Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies

Fang, Huaying; Hui, Qin; Lynch, Julie; Honerlaw, Jacqueline; Assimes, Themistocles L.; Huang, Jie; Vujkovic, Marijana; Damrauer, Scott M.; Pyarajan, Saiju; Gaziano, J. Michael; DuVall, Scott L.; O’Donnell, Christopher J.; Cho, Kelly; Chang, Kyong–Mi; Wilson, Peter W.F.; Tsao, Philip S.; Ramoni, Rachel; Breeling, Jim; Huang, Grant D.; Muralidhar, Sumitra; Moser, Jennifer; Whitbourne, Stacey B.; Brewer, Jessica; Concato, John; Warren, Stuart; Argyres, Dean P.; Stephens, Brady; Brophy, Mary; Humphries, Donald E.; Do, Nhan; Shayan, Shahpoor; Nguyen, Xuan‐Mai T.; Hauser, Elizabeth R.; Sun, Yan V.; Zhao, Hongyu; McArdle, Rachel; Dell’Italia, Louis J.; Harley, John B.; Whittle, Jeff; Beckham, Jean C.; Wells, John A.; Gutierrez, Salvador; Gibson, Gretchen; Kaminsky, Laurence S.; Villareal, Gerardo; Kinlay, Scott; Xu, Junzhe; Hamner, Mark B.; Haddock, Kathlyn Sue; Bhushan, Sujata; Iruvanti, Pran; Godschalk, Michael; Ballas, Zuhair K.; Buford, Malcolm; Mastorides, Stephen; Klein, Jon; Ratcliffe, Nora R.; Flórez, Hermes; Swann, Alan C.; Murdoch, Maureen; Sriram, Peruvemba; Yeh, Shing Shing; Washburn, Ronald G.; Jhala, Darshana; Aguayo, Samuel M.; Cohen, David; Sharma, Satish C.; Callaghan, John T.; Oursler, Kris Ann; Whooley, Mary A.; Ahuja, Sunil K.; Gutierrez, Amparo; Schifman, Ron B.; Greco, Jennifer J.; Rauchman, Michael; Servatius, Richard J.; Oehlert, Mary E.; Wallbom, Agnes; Fernando, Ronald; Morgan, Timothy R.; Stapley, Todd; Sherman, Scott E.; Anderson, Gwenevere; Sonel, Elif; Boyko, Edward J.; Meyer, Laurence; Gupta, Samir; Fayad, Joseph; Hung, Adriana M.; Lichy, Jack H.; Hurley, Robin A.; Robey, Brooks; Striker, Rob; Tang, Hua

doi:10.1016/j.ajhg.2019.08.012

Cited by 215 publications

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“…Linear regression models were run under an additive model in plink2 on 1000G (v3p5) imputed dosages. Analyses were run using models described above within each race/ethnicity stratum (AFR, ASN, EA, HA) classified based on their genotype data using HARE (Fang et al, 2019). Meta-analyses for the trans-ethnic analyses were completed in METAL (Willer et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations

Chen

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Mousas

et al. 2020

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words) 13Most loci identified by GWAS have been found in populations of European ancestry (EA). In 14 trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 15 non-EA individuals, we identified 5,552 trait-variant associations at P<5x10 -9 , including 71 16 novel loci not found in EA populations. We also identified novel ancestry-specific variants not 17 found in EA, including an IL7 missense variant in South Asians associated with lymphocyte 18 count in vivo and IL7 secretion levels in vitro. Fine-mapping prioritized variants annotated as 19 functional, and generated 95% credible sets that were 30% smaller when using the trans-ethnic 20 as opposed to the EA-only results. We explored the clinical significance and predictive value of 21 trans-ethnic variants in multiple populations, and compared genetic architecture and the impact 22 of natural selection on these blood phenotypes between populations. Altogether, our results for 23 hematological traits highlight the value of a more global representation of populations in genetic 24 studies. 25 26 4 causal variants (Li and Keating, 2014). Since blood cells play a key role in pathogen invasion, 50 defense and inflammatory responses, hematologic-associated genetic loci are particularly 51 predisposed to be differentiated across ancestral populations as a result of population history and 52 local evolutionary selective pressures (Ding et al., 2013; Lo et al., 2011; Raj et al., 2013). Given 53 the essential role of blood cells in tissue oxygen delivery, inflammatory responses, atherosclerosis, 54 and thrombosis (Byrnes and Wolberg, 2017; Chu et al., 2010; Colin et al., 2014; Tajuddin et al., 55 2016), factors that contribute to such inter-population differences in blood-cell traits may also play 56 appreciable roles in the pathogenesis of chronic diseases and health disparities between 57 populations. 58 59 5 RESULTS 60 Trans-ethnic and ancestry-specific blood-cell traits genetic associations 61We analyzed genotype-phenotype associations at up to 45 million autosomal variants in 746,667 62 participants, including 184,424 individuals of non-EA descent, for 15 traits (Figure 1, 63 Supplementary Tables 1-4, and Methods). The association results of the EA-specific meta-64 analyses are reported separately in a companion paper. In the trans-ethnic meta-analyses, we 65 identified 5,552 trait-variant associations at P<5x10 -9 , which include 71 novel associations not 66 reported in the EA-specific manuscript ( Supplementary Table 5). Of the 5,552 trans-ethnic loci, 67 128 showed strong evidence of allelic effect heterogeneity across populations (Pancestry.hetero <5x10 -68 9 ) ( Supplementary Figure 1 and Supplementary Table 5). Ancestry-specific meta-analyses 69 revealed 28 novel trait-variant associations (Figure 1 and Supplementary Tables 6-10). 70However, 19 out of these 21 novel AFR-specific associations map to chromosome 1 and are 71 associated with WBC or neutrophil counts, therefore reflecting long-range associations due...

