andrew D, and Raffield, Laura M

Subjects
Biological Sciences, Genetics, Hematology, Biotechnology, Clinical Research, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Blood Platelets, Genome-Wide Association Study, Humans, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Precision Medicine, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Genetics & Heredity
Abstract

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Published
2022

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

Author

Selvaraj, Margaret Sunitha, Li, Xihao, Li, Zilin, Pampana, Akhil, Zhang, David Y, Park, Joseph, Aslibekyan, Stella, Bis, Joshua C, Brody, Jennifer A, Cade, Brian E, Chuang, Lee-Ming, Chung, Ren-Hua, Curran, Joanne E, de las Fuentes, Lisa, de Vries, Paul S, Duggirala, Ravindranath, Freedman, Barry I, Graff, Mariaelisa, Guo, Xiuqing, Heard-Costa, Nancy, Hidalgo, Bertha, Hwu, Chii-Min, Irvin, Marguerite R, Kelly, Tanika N, Kral, Brian G, Lange, Leslie, Li, Xiaohui, Lisa, Martin, Lubitz, Steven A, Manichaikul, Ani W, Michael, Preuss, Montasser, May E, Morrison, Alanna C, Naseri, Take, O’Connell, Jeffrey R, Palmer, Nicholette D, Peyser, Patricia A, Reupena, Muagututia S, Smith, Jennifer A, Sun, Xiao, Taylor, Kent D, Tracy, Russell P, Tsai, Michael Y, Wang, Zhe, Wang, Yuxuan, Bao, Wei, Wilkins, John T, Yanek, Lisa R, Zhao, Wei, Arnett, Donna K, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Chen, Yii-Der Ida, Correa, Adolfo, Cupples, L Adrienne, Dutcher, Susan K, Ellinor, Patrick T, Fornage, Myriam, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, He, Jiang, Kaplan, Robert C, Kardia, Sharon LR, Kim, Ryan, Kooperberg, Charles, Loos, Ruth JF, Viaud-Martinez, Karine A, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Nickerson, Deborah, North, Kari E, Psaty, Bruce M, Redline, Susan, Reiner, Alexander P, Vasan, Ramachandran S, Rich, Stephen S, Willer, Cristen, Rotter, Jerome I, Rader, Daniel J, Lin, Xihong, Peloso, Gina M, and Natarajan, Pradeep

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Biotechnology, Heart Disease - Coronary Heart Disease, Cardiovascular, Heart Disease, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Alleles, Cholesterol, LDL, Genome-Wide Association Study, Humans, Lipids, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium
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
2022

25. Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations

Author

Elgart, Michael, Lyons, Genevieve, Romero-Brufau, Santiago, Kurniansyah, Nuzulul, Brody, Jennifer A, Guo, Xiuqing, Lin, Henry J, Raffield, Laura, Gao, Yan, Chen, Han, de Vries, Paul, Lloyd-Jones, Donald M, Lange, Leslie A, Peloso, Gina M, Fornage, Myriam, Rotter, Jerome I, Rich, Stephen S, Morrison, Alanna C, Psaty, Bruce M, Levy, Daniel, Redline, Susan, and Sofer, Tamar

Subjects
Biological Sciences, Aetiology, 2.1 Biological and endogenous factors, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide, NHLBI’s Trans-Omics in Precision Medicine (TOPMed) Consortium, Biological sciences, Biomedical and clinical sciences
Abstract

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

Published
2022

26. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program

Author

Wheeler, Marsha M, Stilp, Adrienne M, Rao, Shuquan, Halldórsson, Bjarni V, Beyter, Doruk, Wen, Jia, Mihkaylova, Anna V, McHugh, Caitlin P, Lane, John, Jiang, Min-Zhi, Raffield, Laura M, Jun, Goo, Sedlazeck, Fritz J, Metcalf, Ginger, Yao, Yao, Bis, Joshua B, Chami, Nathalie, de Vries, Paul S, Desai, Pinkal, Floyd, James S, Gao, Yan, Kammers, Kai, Kim, Wonji, Moon, Jee-Young, Ratan, Aakrosh, Yanek, Lisa R, Almasy, Laura, Becker, Lewis C, Blangero, John, Cho, Michael H, Curran, Joanne E, Fornage, Myriam, Kaplan, Robert C, Lewis, Joshua P, Loos, Ruth JF, Mitchell, Braxton D, Morrison, Alanna C, Preuss, Michael, Psaty, Bruce M, Rich, Stephen S, Rotter, Jerome I, Tang, Hua, Tracy, Russell P, Boerwinkle, Eric, Abecasis, Goncalo R, Blackwell, Thomas W, Smith, Albert V, Johnson, Andrew D, Mathias, Rasika A, Nickerson, Deborah A, Conomos, Matthew P, Li, Yun, Þorsteinsdóttir, Unnur, Magnússon, Magnús K, Stefansson, Kari, Pankratz, Nathan D, Bauer, Daniel E, Auer, Paul L, and Reiner, Alex P

