In Clinical pharmacology and therapeutics
Over the past few decades, genome-wide association studies (GWAS) 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, GWAS are limited in their ability to uncover mechanistic insight given that most significant association 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 GWAS 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 SNP and genes that explain these phenotypes. These include polygenic risk score (PRS), expression quantitative trait locus (eQTL) mapping and Transcriptome-wide association studies (TWAS). Analogous protein methods such as proteins quantitative trait locus (pQTL) mapping, proteome-wide association studies (PWAS), and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. While, 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.
Yang Guang, Mishra Mrinal, Perera Minoli A
2022-Dec-10
