Browsing by Author "Correa, Adolfo"

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    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
    (Springer Nature, 2022) 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 H. H.; 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; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Willer, Cristen J.; Natarajan, Pradeep; Peloso, Gina M.; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
    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.
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    A high-resolution HLA reference panel capturing global population diversity enables multi-ancestry fine-mapping in HIV host response
    (Springer Nature, 2021) Luo, Yang; Kanai, Masahiro; Choi, Wanson; Li, Xinyi; Sakaue, Saori; Yamamoto, Kenichi; Ogawa, Kotaro; Gutierrez-Arcelus, Maria; Gregersen, Peter K.; Stuart, Philip E.; Elder, James T.; Forer, Lukas; Schönherr, Sebastian; Fuchsberger, Christian; Smith, Albert V.; Fellay, Jacques; Carrington, Mary; Haas, David W.; Guo, Xiuqing; Palmer, Nicholette D.; Chen, Yii-Der Ida; Rotter, Jerome I.; Taylor, Kent D.; Rich, Stephen S.; Correa, Adolfo; Wilson, James G.; Kathiresan, Sekar; Cho, Michael H.; Metspalu, Andres; Esko, Tonu; Okada, Yukinori; Han, Buhm; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; McLaren, Paul J.; Raychaudhuri, Soumya; Obstetrics and Gynecology, School of Medicine
    Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large (n = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance.
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    Associations between air pollution indicators and prevalent and incident diabetes in an African American cohort, the Jackson Heart Study
    (Wolters Kluwer, 2021-04-22) Weaver, Anne M.; Bidulescu, Aurelian; Wellenius, Gregory A.; Hickson, DeMarc A.; Sims, Mario; Vaidyanathan, Ambarish; Wu, Wen-Chih; Correa, Adolfo; Wang, Yi; Environmental Health Science, School of Public Health
    Background: Diabetes is especially prevalent among African Americans. Prior studies suggest that long-term exposure to ambient air pollution may be associated with greater incidence of diabetes, but results remain heterogeneous. Few studies have included large numbers of African Americans. Methods: We assessed diabetes status and concentrations of 1- and 3-year fine particulate matter (PM2.5) and ozone (O3) among African American participants of the Jackson Heart Study at visits 1 (2000-2004, N = 5128) and 2 (2005-2008, N = 2839). We used mixed-effect modified Poisson regression to estimate risk ratios (RRs) and 95% confidence intervals (CIs) of incidence of diabetes by visit 2 and prevalence ratios (PRs) of the association between air pollution exposure and prevalent diabetes at visits 1 and 2. We adjusted for potential confounding by patient characteristics, as well as inverse probability weights of diabetes at visit 2, accounting for clustering by census tract. Results: We observed associations between incident diabetes and interquartile range increase in 1-year O3 (RR 1.34, 95% CI = 1.11, 1.61) and 3-year O3 (RR 0.88, 95% CI = 0.76, 1.02). We observed associations between prevalent diabetes and 1-year PM2.5 (PR 1.08, 95% CI = 1.00, 1.17), 1-year O3 (PR 1.18, 95% CI = 1.10, 1.27), and 3-year O3 (PR 0.95, 95% CI = 0.90, 1.01) at visit 2. Conclusions: Our results provide some evidence of positive associations between indicators of long-term PM2.5 and O3 exposure and diabetes. This study is particularly relevant to African Americans, who have higher prevalence of diabetes but relatively few studies of environmental pollution risk factors.
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    Impact of Rare and Common Genetic Variants on Diabetes Diagnosis by Hemoglobin A1c in Multi-Ancestry Cohorts: The Trans-Omics for Precision Medicine Program
    (Elsevier, 2019-09-26) 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.; TOPMed Diabetes Working Group; TOPMed Hematology Working Group; TOPMed Hemostasis Working Group; National Heart, Lung, and Blood Institute TOPMed Consortium; Epidemiology, School of Public Health
    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.
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    Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
    (American Diabetes Association, 2023) 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; Manning, Alisa K.; Epidemiology, Richard M. Fairbanks School of Public Health
    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 highlights: We 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.