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Hematology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Good Health and Well Being, Humans, Genome-Wide Association Study, Whole Genome Sequencing, Blood Cells
Abstract

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Published
2022

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

Author

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

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

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

Published
2022

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

Author

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

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

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

Published
2021

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

Author

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

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

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

Published
2021

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

Author

Mikhaylova, Anna V, McHugh, Caitlin P, Polfus, Linda M, Raffield, Laura M, Boorgula, Meher Preethi, Blackwell, Thomas W, Brody, Jennifer A, Broome, Jai, Chami, Nathalie, Chen, Ming-Huei, Conomos, Matthew P, Cox, Corey, Curran, Joanne E, Daya, Michelle, Ekunwe, Lynette, Glahn, David C, Heard-Costa, Nancy, Highland, Heather M, Hobbs, Brian D, Ilboudo, Yann, Jain, Deepti, Lange, Leslie A, Miller-Fleming, Tyne W, Min, Nancy, Moon, Jee-Young, Preuss, Michael H, Rosen, Jonathon, Ryan, Kathleen, Smith, Albert V, Sun, Quan, Surendran, Praveen, de Vries, Paul S, Walter, Klaudia, Wang, Zhe, Wheeler, Marsha, Yanek, Lisa R, Zhong, Xue, Abecasis, Goncalo R, Almasy, Laura, Barnes, Kathleen C, Beaty, Terri H, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Butterworth, Adam S, Chavan, Sameer, Cho, Michael H, Choquet, Hélène, Correa, Adolfo, Cox, Nancy, DeMeo, Dawn L, Faraday, Nauder, Fornage, Myriam, Gerszten, Robert E, Hou, Lifang, Johnson, Andrew D, Jorgenson, Eric, Kaplan, Robert, Kooperberg, Charles, Kundu, Kousik, Laurie, Cecelia A, Lettre, Guillaume, Lewis, Joshua P, Li, Bingshan, Li, Yun, Lloyd-Jones, Donald M, Loos, Ruth JF, Manichaikul, Ani, Meyers, Deborah A, Mitchell, Braxton D, Morrison, Alanna C, Ngo, Debby, Nickerson, Deborah A, Nongmaithem, Suraj, North, Kari E, O’Connell, Jeffrey R, Ortega, Victor E, Pankratz, Nathan, Perry, James A, Psaty, Bruce M, Rich, Stephen S, Soranzo, Nicole, Rotter, Jerome I, Silverman, Edwin K, Smith, Nicholas L, Tang, Hua, Tracy, Russell P, Thornton, Timothy A, Vasan, Ramachandran S, Zein, Joe, Mathias, Rasika A, Consortium, NHLBI Trans-Omics for Precision Medicine, Reiner, Alexander P, and Auer, Paul L

Subjects
Human Genome, Genetics, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Good Health and Well Being, Asthma, Biomarkers, Dermatitis, Atopic, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Leukocytes, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Polymorphism, Single Nucleotide, Prognosis, Proteome, Pulmonary Disease, Chronic Obstructive, Quantitative Trait Loci, United Kingdom, United States, Whole Genome Sequencing, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, blood-cell counts, whole-genome sequencing, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
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

