Protein prediction for trait mapping in diverse populations

Abstract

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n = 183), Chinese (n = 71), European (n = 416), and Hispanic/Latino (n = 301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ∼50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837327.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

“…Thus, we assessed the applicability of our models in TWAS using S-PrediXcan on PAGE and PanUKBB GWAS summary statistics and found out that across all tissues and populations, MASHR models yielded the highest number of total gene-trait pairs associations, with MASHR AFA reporting the highest number. In this manner, it seems that although MASHR improved gene expression prediction accuracy for all populations analyzed, using transcriptome prediction models that match the ancestries of the GWAS dataset still yields the highest number of TWAS discoveries, which is in agreement with many previous works 11,30–33 . Furthermore, by investigating which associations had been previously reported in the GWAS Catalog, we saw that most new discoveries were found by MASHR models.…”

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

“…In another study, researchers developed a method to map eQTL in a cohort of individuals with non-European ancestry, including individuals of African American and Hispanic ancestry [114]. The same research group also mapped pQTL in cohorts from populations of African American, Chinese, and Hispanic ancestry [115]. These studies are still limited in terms of sample size.…”

Proteome and genome integration analysis of obesity

“…To date, two PWASs, one brain PWAS, and one plasma PWAS have been applied to explore the relationships between the proteome and obesity. [59,60] Brain PWAS in obesity In 2021, Wingo et al [59] published a brain PWAS. The reference panel was individual-level brain proteome data from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) cohort.…”

“…To date, two PWASs, one brain PWAS, and one plasma PWAS have been applied to explore the relationships between the proteome and obesity. [ 59 , 60 ]…”