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.

Overlap in synaptic neurological condition susceptibility pathways and the neural pannexin 1 interactome revealed by bioinformatics analyses

“…GWAS exhibits several key caveats. Importantly, GWAS was historically somewhat restricted to European populations, thereby limiting their broader use and application [ 122 ]; importantly, some recent progress has been made in expanding GWAS to more groups, especially Asian populations (see Table 7 for details of GWASs used in the current study). Additionally, although GWAS identify genetic differences associated with disease (and beyond), these are not necessarily causal, and where mechanistic links might be present, these can be challenging and complicated to unravel [ 123 ].…”

Advancing equity in human genomics through tissue-specific multi-ancestry molecular data

“…Despite the enormous ongoing efforts in the GWAS community to generate genetic data from diverse ancestries, a significant gap remains in the availability of molecular data from primary tissues and cells of diverse populations, coupled with genotype information 8 ( Figure 1 ). Returning to the novel association reported in the Ugandan GWAS of eGFR, attempts to elucidate the function of the identified intergenic variant through colocalization are limited because this variant is absent or rare in European-centric molecular QTL resources.…”

Diversity, Equity, and Inclusion: Translating Clinical Pharmacology for All

“…These consultative and collaborative strategies are designed to enhance trust by facilitating input from community experts to enhance research design, implementation, and dissemination, with the purpose of enabling equitable access to clinical trials and delivery of innovation for people who have historically been left out of biomedical research. In their comprehensive review of methodologies for population pharmacogenomics research, Yang et al 11 . offer a landscape analysis of multi‐omics technologies across the spectrum from the genome to transcriptome, proteome and metabolome, with a specific focus on database resources and analytics that maximize the ability to discover variants of pathophysiologic and pharmacological significance in minority and admixed populations.…”