32. FGL1 as a modulator of plasma D‐dimer levels: Exome‐wide marker analysis of plasma tPA, PAI‐1, and D‐dimer

Author

Thibord, Florian, Song, Ci, Pattee, Jack, Rodriguez, Benjamin AT, Chen, Ming‐Huei, O’Donnell, Christopher J, Kleber, Marcus E, Delgado, Graciela E, Guo, Xiuqing, Yao, Jie, Taylor, Kent D, Ozel, Ayse Bilge, Brody, Jennifer A, McKnight, Barbara, Gyorgy, Beata, Simonsick, Eleanor, Leonard, Hampton L, Carrasquilla, Germán D, Guindo‐Martinez, Marta, Silveira, Angela, Temprano‐Sagrera, Gerard, Yanek, Lisa R, Becker, Diane M, Mathias, Rasika A, Becker, Lewis C, Raffield, Laura M, Kilpeläinen, Tuomas O, Grarup, Niels, Pedersen, Oluf, Hansen, Torben, Linneberg, Allan, Hamsten, Anders, Watkins, Hugh, Sabater‐Lleal, Maria, Nalls, Mike A, Trégouët, David‐Alexandre, Morange, Pierre‐Emmanuel, Psaty, Bruce M, Tracy, Russel P, Smith, Nicholas L, Desch, Karl C, Cushman, Mary, Rotter, Jerome I, de Vries, Paul S, Pankratz, Nathan D, Folsom, Aaron R, Morrison, Alanna C, März, Winfried, Tang, Weihong, and Johnson, Andrew D

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Hematology, Genetics, Human Genome, Prevention, Exome, Fibrin Fibrinogen Degradation Products, Fibrinogen, Fibrinolysis, Humans, Plasminogen Activator Inhibitor 1, Tissue Plasminogen Activator, computational biology, exome, fibrinogen, fibrinolysis, genetic association study, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundUse of targeted exome-arrays with common, rare variants and functionally enriched variation has led to discovery of new genes contributing to population variation in risk factors. Plasminogen activator-inhibitor 1 (PAI-1), tissue plasminogen activator (tPA), and the plasma product D-dimer are important components of the fibrinolytic system. There have been few large-scale genome-wide or exome-wide studies of PAI-1, tPA, and D-dimer.ObjectivesWe sought to discover new genetic loci contributing to variation in these traits using an exome-array approach.MethodsCohort-level analyses and fixed effects meta-analyses of PAI-1 (n = 15 603), tPA (n = 6876,) and D-dimer (n = 19 306) from 12 cohorts of European ancestry with diverse study design were conducted, including single-variant analyses and gene-based burden testing.ResultsFive variants located in NME7, FGL1, and the fibrinogen locus, all associated with D-dimer levels, achieved genome-wide significance (P

Published
2021

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

Author

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

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

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

Published
2021

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

Author

Hu, Yao, Stilp, Adrienne M, McHugh, Caitlin P, Rao, Shuquan, Jain, Deepti, Zheng, Xiuwen, Lane, John, de Bellefon, Sébastian Méric, Raffield, Laura M, Chen, Ming-Huei, Yanek, Lisa R, Wheeler, Marsha, Yao, Yao, Ren, Chunyan, Broome, Jai, Moon, Jee-Young, de Vries, Paul S, Hobbs, Brian D, Sun, Quan, Surendran, Praveen, Brody, Jennifer A, Blackwell, Thomas W, Choquet, Hélène, Ryan, Kathleen, Duggirala, Ravindranath, Heard-Costa, Nancy, Wang, Zhe, Chami, Nathalie, Preuss, Michael H, Min, Nancy, Ekunwe, Lynette, Lange, Leslie A, Cushman, Mary, Faraday, Nauder, Curran, Joanne E, Almasy, Laura, Kundu, Kousik, Smith, Albert V, Gabriel, Stacey, Rotter, Jerome I, Fornage, Myriam, Lloyd-Jones, Donald M, Vasan, Ramachandran S, Smith, Nicholas L, North, Kari E, Boerwinkle, Eric, Becker, Lewis C, Lewis, Joshua P, Abecasis, Goncalo R, Hou, Lifang, O’Connell, Jeffrey R, Morrison, Alanna C, Beaty, Terri H, Kaplan, Robert, Correa, Adolfo, Blangero, John, Jorgenson, Eric, Psaty, Bruce M, Kooperberg, Charles, Walton, Russell T, Kleinstiver, Benjamin P, Tang, Hua, Loos, Ruth JF, Soranzo, Nicole, Butterworth, Adam S, Nickerson, Debbie, Rich, Stephen S, Mitchell, Braxton D, Johnson, Andrew D, Auer, Paul L, Li, Yun, Mathias, Rasika A, Lettre, Guillaume, Pankratz, Nathan, Laurie, Cathy C, Laurie, Cecelia A, Bauer, Daniel E, Conomos, Matthew P, Reiner, Alexander P, and Consortium, NHLBI Trans-Omics for Precision Medicine