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    Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility
    (Nature Publishing Group, 2015-01-29) Wessel, Jennifer; Chu, Audrey Y.; Willems, Sara M.; Wang, Shuai; Yaghootkar, Hanieh; Brody, Jennifer A.; Dauriz, Marco; Hivert, Marie-France; Raghavan, Sridharan; Lipovich, Leonard; Hidalgo, Bertha; Fox, Keolu; Huffman, Jennifer E.; An, Ping; Lu, Yingchang; Rasmussen-Torvik, Laura J.; Grarup, Niels; Ehm, Margaret G.; Li, Li; Baldridge, Abigail S.; Stančáková, Alena; Abrol, Ravinder; Besse, Céline; Boland, Anne; Bork-Jensen, Jette; Fornage, Myriam; Freitag, Daniel F.; Garcia, Melissa E.; Guo, Xiuqing; Hara, Kazuo; Isaacs, Aaron; Jakobsdottir, Johanna; Lange, Leslie A.; Layton, Jill C.; Li, Man; Hua Zhao, Jing; Meidtner, Karina; Morrison, Alanna C.; Nalls, Mike A.; Peters, Marjolein J.; Sabater-Lleal, Maria; Schurmann, Claudia; Silveira, Angela; Smith, Albert V.; Southam, Lorraine; Stoiber, Marcus H.; Strawbridge, Rona J.; Taylor, Kent D.; Varga, Tibor V.; Allin, Kristine H.; Amin, Najaf; Aponte, Jennifer L.; Aung, Tin; Barbieri, Caterina; Bihlmeyer, Nathan A.; Boehnke, Michael; Bombieri, Cristina; Bowden, Donald W.; Burns, Sean M.; Chen, Yuning; Chen, Yii-DerI; Cheng, Ching-Yu; Correa, Adolfo; Czajkowski, Jacek; Dehghan, Abbas; Ehret, Georg B.; Eiriksdottir, Gudny; Escher, Stefan A.; Farmaki, Aliki-Eleni; Frånberg, Mattias; Gambaro, Giovanni; Giulianini, Franco; Goddard, William A.; Goel, Anuj; Gottesman, Omri; Grove, Megan L.; Gustafsson, Stefan; Hai, Yang; Hallmans, Göran; Heo, Jiyoung; Hoffmann, Per; Ikram, Mohammad K.; Jensen, Richard A.; Jørgensen, Marit E.; Jørgensen, Torben; Karaleftheri, Maria; Khor, Chiea C.; Kirkpatrick, Andrea; Kraja, Aldi T.; Kuusisto, Johanna; Lange, Ethan M.; Lee, I. T.; Lee, Wen-Jane; Leong, Aaron; Liao, Jiemin; Liu, Chunyu; Liu, Yongmei; Lindgren, Cecilia M.; Linneberg, Allan; Malerba, Giovanni; Mamakou, Vasiliki; Marouli, Eirini; Maruthur, Nisa M.; Matchan, Angela; McKean-Cowdin, Roberta; McLeod, Olga; Metcalf, Ginger A.; Mohlke, Karen L.; Muzny, Donna M.; Ntalla, Ioanna; Palmer, Nicholette D.; Pasko, Dorota; Peter, Andreas; Rayner, Nigel W.; Renström, Frida; Rice, Ken; Sala, Cinzia F.; Sennblad, Bengt; Serafetinidis, Ioannis; Smith, Jennifer A.; Soranzo, Nicole; Speliotes, Elizabeth K.; Stahl, Eli A.; Stirrups, Kathleen; Tentolouris, Nikos; Thanopoulou, Anastasia; Torres, Mina; Traglia, Michela; Tsafantakis, Emmanouil; Javad, Sundas; Yanek, Lisa R.; Zengini, Eleni; Becker, Diane M.; Bis, Joshua C.; Brown, James B.; Adrienne Cupples, L.; Hansen, Torben; Ingelsson, Erik; Karter, Andrew J.; Lorenzo, Carlos; Mathias, Rasika A.; Norris, Jill M.; Peloso, Gina M.; Sheu, Wayne H.