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Section: Methodsmentioning

confidence: 99%

Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations

Chen

Raffield

Mousas

et al. 2020

Preprint

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159

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“…Informed consent is obtained from all participants to provide blood for genomic analysis and access to their full EHR data within the VA prior to and after enrollment. Imputed genetic information is available for up to 314,434 participants assigned to white-European ancestry using the HARE algorithm 57,58 . We used inpatient and outpatient ICD9/10 diagnostic and Current Procedural Terminology codes to identify subjects with clinical CAD either before enrollment going back to 2002 or after enrollment until mid-August 2018.…”

Section: Methodsmentioning

confidence: 99%

Chromosome 1q21.2 and additional loci influence risk of spontaneous coronary artery dissection and myocardial infarction

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Spontaneous coronary artery dissection (SCAD) is a non-atherosclerotic cause of myocardial infarction (MI), typically in young women. We undertook a genome-wide association study of SCAD (N cases = 270/N controls = 5,263) and identified and replicated an association of rs12740679 at chromosome 1q21.2 (P discovery+replication = 2.19 × 10 −12 , OR = 1.8) influencing ADAMTSL4 expression. Meta-analysis of discovery and replication samples identified associations with P < 5 × 10 −8 at chromosome 6p24.1 in PHACTR1, chromosome 12q13.3 in LRP1, and in females-only, at chromosome 21q22.11 near LINC00310. A polygenic risk score for SCAD was associated with (1) higher risk of SCAD in individuals with fibromuscular dysplasia (P = 0.021, OR = 1.82 [95% CI: 1.09-3.02]) and (2) lower risk of atherosclerotic coronary artery disease and MI in the UK Biobank (P = 1.28 × 10 −17 , HR = 0.91 [95% CI :0.89-0.93], for MI) and Million Veteran Program (P = 9.33 × 10 −36 , OR = 0.95 [95% CI: 0.94-0.96], for CAD; P = 3.35 × 10 −6 , OR = 0.96 [95% CI: 0.95-0.98] for MI). Here we report that SCADrelated MI and atherosclerotic MI exist at opposite ends of a genetic risk spectrum, inciting MI with disparate underlying vascular biology.

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“…For the current analysis, clinical and genetic data were available from 234,683 European (EU), 64,961 AA, and 22,615 LA participants (S2 Table in S1 File ) categorized as mutually-exclusive ancestral groups based on CDW data, self-identified race/ethnicity, and genetically inferred ancestry enrolled in MVP from 2011 until 2016 [ 33 ]. Asian American participants were excluded due to small sample size.…”

Section: Methodsmentioning

confidence: 99%

Validating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program

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Background & aims Given ongoing challenges in non-invasive non-alcoholic liver disease (NAFLD) diagnosis, we sought to validate an ALT-based NAFLD phenotype using measures readily available in electronic health records (EHRs) and population-based studies by leveraging the clinical and genetic data in the Million Veteran Program (MVP), a multi-ethnic mega-biobank of US Veterans. Methods MVP participants with alanine aminotransferases (ALT) >40 units/L for men and >30 units/L for women without other causes of liver disease were compared to controls with normal ALT. Genetic variants spanning eight NAFLD risk or ALT-associated loci ( LYPLAL1 , GCKR , HSD17B13 , TRIB1 , PPP1R3B , ERLIN1 , TM6SF2 , PNPLA3) were tested for NAFLD associations with sensitivity analyses adjusting for metabolic risk factors and alcohol consumption. A manual EHR review assessed performance characteristics of the NAFLD phenotype with imaging and biopsy data as gold standards. Genetic associations with advanced fibrosis were explored using FIB4, NAFLD Fibrosis Score and platelet counts. Results Among 322,259 MVP participants, 19% met non-invasive criteria for NAFLD. Trans-ethnic meta-analysis replicated associations with previously reported genetic variants in all but LYPLAL1 and GCKR loci (P<6x10 -3 ), without attenuation when adjusted for metabolic risk factors and alcohol consumption. At the previously reported LYPLAL1 locus, the established genetic variant did not appear to be associated with NAFLD, however the regional association plot showed a significant association with NAFLD 279kb downstream. In the EHR validation, the ALT-based NAFLD phenotype yielded a positive predictive value 0.89 and 0.84 for liver biopsy and abdominal imaging, respectively (inter-rater reliability (Cohen’s kappa = 0.98)). HSD17B13 and PNPLA3 loci were associated with advanced fibrosis. Conclusions We validate a simple, non-invasive ALT-based NAFLD phenotype using EHR data by leveraging previously established NAFLD risk-associated genetic polymorphisms.

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Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies

Cited by 215 publications

References 37 publications

Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations

Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations

Chromosome 1q21.2 and additional loci influence risk of spontaneous coronary artery dissection and myocardial infarction

Validating a non-invasive, ALT-based non-alcoholic fatty liver phenotype in the million veteran program

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