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Biotechnology, Clinical Research, Hematology, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adult, Aged, Chromosomes, Human, Pair 16, Datasets as Topic, Erythrocytes, Female, Gene Editing, Genetic Variation, Genome-Wide Association Study, HEK293 Cells, Humans, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Quality Control, Reproducibility of Results, United States, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, base editing, red blood cell traits, whole-genome sequencing, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

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

35. Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices.

Author

Natarajan, Pradeep, Pampana, Akhil, Graham, Sarah E, Ruotsalainen, Sanni E, Perry, James A, de Vries, Paul S, Broome, Jai G, Pirruccello, James P, Honigberg, Michael C, Aragam, Krishna, Wolford, Brooke, Brody, Jennifer A, Antonacci-Fulton, Lucinda, Arden, Moscati, Aslibekyan, Stella, Assimes, Themistocles L, Ballantyne, Christie M, Bielak, Lawrence F, Bis, Joshua C, Cade, Brian E, Do, Ron, Doddapaneni, Harsha, Emery, Leslie S, Hung, Yi-Jen, Irvin, Marguerite R, Khan, Alyna T, Lange, Leslie, Lee, Jiwon, Lemaitre, Rozenn N, Martin, Lisa W, Metcalf, Ginger, Montasser, May E, Moon, Jee-Young, Muzny, Donna, O'Connell, Jeffrey R, Palmer, Nicholette D, Peralta, Juan M, Peyser, Patricia A, Stilp, Adrienne M, Tsai, Michael, Wang, Fei Fei, Weeks, Daniel E, Yanek, Lisa R, Wilson, James G, Abecasis, Goncalo, Arnett, Donna K, Becker, Lewis C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Chang, Yi-Cheng, Chen, Yii-Der I, Choi, Won Jung, Correa, Adolfo, Curran, Joanne E, Daly, Mark J, Dutcher, Susan K, Ellinor, Patrick T, Fornage, Myriam, Freedman, Barry I, Gabriel, Stacey, Germer, Soren, Gibbs, Richard A, He, Jiang, Hveem, Kristian, Jarvik, Gail P, Kaplan, Robert C, Kardia, Sharon LR, Kenny, Eimear, Kim, Ryan W, Kooperberg, Charles, Laurie, Cathy C, Lee, Seonwook, Lloyd-Jones, Don M, Loos, Ruth JF, Lubitz, Steven A, Mathias, Rasika A, Martinez, Karine A Viaud, McGarvey, Stephen T, Mitchell, Braxton D, Nickerson, Deborah A, North, Kari E, Palotie, Aarno, Park, Cheol Joo, Psaty, Bruce M, Rao, DC, Redline, Susan, Reiner, Alexander P, Seo, Daekwan, Seo, Jeong-Sun, Smith, Albert V, Tracy, Russell P, Vasan, Ramachandran S, Kathiresan, Sekar, Cupples, L Adrienne, Rotter, Jerome I, Morrison, Alanna C, Rich, Stephen S, Ripatti, Samuli, and Willer, Cristen

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, FinnGen, Subcutaneous Tissue, Chromosomes, Human, X, Humans, Genetic Predisposition to Disease, Lipids, Eye Proteins, Nerve Tissue Proteins, Gene Expression Regulation, Genotype, Polymorphism, Single Nucleotide, Middle Aged, Female, Male, Genetic Loci, Genetic Association Studies, Whole Genome Sequencing, Phenomics, Cardiometabolic Risk Factors, Chromosomes, Human, X, Polymorphism, Single Nucleotide
Abstract

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10-72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10-4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10-5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids.

Published
2021

36. Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P., Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Daniel, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J.F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A., Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Pleiness, Jacob, Pollin, Toni, Post, Wendy, Powers Becker, Julia, Preethi Boorgula, Meher, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T., Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Williams, Scott, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Baojun, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Recto, Kathryn, Kachroo, Priyadarshini, Huan, Tianxiao, Lee, Gha Young, Bui, Helena, Lee, Dong Heon, Gereige, Jessica, Yao, Chen, Hwang, Shih-Jen, Joehanes, Roby, O’Connor, George T., and DeMeo, Dawn L.

