Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Abstract

Background Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative—an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736). Methods We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. Results We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals’ SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10−16, EAA p-value=6.73×10−11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. Conclusions Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Polygenic scoring accuracy varies across the genetic ancestry continuum in all human populations

“…Ancestry ascertainment in ATLAS. The ATLAS individuals are clustered into five genetic ancestry clusters -European Americans (EA), Hispanic and Latino Americans (HL), South Asian Americans (SAA) and East Asian Americans (ESA) and African Americans (AA) as described in ref 25 based on their proximity with 1000 Genome super populations on the PC space. First, we filter the ATLAS typed genotypes with plink2 by Mendel error rate ('plink --me 1 1 -set-me-missing'), founders ('--filterfounders'), minor allele frequency ('-maf 0.15'), genotype missing call rate ('--geno 0.05'), and Hardy-Weinberg equilibrium test p-value ('-hwe 0.001').…”

“…Different algorithms and/or reference panels may assign the same individual to different clusters 15,23,24 and thus to different PGS accuracy classes. Moreover, many individuals are not assigned to a cluster due to limited reference panels used for genetic ancestry inference 23,25 , leaving such individuals outside PGS accuracy characterization; this poses equity concerns as it limits PGS applications only to individuals within well-defined clusters of genetic ancestries.…”

Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles

“…In this study, we used a publicly available PGS for tobacco use disorder, developed in European-ancestry individuals in UK Biobank 14, and imputed these scores into the UCLA ATLAS biobank [15][16][17][18][19] . We found that the TUD-PGS demonstrated inconsistent predictive performance and risk stratification in non-European ancestry groups.…”

Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles

“…In our analysis, we combined a PGS for tobacco use disorder (TUD) with a PheWAS approach to create a PGS-PheWAS, a powerful way to examine the potential pleiotropic effects of multiple genetic variants that predispose to tobacco use disorder and identify systemic disease risks for individuals with a genetic predisposition to tobacco use 13 . We used a publicly available PGS for tobacco use disorder, developed in European-ancestry individuals in UK Biobank 14 , and imputed these scores into the UCLA ATLAS biobank which comprises consented and genotyped UCLA patients representing diverse ancestry groups and phenotypes drawn from their electronic health records [15][16][17][18][19] . We found that the TUD-PGS demonstrated inconsistent predictive performance and risk strati cation in non-European ancestry groups within the UCLA ATLAS biobank.…”