-H.; Toniolo, Daniela; Vaidya, Dhananjay; Varma, Rohit; Wagenknecht, Lynne E.; Boeing, Heiner; Bottinger, Erwin P.; Dedoussis, George; Deloukas, Panos; Ferrannini, Ele; Franco, Oscar H.; Franks, Paul W.; Gibbs, Richard A.; Gudnason, Vilmundur; Hamsten, Anders; Harris, Tamara B.; Hattersley, Andrew T.; Hayward, Caroline; Hofman, Albert; Jansson, Jan-Håkan; Langenberg, Claudia; Launer, Lenore J.; Levy, Daniel; Oostra, Ben A.; O'Donnell, Christopher J.; O'Rahilly, Stephen; Padmanabhan, Sandosh; Pankow, James S.; Polasek, Ozren; Province, Michael A.; Rich, Stephen S.; Ridker, Paul M.; Rudan, Igor; Schulze, Matthias B.; Smith, Blair H.; Uitterlinden, André G.; Walker, Mark; Watkins, Hugh; Wong, Tien Y.; Zeggini, Eleftheria; Laakso, Markku; Borecki, Ingrid B.; Chasman, Daniel I.; Pedersen, Oluf; Psaty, Bruce M.; Shyong Tai, E.; van Duijn, Cornelia M.; Wareham, Nicholas J.; Waterworth, Dawn M.; Boerwinkle, Eric; Linda Kao, W. H.; Florez, Jose C.; Loos, Ruth J. F.; Wilson, James G.; Frayling, Timothy M.; Siscovick, David S.; Dupuis, Josée; Rotter, Jerome I.; Meigs, James B.; Scott, Robert A.; Goodarzi, Mark O.; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Fasting ​glucose and ​insulin are intermediate traits for type 2 diabetes. Here we explore the role of coding variation on these traits by analysis of variants on the HumanExome BeadChip in 60,564 non-diabetic individuals and in 16,491 T2D cases and 81,877 controls. We identify a novel association of a low-frequency nonsynonymous SNV in ​GLP1R (A316T; rs10305492; MAF=1.4%) with lower FG (β=−0.09±0.01 mmol l−1, P=3.4 × 10−12), T2D risk (OR[95%CI]=0.86[0.76–0.96], P=0.010), early ​insulin secretion (β=−0.07±0.035 pmolinsulin mmolglucose−1, P=0.048), but higher 2-h ​glucose (β=0.16±0.05 mmol l−1, P=4.3 × 10−4). We identify a gene-based association with FG at ​G6PC2 (pSKAT=6.8 × 10−6) driven by four rare protein-coding SNVs (H177Y, Y207S, R283X and S324P). We identify rs651007 (MAF=20%) in the first intron of ​ABO at the putative promoter of an antisense lncRNA, associating with higher FG (β=0.02±0.004 mmol l−1, P=1.3 × 10−8). Our approach identifies novel coding variant associations and extends the allelic spectrum of variation underlying diabetes-related quantitative traits and T2D susceptibility.