Published
2023
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37. Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Author

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

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

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

Published
2021

38. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants

Author

Aragam, Krishna G., Jiang, Tao, Goel, Anuj, Kanoni, Stavroula, Wolford, Brooke N., Atri, Deepak S., Weeks, Elle M., Wang, Minxian, Hindy, George, Zhou, Wei, Grace, Christopher, Roselli, Carolina, Marston, Nicholas A., Kamanu, Frederick K., Surakka, Ida, Venegas, Loreto Muñoz, Sherliker, Paul, Koyama, Satoshi, Ishigaki, Kazuyoshi, Åsvold, Bjørn O., Brown, Michael R., Brumpton, Ben, de Vries, Paul S., Giannakopoulou, Olga, Giardoglou, Panagiota, Gudbjartsson, Daniel F., Güldener, Ulrich, Haider, Syed M. Ijlal, Helgadottir, Anna, Ibrahim, Maysson, Kastrati, Adnan, Kessler, Thorsten, Kyriakou, Theodosios, Konopka, Tomasz, Li, Ling, Ma, Lijiang, Meitinger, Thomas, Mucha, Sören, Munz, Matthias, Murgia, Federico, Nielsen, Jonas B., Nöthen, Markus M., Pang, Shichao, Reinberger, Tobias, Schnitzler, Gavin, Smedley, Damian, Thorleifsson, Gudmar, von Scheidt, Moritz, Ulirsch, Jacob C., Arnar, David O., Burtt, Noël P., Costanzo, Maria C., Flannick, Jason, Ito, Kaoru, Jang, Dong-Keun, Kamatani, Yoichiro, Khera, Amit V., Komuro, Issei, Kullo, Iftikhar J., Lotta, Luca A., Nelson, Christopher P., Roberts, Robert, Thorgeirsson, Gudmundur, Thorsteinsdottir, Unnur, Webb, Thomas R., Baras, Aris, Björkegren, Johan L. M., Boerwinkle, Eric, Dedoussis, George, Holm, Hilma, Hveem, Kristian, Melander, Olle, Morrison, Alanna C., Orho-Melander, Marju, Rallidis, Loukianos S., Ruusalepp, Arno, Sabatine, Marc S., Stefansson, Kari, Zalloua, Pierre, Ellinor, Patrick T., Farrall, Martin, Danesh, John, Ruff, Christian T., Finucane, Hilary K., Hopewell, Jemma C., Clarke, Robert, Gupta, Rajat M., Erdmann, Jeanette, Samani, Nilesh J., Schunkert, Heribert, Watkins, Hugh, Willer, Cristen J., Deloukas, Panos, Kathiresan, Sekar, and Butterworth, Adam S.

Published
2022
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39. Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

Author

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

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

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

Published
2020

40. Genetic loci associated with prevalent and incident myocardial infarction and coronary heart disease in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium

Author

Hahn, Julie, Fu, Yi-Ping, Brown, Michael R, Bis, Joshua C, de Vries, Paul S, Feitosa, Mary F, Yanek, Lisa R, Weiss, Stefan, Giulianini, Franco, Smith, Albert Vernon, Guo, Xiuqing, Bartz, Traci M, Becker, Diane M, Becker, Lewis C, Boerwinkle, Eric, Brody, Jennifer A, Chen, Yii-Der Ida, Franco, Oscar H, Grove, Megan, Harris, Tamara B, Hofman, Albert, Hwang, Shih-Jen, Kral, Brian G, Launer, Lenore J, Markus, Marcello RP, Rice, Kenneth M, Rich, Stephen S, Ridker, Paul M, Rivadeneira, Fernando, Rotter, Jerome I, Sotoodehnia, Nona, Taylor, Kent D, Uitterlinden, André G, Völker, Uwe, Völzke, Henry, Yao, Jie, Chasman, Daniel I, Dörr, Marcus, Gudnason, Vilmundur, Mathias, Rasika A, Post, Wendy, Psaty, Bruce M, Dehghan, Abbas, O’Donnell, Christopher J, and Morrison, Alanna C

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Heart Disease, Heart Disease - Coronary Heart Disease, Human Genome, Aging, Clinical Research, Cardiovascular, 2.4 Surveillance and distribution, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Coronary Artery Disease, Cross-Sectional Studies, Europe, Genetic Loci, Genome-Wide Association Study, Humans, Myocardial Infarction, Polymorphism, Single Nucleotide, Prospective Studies, White People, General Science & Technology
Abstract

BackgroundGenome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome.Methods and resultsUsing samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10-7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event.ConclusionThis study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.

