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Kelly TN, Sun X, He KY, Brown MR, Taliun SAGagliano, Hellwege JN, Irvin MR, Mi X, Brody JA, Franceschini N, et al. Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension. Hypertension. 2022 :101161HYPERTENSIONAHA12219324.

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Zillikens CM, Demissie S, Hsu Y-H, Yerges-Armstrong LM, Chou W-C, Stolk L, Livshits G, Broer L, Johnson T, Koller DL, et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun. 2017 ;8(1):80.

Christophersen IE, Rienstra M, Roselli C, Yin X, Geelhoed B, Barnard J, Lin H, Arking DE, Smith AV, Albert CM, et al. Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation. Nat Genet. 2017 ;49(6):946-952.

Scott RA, Lagou V, Welch RP, Wheeler E, Montasser ME, Luan J'an, Mägi R, Strawbridge RJ, Rehnberg E, Gustafsson S, et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet. 2012 ;44(9):991-1005.

Tajuddin SM, Schick UM, Eicher JD, Chami N, Giri A, Brody JA, W Hill D, Kacprowski T, Li J, Lyytikäinen L-P, et al. Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases. Am J Hum Genet. 2016 ;99(1):22-39.

Tcheandjieu C, Zhu X, Hilliard AT, Clarke SL, Napolioni V, Ma S, Lee KMin, Fang H, Chen F, Lu Y, et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat Med. 2022 ;28(8):1679-1692.

Sung YJ, Winkler TW, Fuentes Lde Las, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, et al. A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. Am J Hum Genet. 2018 ;102(3):375-400.

Crosby J, Peloso GM, Auer PL, Crosslin DR, Stitziel NO, Lange LA, Lu Y, Tang Z-Z, Zhang H, Hindy G, et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N Engl J Med. 2014 ;371(1):22-31.

Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert M-F, Raghavan S, Lipovich L, et al. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun. 2015 ;6:5897.

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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchel BD, Psaty BM, et al. Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores. medRxiv. 2023 .

Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, et al. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep. 2024 ;14(1):12436.

Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, Thorleifsson G, Zillikens CM, Speliotes EK, Mägi R, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010 ;42(11):949-60.

Liu C, Kraja AT, Smith JA, Brody JA, Franceschini N, Bis JC, Rice K, Morrison AC, Lu Y, Weiss S, et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat Genet. 2016 ;48(10):1162-70.

Monda KL, Chen GK, Taylor KC, Palmer C, Edwards TL, Lange LA, C Y Ng M, Adeyemo AA, Allison MA, Bielak LF, et al. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet. 2013 ;45(6):690-6.

C Y Ng M, Shriner D, Chen BH, Li J, Chen W-M, Guo X, Liu J, Bielinski SJ, Yanek LR, Nalls MA, et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 2014 ;10(8):e1004517.

Gorski M, Jung B, Li Y, Matias-Garcia PR, Wuttke M, Coassin S, Thio CHL, Kleber ME, Winkler TW, Wanner V, et al. Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline. Kidney Int. 2020 .

Lundin JI, Peters U, Hu Y, Ammous F, Avery CL, Benjamin EJ, Bis JC, Brody JA, Carlson C, Cushman M, et al. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations. Epigenetics. 2024 ;19(1):2333668.

Nauffal V, Morrill VN, Jurgens SJ, Choi SHoan, Hall AW, Weng L-C, Halford JL, Austin-Tse C, Haggerty CM, Harris SL, et al. Monogenic and Polygenic Contributions to QTc Prolongation in the Population. Circulation. 2022 .

Jakubek YA, Zhou Y, Stilp A, Bacon J, Wong JW, Ozcan Z, Arnett D, Barnes K, Bis JC, Boerwinkle E, et al. Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing. Nat Genet. 2023 ;55(11):1912-1919.

Mahajan A, Spracklen CN, Zhang W, C Y Ng M, Petty LE, Kitajima H, Yu GZ, Rüeger S, Speidel L, Kim YJin, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022 ;54(5):560-572.

Bentley AR, Sung YJ, Brown MR, Winkler TW, Kraja AT, Ntalla I, Schwander K, Chasman DI, Lim E, Deng X, et al. Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat Genet. 2019 ;51(4):636-648.

Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Rayner NW, Bocher O, et al. Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications. medRxiv. 2023 .

Ntalla I, Weng L-C, Cartwright JH, Hall AWeber, Sveinbjornsson G, Tucker NR, Choi SHoan, Chaffin MD, Roselli C, Barnes MR, et al. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction. Nat Commun. 2020 ;11(1):2542.

Noordam R, Bos MM, Wang H, Winkler TW, Bentley AR, Kilpeläinen TO, de Vries PS, Sung YJu, Schwander K, Cade BE, et al. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat Commun. 2019 ;10(1):5121.

Kilpeläinen TO, Bentley AR, Noordam R, Sung YJu, Schwander K, Winkler TW, Jakupović H, Chasman DI, Manning A, Ntalla I, et al. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nat Commun. 2019 ;10(1):376.

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