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    Physical Activity, Inflammation, Coronary Artery Calcification, and Incident Coronary Heart Disease in African Americans: Insights From the Jackson Heart Study
    (Elsevier, 2021) Kamimura, Daisuke; Cain-Shields, Loretta R.; Clark, Donald, III.; Oshunbade, Adebamike A.; Ashley, Kellan E.; Guild, Cameron S.; Loprinzi, Paul D.; Newton, Robert; Blaha, Michael J.; Suzuki, Takeki; Butler, Javed; Hall, John E.; Correa, Adolfo; Hall, Michael E.; Medicine, School of Medicine
    Objective: To examine associations between physical activity, inflammation, coronary artery calcification and incident coronary heart disease in African Americans. Methods: Among Jackson Heart Study participants without prevalent coronary heart disease at baseline (n=4295), we examined the relationships between physical activity and high-sensitivity CRP, the presence of coronary artery calcification (Agatston score≥100), and incident coronary heart disease. Based on the American Heart Association’s Life’s Simple 7 metrics, participants were classified as having poor, intermediate or ideal physical activity. Results: After adjusting for possible confounding factors, ideal physical activity was associated with lower high-sensitivity CRP levels (β: −0.15, 95%CI −0.15, −0.002) and a lower prevalence of coronary artery calcification (odds ratio: 0.70, 95%CI 0.51, 0.96) compared with poor physical activity. Over a median of 12.8 years follow up, there were 164 incident coronary heart disease events (3.3/1000 person-years). Ideal physical activity was associated with a lower rate of incident coronary heart disease compared with poor physical activity (hazard ratio 0.55, 95% CI 0.31, 0.98). Conclusions: In a large community-based African American cohort, ideal physical activity was associated with lower inflammation levels, a lower prevalence of coronary artery calcification, and a lower rate of incident coronary heart disease. These findings suggest that promotion of ideal physical activity may be an important way to reduce the risk of subclinical and future clinical coronary heart disease in African Americans.
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    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies
    (Springer Nature, 2023) 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 H. H.; 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; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I.; Natarajan, Pradeep; Peloso, Gina M.; Li, Zilin; Lin, Xihong; Biostatistics and Health Data Science, School of Medicine
    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.
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    Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
    (Springer Nature, 2021) Taliun, Daniel; Harris, Daniel N.; Kessler, Michael D.; Carlson, Jedidiah; Szpiech, Zachary A.; Torres, Raul; Gagliano Taliun, Sarah A.; Corvelo, André; Gogarten, Stephanie M.; Kang, Hyun Min; Pitsillides, Achilleas N.; LeFaive, Jonathon; Lee, Seung-Been; Tian, Xiaowen; Browning, Brian L.; Das, Sayantan; Emde, Anne-Katrin; Clarke, Wayne E.; Loesch, Douglas P.; Shetty, Amol C.; Blackwell, Thomas W.; Smith, Albert V.; Wong, Quenna; Liu, Xiaoming; Conomos, Matthew P.; Bobo, Dean M.; Aguet, François; Albert, Christine; Alonso, Alvaro; Ardlie, Kristin G.; Arking, Dan E.; Aslibekyan, Stella; Auer, Paul L.; Barnard, John; Barr, R. Graham; Barwick, Lucas; Becker, Lewis C.; Beer, Rebecca L.; Benjamin, Emelia J.; Bielak, Lawrence F.; Blangero, John; Boehnke, Michael; Bowden, Donald W.; Brody, Jennifer A.; Burchard, Esteban G.; Cade, Brian E.; Casella, James F.; Chalazan, Brandon; Chasman, Daniel I.; Chen, Yii-Der Ida; Cho, Michael H.; Choi, Seung Hoan; Chung, Mina K.; Clish, Clary B.; Correa, Adolfo; Curran, Joanne E.; Custer, Brian; Darbar, Dawood; Daya, Michelle; de Andrade, Mariza; DeMeo, Dawn