Published
2020

41. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

Author

Kowalski, Madeline H, Qian, Huijun, Hou, Ziyi, Rosen, Jonathan D, Tapia, Amanda L, Shan, Yue, Jain, Deepti, Argos, Maria, Arnett, Donna K, Avery, Christy, Barnes, Kathleen C, Becker, Lewis C, Bien, Stephanie A, Bis, Joshua C, Blangero, John, Boerwinkle, Eric, Bowden, Donald W, Buyske, Steve, Cai, Jianwen, Cho, Michael H, Choi, Seung Hoan, Choquet, Hélène, Cupples, L Adrienne, Cushman, Mary, Daya, Michelle, de Vries, Paul S, Ellinor, Patrick T, Faraday, Nauder, Fornage, Myriam, Gabriel, Stacey, Ganesh, Santhi K, Graff, Misa, Gupta, Namrata, He, Jiang, Heckbert, Susan R, Hidalgo, Bertha, Hodonsky, Chani J, Irvin, Marguerite R, Johnson, Andrew D, Jorgenson, Eric, Kaplan, Robert, Kardia, Sharon LR, Kelly, Tanika N, Kooperberg, Charles, Lasky-Su, Jessica A, Loos, Ruth JF, Lubitz, Steven A, Mathias, Rasika A, McHugh, Caitlin P, Montgomery, Courtney, Moon, Jee-Young, Morrison, Alanna C, Palmer, Nicholette D, Pankratz, Nathan, Papanicolaou, George J, Peralta, Juan M, Peyser, Patricia A, Rich, Stephen S, Rotter, Jerome I, Silverman, Edwin K, Smith, Jennifer A, Smith, Nicholas L, Taylor, Kent D, Thornton, Timothy A, Tiwari, Hemant K, Tracy, Russell P, Wang, Tao, Weiss, Scott T, Weng, Lu-Chen, Wiggins, Kerri L, Wilson, James G, Yanek, Lisa R, Zöllner, Sebastian, North, Kari E, Auer, Paul L, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology & Hemostasis Working Group, Raffield, Laura M, Reiner, Alexander P, and Li, Yun

Subjects
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Hematology & Hemostasis Working Group, Humans, Genetic Predisposition to Disease, Computational Biology, Genetics, Population, Gene Frequency, Linkage Disequilibrium, Databases, Genetic, Adult, Aged, Aged, 80 and over, Middle Aged, African Americans, Hispanic Americans, United States, Female, Male, Genome-Wide Association Study, beta-Globins, Genotyping Techniques, Precision Medicine, Whole Genome Sequencing, Genetics, Population, Databases, Genetic, and over, Developmental Biology
Abstract

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count 86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.

Published
2019

42. Genome-wide meta-analysis of macronutrient intake of 91,114 European ancestry participants from the cohorts for heart and aging research in genomic epidemiology consortium

