Leveraging metabolic pathways and gene expression data to propel understanding of severe obesity - We know little about the mechanisms underlying a critical subset of heterogeneous obesity: Severe Obesity (SevO; BMI≥40kg/m2; ~>100 lbs overweight), which is a risk factor for a host of chronic cardiometabolic and other diseases, with disproportionate impact across populations. Given these differences, deepening the genomic translational pipeline, and identifying regulatory mechanisms of SevO across populations is imperative. Yet, because SevO is an exclusion criterion in many clinical research studies and certain groups 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 subpopulations. 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 ancestry. Effects of ancestral heterogeneity within and across populations and differential disease risk by ancestry 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. 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. We propose to examine: (Aim 1) whole blood RNAseq data from >15,000 individuals (892 SevO cases, 3,342 controls) and (Aim 2) metabolomic data from >46,000 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 population-specific eQTL and metaboQTL maps for critical subpopulations. We have developed a rigorous plan of discovery, internal replication, external validation, and generalization across 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 individuals 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 large, electronic health record-linked biobanks. These studies, unprecedented in size, 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, fundamentally alter understanding of mechanisms underlying SevO and shed light on potential targets for prevention, diagnostics, and treatments.