L.; Dutcher, Susan K.; Ellinor, Patrick T.; Emery, Leslie S.; Eng, Celeste; Fatkin, Diane; Fingerlin, Tasha; Forer, Lukas; Fornage, Myriam; Franceschini, Nora; Fuchsberger, Christian; Fullerton, Stephanie M.; Germer, Soren; Gladwin, Mark T.; Gottlieb, Daniel J.; Guo, Xiuqing; Hall, Michael E.; He, Jiang; Heard-Costa, Nancy L.; Heckbert, Susan R.; Irvin, Marguerite R.; Johnsen, Jill M.; Johnson, Andrew D.; Kaplan, Robert; Kardia, Sharon L. R.; Kelly, Tanika; Kelly, Shannon; Kenny, Eimear E.; Kiel, Douglas P.; Klemmer, Robert; Konkle, Barbara A.; Kooperberg, Charles; Köttgen, Anna; Lange, Leslie A.; Lasky-Su, Jessica; Levy, Daniel; Lin, Xihong; Lin, Keng-Han; Liu, Chunyu; Loos, Ruth J. F.; Garman, Lori; Gerszten, Robert; Lubitz, Steven A.; Lunetta, Kathryn L.; Mak, Angel C. Y.; Manichaikul, Ani; Manning, Alisa K.; Mathias, Rasika A.; McManus, David D.; McGarvey, Stephen T.; Meigs, James B.; Meyers, Deborah A.; Mikulla, Julie L.; Minear, Mollie A.; Mitchell, Braxton D.; Mohanty, Sanghamitra; Montasser, May E.; Montgomery, Courtney; Morrison, Alanna C.; Murabito, Joanne M.; Natale, Andrea; Natarajan, Pradeep; Nelson, Sarah C.; North, Kari E.; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Pankratz, Nathan; Peloso, Gina M.; Peyser, Patricia A.; Pleiness, Jacob; Post, Wendy S.; Psaty, Bruce M.; Rao, D. C.; Redline, Susan; Reiner, Alexander P.; Roden, Dan; Rotter, Jerome I.; Ruczinski, Ingo; Sarnowski, Chloé; Schoenherr, Sebastian; Schwartz, David A.; Seo, Jeong-Sun; Seshadri, Sudha; Sheehan, Vivien A.; Sheu, Wayne H.; Shoemaker, M. Benjamin; Smith, Nicholas L.; Smith, Jennifer A.; Sotoodehnia, Nona; Stilp, Adrienne M.; Tang, Weihong; Taylor, Kent D.; Telen, Marilyn; Thornton, Timothy A.; Tracy, Russell P.; Van Den Berg, David J.; Vasan, Ramachandran S.; Viaud-Martinez, Karine A.; Vrieze, Scott; Weeks, Daniel E.; Weir, Bruce S.; Weiss, Scott T.; Weng, Lu-Chen; Willer, Cristen J.; Zhang, Yingze; Zhao, Xutong; Arnett, Donna K.; Ashley-Koch, Allison E.; Barnes, Kathleen C.; Boerwinkle, Eric; Gabriel, Stacey; Gibbs, Richard; Rice, Kenneth M.; Rich, Stephen S.; Silverman, Edwin K.; Qasba, Pankaj; Gan, Weiniu; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Papanicolaou, George J.; Nickerson, Deborah A.; Browning, Sharon R.; Zody, Michael C.; Zöllner, Sebastian; Wilson, James G.; Cupples, L. Adrienne; Laurie, Cathy C.; Jaquish, Cashell E.; Hernandez, Ryan D.; O'Connor, Timothy D.; Abecasis, Gonçalo R.; Epidemiology, Richard M. Fairbanks School of Public Health
    The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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    Type 2 Diabetes Genetic Risk Scores Are Associated With Increased Type 2 Diabetes Risk Among African Americans by Cardiometabolic Status
    (Sage, 2018-01-03) Layton, Jill; Li, Xiaochen; Shen, Changyu; de Groot, Mary; Lange, Leslie; Correa, Adolfo; Wessel, Jennifer; Epidemiology, School of Public Health
    The relationship between genetic risk variants associated with glucose homeostasis and type 2 diabetes risk has yet to be fully explored in African American populations. We pooled data from 4 prospective studies including 4622 African Americans to assess whether β-cell dysfunction (BCD) and/or insulin resistance (IR) genetic variants were associated with increased type 2 diabetes risk. The BCD genetic risk score (GRS) and combined BCD/IR GRS were significantly associated with increased type 2 diabetes risk. In cardiometabolic-stratified models, the BCD and IR GRS were associated with increased type 2 diabetes risk among 5 cardiometabolic strata: 3 clinically healthy strata and 2 clinically unhealthy strata. Genetic risk scores related to BCD and IR were associated with increased risk of type 2 diabetes in African Americans. Notably, the GRSs were significant predictors of type 2 diabetes among individuals in clinically normal ranges of cardiometabolic traits.