Author

Merino, Jordi, Dashti, Hassan S, Li, Sherly X, Sarnowski, Chloé, Justice, Anne E, Graff, Misa, Papoutsakis, Constantina, Smith, Caren E, Dedoussis, George V, Lemaitre, Rozenn N, Wojczynski, Mary K, Männistö, Satu, Ngwa, Julius S, Kho, Minjung, Ahluwalia, Tarunveer S, Pervjakova, Natalia, Houston, Denise K, Bouchard, Claude, Huang, Tao, Orho-Melander, Marju, Frazier-Wood, Alexis C, Mook-Kanamori, Dennis O, Pérusse, Louis, Pennell, Craig E, de Vries, Paul S, Voortman, Trudy, Li, Olivia, Kanoni, Stavroula, Rose, Lynda M, Lehtimäki, Terho, Zhao, Jing Hua, Feitosa, Mary F, Luan, Jian’an, McKeown, Nicola M, Smith, Jennifer A, Hansen, Torben, Eklund, Niina, Nalls, Mike A, Rankinen, Tuomo, Huang, Jinyan, Hernandez, Dena G, Schulz, Christina-Alexandra, Manichaikul, Ani, Li-Gao, Ruifang, Vohl, Marie-Claude, Wang, Carol A, van Rooij, Frank JA, Shin, Jean, Kalafati, Ioanna P, Day, Felix, Ridker, Paul M, Kähönen, Mika, Siscovick, David S, Langenberg, Claudia, Zhao, Wei, Astrup, Arne, Knekt, Paul, Garcia, Melissa, Rao, DC, Qi, Qibin, Ferrucci, Luigi, Ericson, Ulrika, Blangero, John, Hofman, Albert, Pausova, Zdenka, Mikkilä, Vera, Wareham, Nick J, Kardia, Sharon LR, Pedersen, Oluf, Jula, Antti, Curran, Joanne E, Zillikens, M Carola, Viikari, Jorma S, Forouhi, Nita G, Ordovás, José M, Lieske, John C, Rissanen, Harri, Uitterlinden, André G, Raitakari, Olli T, Kiefte-de Jong, Jessica C, Dupuis, Josée, Rotter, Jerome I, North, Kari E, Scott, Robert A, Province, Michael A, Perola, Markus, Cupples, L Adrienne, Turner, Stephen T, Sørensen, Thorkild IA, Salomaa, Veikko, Liu, Yongmei, Sung, Yun J, Qi, Lu, Bandinelli, Stefania, Rich, Stephen S, de Mutsert, Renée, Tremblay, Angelo, Oddy, Wendy H, Franco, Oscar H, and Paus, Tomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Obesity, Human Genome, Genetics, Prevention, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular, Metabolic and endocrine, Aged, Aging, Alpha-Ketoglutarate-Dependent Dioxygenase FTO, Cohort Studies, Energy Intake, Female, Fibroblast Growth Factors, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Genotype, Heart Diseases, Humans, Male, Membrane Proteins, Middle Aged, Nutrients, Polymorphism, Single Nucleotide, Receptors, Retinoic Acid, White People, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Macronutrient intake, the proportion of calories consumed from carbohydrate, fat, and protein, is an important risk factor for metabolic diseases with significant familial aggregation. Previous studies have identified two genetic loci for macronutrient intake, but incomplete coverage of genetic variation and modest sample sizes have hindered the discovery of additional loci. Here, we expanded the genetic landscape of macronutrient intake, identifying 12 suggestively significant loci (P

Published
2019

43. DNA methylation analysis is used to identify novel genetic loci associated with circulating fibrinogen levels in blood

Author

Hahn, Julie, Bressler, Jan, Domingo-Relloso, Arce, Chen, Ming-Huei, McCartney, Daniel L., Teumer, Alexander, van Dongen, Jenny, Kleber, Marcus E., Aïssi, Dylan, Swenson, Brenton R., Yao, Jie, Zhao, Wei, Huang, Jian, Xia, Yujing, Brown, Michael R., Costeira, Ricardo, de Geus, Eco J.C., Delgado, Graciela E., Dobson, Dre'Von A., Elliott, Paul, Grabe, Hans J., Guo, Xiuqing, Harris, Sarah E., Huffman, Jennifer E., Kardia, Sharon L.R., Liu, Yongmei, Lorkowski, Stefan, Marioni, Riccardo E., Nauck, Matthias, Ratliff, Scott M., Sabater-Lleal, Maria, Spector, Tim D., Suchon, Pierre, Taylor, Kent D., Thibord, Florian, Trégouët, David-Alexandre, Wiggins, Kerri L., Willemsen, Gonneke, Bell, Jordana T., Boomsma, Dorret I., Cole, Shelley A., Cox, Simon R., Dehghan, Abbas, Greinacher, Andreas, Haack, Karin, März, Winfried, Morange, Pierre-Emmanuel, Rotter, Jerome I., Sotoodehnia, Nona, Tellez-Plaza, Maria, Navas-Acien, Ana, Smith, Jennifer A., Johnson, Andrew D., Fornage, Myriam, Smith, Nicholas L., Wolberg, Alisa S., Morrison, Alanna C., and de Vries, Paul S.

