ABSTRACT
We know little about the mechanisms underlying a critical subset of heterogeneous obesity: Severe Obesity
(SevO; BMI≥40kg/m2; ~>100 lbs overweight), a risk factor for a host of chronic cardiometabolic and other
diseases, disproportionately impacting Hispanic/Latino (HL) populations. Given the rapidly growing US HL
population, deepening the genomic translational pipeline, and identifying regulatory mechanisms of SevO in HL
populations are imperative. Yet, because SevO is an exclusion criterion in many clinical research studies and
HL are profoundly underrepresented in genomic, transcriptomic, and metabolomic research, we know little about
its underlying mechanistic pathways or the clinical significance of SevO susceptibility in HL individuals. As a
result, studies attempting to fine map GWAS loci, estimate expression quantitative trait loci (eQTL) and
metabolomics quantitative trait loci (metaboQTL) have been uninformed by HL populations and effects of
ancestral heterogeneity within and across HL populations have also been historically under-investigated. These
fundamental gaps in data and in ancestry-informed analyses necessitate integrative studies of multi-omics to
propel mechanistic understanding and establish clinical significance of SevO susceptibility in HL. Thus, we
propose to leverage transcriptomics, metabolomics, and SevO GWAS data from extant epidemiologic and
clinical studies in the US, Central, and South America, to discover, identify causal relationships, and reveal
molecular mechanisms of SevO in HL populations. We propose to examine: (Aim 1) whole blood RNAseq data
from >15,000 HL individuals (892 SevO cases, 3,342 controls) and (Aim 2) metabolomic data from >46,000
HL individuals (1,588 SevO cases, 9,827 controls) to identify transcriptomic and metabolomic signals
underlying SevO using ancestry-informed models to derive the first ever, ancestry-informed HL-specific eQTL
and metaboQTL maps. We have developed a rigorous plan of discovery, internal replication, external validation,
and generalization across diverse populations and cell types to ensure a high level of scientific rigor. In Aim 3,
we propose to identify mechanisms and infer causal pathways underlying SevO by integrating these measures
with SevO GWAS data from ~240,000 HL to perform ancestry-informed colocalization, pathway, and causal
inference modeling, and establish broad clinical significance using GWAS-, transcript-, and metabolite-informed
polygenic risk scores of SevO in HL individuals in large, electronic health record-linked biobanks. These studies,
unprecedented in size and ancestral diversity in HL populations will increase knowledge of the metabolic
pathways and gene expression profiles of SevO, filling critical gaps. Using our deep expertise in cutting-edge
methods in ancestry-informative analyses, we propose novel methodological approaches including integration
of local ancestry eQTL and metaboQTL mapping studies and multi-omics informed polygenic risk scores for use
in clinical biobank data. We will broaden the representativeness of the genomics literature and fundamentally
alter understanding of mechanisms underlying SevO in an understudied and disproportionately impacted group.
