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Updated: Jan 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Genome-wide association study provides novel insight into the genetic architecture of severe obesity
Mohanraj Krishnan1,2,3, Mohammad Yaser Anwar1, Anne E Justice4
1Department of Epidemiology University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, North Carolina, United States of America.
Abstract:
Severe obesity (SevO) is a primary driver of cardiovascular diseases (CVD), cardiometabolic diseases (CMD) and several cancers, with a disproportionate impact on marginalized populations. SevO is an understudied global health disease, limiting knowledge about its mechanisms and impacts. In genome-wide association study (GWAS) meta-analyses of the tail end of the BMI distribution (≥95th percentile BMI) and two SevO phenotypes [Obesity Class III BMI ≥ 40 kg/m2 and Obesity Class IV BMI ≥ 50 kg/m2] in 159,359 individuals across eleven ancestrally diverse population-based studies followed by replication in 480,897 individuals across six ancestrally diverse studies, we identified and replicated three novel signals in known loci of BMI [TENM2, PLCL2, ZNF184], associated with SevO traits. We confirmed a large overlap in the genetic architecture of continuous BMI and severe obesity phenotypes, suggesting little genetic heterogeneity in common variants, between obesity subgroups. Systematic analyses combining functional mapping, polygenic risk scores (PRS), phenome wide association studies (PheWAS) and environmental risk factors further reinforce shared downstream comorbidities associated with continuous measures of BMI and the importance of known lifestyle factors in interaction with genetic predisposition to SevO. Our study expands the number of SevO signals, demonstrates a strong overlap in the genetic architecture of SevO and BMI and reveals a remarkable impact of SevO on the clinical phenome, affording new opportunities for clinical prevention and mechanistic insights.
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