Published
2023
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47. Gene-lifestyle interactions in the genomics of human complex traits

Author

Laville, Vincent, Majarian, Timothy, Sung, Yun J., Schwander, Karen, Feitosa, Mary F., Chasman, Daniel I., Bentley, Amy R., Rotimi, Charles N., Cupples, L. Adrienne, de Vries, Paul S., Brown, Michael R., Morrison, Alanna C., Kraja, Aldi T., Province, Mike, Gu, C. Charles, Gauderman, W. James, Rao, D. C., Manning, Alisa K., and Aschard, Hugues

Published
2022
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48. Antithrombin, PC (Protein C), and PS (Protein S): Genome and Transcriptome-Wide Association Studies Identify 7 Novel Loci Regulating Plasma Levels

Author

Ji, Yuekai, Temprano-Sagrera, Gerard, Holle, Lori A., Bebo, Allison, Brody, Jennifer, Le, Ngoc-Quynh, Kangro, Kadri, Brown, Michael R., Martinez-Perez, Angel, Sitlani, Colleen M., Suchon, Pierre, Kleber, Marcus E., Emmert, David B., Bilge Ozel, Ayse, Dobson, Dre’Von A., Tang, Weihong, Llobet, Dolors, Tracy, Russell P., Deleuze, Jean-François, Delgado, Graciela E., Gögele, Martin, Wiggins, Kerri L., Souto, Juan Carlos, Pankow, James S., Taylor, Kent D., Trégouët, David-Alexandre, Moissl, Angela P., Fuchsberger, Christian, Rosendaal, Frits R., Morrison, Alanna C., Soria, Jose Manuel, Cushman, Mary, Morange, Pierre-Emmanuel, März, Winfried, Hicks, Andrew A., Desch, Karl C., Johnson, Andrew D., de Vries, Paul S., Wolberg, Alisa S., Smith, Nicholas L., and Sabater-Lleal, Maria

Published
2023
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49. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Author

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

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

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

Published
2019

50. Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program

Author

Sarnowski, Chloé, Leong, Aaron, Raffield, Laura M, Wu, Peitao, de Vries, Paul S, DiCorpo, Daniel, Guo, Xiuqing, Xu, Huichun, Liu, Yongmei, Zheng, Xiuwen, Hu, Yao, Brody, Jennifer A, Goodarzi, Mark O, Hidalgo, Bertha A, Highland, Heather M, Jain, Deepti, Liu, Ching-Ti, Naik, Rakhi P, O’Connell, Jeffrey R, Perry, James A, Porneala, Bianca C, Selvin, Elizabeth, Wessel, Jennifer, Psaty, Bruce M, Curran, Joanne E, Peralta, Juan M, Blangero, John, Kooperberg, Charles, Mathias, Rasika, Johnson, Andrew D, Reiner, Alexander P, Mitchell, Braxton D, Cupples, L Adrienne, Vasan, Ramachandran S, Correa, Adolfo, Morrison, Alanna C, Boerwinkle, Eric, Rotter, Jerome I, Rich, Stephen S, Manning, Alisa K, Dupuis, Josée, Meigs, James B, Group, TOPMed Diabetes Working, Group, TOPMed Hematology Working, Group, TOPMed Hemostasis Working, and National Heart, Lung

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Prevention, Diabetes, Clinical Research, Metabolic and endocrine, Good Health and Well Being, Cohort Studies, Diabetes Mellitus, Female, Genetic Variation, Glycated Hemoglobin, Humans, Male, Polymorphism, Single Nucleotide, Population Groups, Precision Medicine, TOPMed Diabetes Working Group, TOPMed Hematology Working Group, TOPMed Hemostasis Working Group, National Heart, Lung, and Blood Institute TOPMed Consortium, The Trans-Omics for Precision Medicine (TOPMed) program, hemoglobin A1c, multi-ancestry sample, whole-genome sequence association analyses, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in individuals with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics, and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K and/or FN3KRP in Europeans, and G6PD in African-Americans and Hispanics) and we identified an African-ancestry-specific low-frequency variant (rs1039215 in HBG2 and HBE1, minor allele frequency (MAF) = 0.03). The most associated G6PD variant (rs1050828-T, p.Val98Met, MAF = 12% in African-Americans, MAF = 2% in Hispanics) lowered HbA1c (-0.88% in hemizygous males, -0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693, p.Leu353Pro, MAF = 0.5%; -0.98% in hemizygous males, -0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African American cohorts, and we replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.

Published
2019

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