Integrated Genomic and Transcriptomic Study Reveals MAPK11 and PER1 as Important Obesity Susceptibility Genes in a High-Risk Hispanic/Latino Population

整合基因组学和转录组学研究揭示 MAPK11 和 PER1 是高危西班牙裔/拉丁裔人群中重要的肥胖易感基因

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Abstract

BACKGROUND: While GWAS (genome-wide association studies) have identified over 1000 obesity-associated loci, their functional impact on gene expression remains unclear. Moreover, many studies have not fully captured the genetic architecture of obesity in high-risk populations or considered the complexity of adiposity beyond traditional measures. To address these gaps, this study explores the genetic and transcriptomic pathways of obesity using diverse obesity phenotypes in a high-risk population. METHODS: We analyzed genomic and whole-blood transcriptomic data from the CCHC (Cameron County Hispanic Cohort), performing GWAS on 13 obesity-related traits. Differential expression analysis was conducted for genes near GWAS-identified single nucleotide polymorphisms (P<5×10(-6)) followed by expression quantitative trait loci mapping and GWAS-expression quantitative trait loci colocalization. RESULTS: GWAS identified 486 trait associations, including 6 genome-wide significant (P<5×10(-8)) loci, with 3 novel signals linked to abdominal subcutaneous adipose tissue, body fat percentage, and waist circumference. Among 3024 genes near these loci, 60 showed differential expression. Further expression quantitative trait loci analysis suggested 2 single nucleotide polymorphism-gene-trait relationships: rs543314376-MAPK11, associated with subcutaneous adipose tissue volume in females, and rs963018484-PER1, linked to body mass index in females. Both genes play key roles in obesity-related pathways, including inflammation and circadian rhythm regulation. CONCLUSIONS: This integrative genomic-transcriptomic analysis uncovers 2 novel candidate genes for obesity and underscores the critical need for involving all populations and comprehensive adiposity measures in obesity research. By expanding beyond body mass index in a Hispanic/Latino population, we move closer to a deeper and more inclusive understanding of obesity's genetic architecture.

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