Discovery of Novel Obesity Genes Through Cross-Ancestry Analysis Underscores Need for Genetic Diversity
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Obesity is a complex, heritable disorder and a worldwide epidemic, yet historically, gene discoveries related to this condition have predominantly relied on studies of homogeneous populations, particularly those of European ancestry. This ancestral bias limits the generalizability of findings and hampers the development of effective precision medicine approaches globally.
Fortunately, a massive new study, led by researchers at Penn State and published in Nature Communications, has successfully tackled this limitation by employing a cross-ancestry analysis. The study examined the genetic data and BMI of 839,110 individuals from six continental ancestries (African, American, East Asian, European, Middle Eastern, and South Asian). Data were leveraged from two significant resources: the UK Biobank (UKB) and the more diverse All of Us (AoU) Research Program.
By focusing on rare, high-impact predicted loss-of-function variants, the researchers identified 13 genes significantly associated with Body Mass Index (BMI). Crucially, five of these genes had not been previously linked to obesity in prior rare-variant studies. These novel obesity genes are YLPM1, RIF1, GIGYF1, SLC5A3, and GRM7.
The impact of these newly discovered genes is substantial: four of them—YLPM1, RIF1, GIGYF1, and GRM7—were found to confer about a three-fold risk for severe obesity. Their effect sizes were comparable to established obesity genes like MC4R and BSN. Like many known obesity genes, the new discoveries are expressed in the brain and adipose tissue, reinforcing the central role of the central nervous system (CNS) in weight homeostasis.
The analysis provided critical insights into how genetic risk operates across different populations. Genes such as YLPM1, MC4R, and SLTM showed remarkably consistent effects on BMI across multiple ancestries, highlighting their generalizability. However, others, like GRM7 and APBA1, exhibited significant ancestral heterogeneity or European-specific bias, underscoring why studies lacking genetic diversity often miss important global drivers of disease.
Furthermore, the researchers explored the downstream health consequences, finding that these genes also influence cardiometabolic comorbidities. For example, GIGYF1 and SLTM carriers showed increased risk for Type 2 Diabetes, partially mediated by BMI. In contrast, SLC5A3 showed a significant direct link to gastroesophageal reflux disease (GERD) independent of BMI.
The findings also confirm that polygenic risk, stemming from common variants, acts additively alongside rare, high-impact variants to increase obesity penetrance. This comprehensive view of obesity genetics, obtained by prioritizing genetic diversity through cross-ancestry analysis, provides indispensable insights that will guide effective therapies and precision medicine globally.
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