Multi-Omics Studies in Historically Excluded Populations: The Road to Equity

Abstract

Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.

Figure 1

Linkage Disequilibrium (LD) difference between populations at the CYP2C19 locus. (a) The LD plot centered at CYP2C19*2 (rs4244285, chr10:94776859–94,786,858 – shown as the red box) in AFR population (Yoruba – YRI). (b) The LD plot centered at CYP2C19*2 (rs4244285, chr10:94776859–94,786,858) in EUR population (CEU – Utah residents with European ancestry). The pairwise LD (D′) between all single-nucleotide polymorphisms (SNPs) in the regions is represented by the shade of red within the LD plot and the number shown in the intersecting square represents the r² LD. LD block were constructed through Haploview using the default algorithm. Of note there are few SNPs and LD blocks in the European (EUR) population as opposed to the African (AFR) populations, as expected. (c) Ensembl plot showing SNPs in LD with rs4244285 in AFR and EUR populations. The blue dashed line represents perfect r² LD = 1.0 and the red dashed line represent r² LD = 0.8. Dots represents SNP within the 50 kB regions around CYP2C19*2 (rs4244285), with the color of the dots representing genomics annotations. Of note, several SNPs which are in perfect LD in EUR are below r² of 0.7 in AFR.

Figure 2

Population distribution in the GWAS Catalog. The donut chart shows the distribution of populations (self-identified) from the EMBL-EBI GWAS catalog representing 5,913 studies. European participants contributed the largest percentage of participants (95.89%). The largest non-European component are Asians (2.93%). Other ancestries contributed ~ 1% of participants. GWAS, genomewide association study.

Figure 3

Power for expression quantitative trait locus (eQTL) analysis by population in GTEx version 8. The ancestry of 838 individuals included in GTEx was categorized by principal components analysis. (a) The PC1 and PC2 from the principal component (PC) analysis separates GTEx samples by continental ancestry. Compared with the position of the 1000 Genome reference panel, we categorized the ancestry for the GTEx samples with subjects on the axes between the European (EUR) and African (AFR) populations categorized as African Americans and the subject overlapping the AMR a were categorized as Hispanic/Latinx. (b) A pie chart showing the population breakdown (as determined by PCs) within GTEx. Of the total cohort, 687 (81.98%) are European ancestry, 102 (12.41%) are African ancestry, 31 (3.7%) are AMR, 16 are Asian ancestry (East Asian (EAS) and South Asian (SAS)). (c) After removing all EUR ancestry individuals, we examined how many tissue types reached the threshold of adequate size for eQTL analysis as set by GTEx (N = 80) The black dash line shows this threshold. Each bar represents the number of individuals of that ancestry within each tissue type with available genomic data. Only the African ancestry samples in muscle and whole blood have adequate sample size for eQTL analysis. Of note all tissue types reach this analysis threshold for EUR ancestry individuals. This analysis does not take into account the presence of matching transcriptomic data. Through quality control (QC) or missingness, the number of transcriptomes available for analysis may be even lower thus further limiting the ability to perform eQTL mapping. NA, not applicable.

Figure 4

Summary of the ancestry distribution of PWAS studies. A donut chart shows the distribution of populations in PWAS studies (obtained from http://www.metabolomix.com/a-table-of-all-published-gwas-with-proteomics/). Currently, 93.15% of participants in published PWAS studies were European ancestry. Only 3.26% of participants were admixed population (African Americans and Hispanic); 1.23% of participants were Africans, 0.07% were Asian, and 2.28% were unknown populations.