By analyzing genetic data from more than 800,000 individuals across six ancestries, researchers have identified previously unknown obesity genes, opening new avenues for global, ancestry-informed treatments.
Study: Discovery of obesity genes through cross-ancestry analysis. Image Credit: CI Photos / Shutterstock
Obesity is a global epidemic affecting millions of people every day and is associated with comorbidities ranging from heart disease and Type 2 diabetes to osteoarthritis and social stigma. While lifestyle factors, such as diet and exercise, influence obesity, years of genetic research have identified approximately 20 genes that have a significant impact on a person's likelihood of developing the condition.
Cross-Ancestry Genetic Study Reveals New Obesity Genes
A new study, published in the journal Nature Communications by researchers at Penn State, involving 839,110 adults from six continental ancestries, has identified 13 genes associated with obesity across these ancestries. While eight of these genes had been identified in previous studies, five were discovered for the first time, with no prior links to obesity. In addition, the team dissected how these genes influence obesity-related comorbidities such as Type 2 diabetes and heart failure risk.
Addressing Bias in Genetic Research
"Obesity touches millions, but most studies have focused on a few," said Deepro Banerjee, a graduate student in the bioinformatics and genomics program at Penn State and first author of the study. "Previous studies have relied predominantly on European-ancestry populations, reflecting an ancestral bias and missing opportunities to discover additional genes whose mutations may be more prevalent in other ancestries yet still clinically relevant for Europeans."
Insights into Global Genetic Underpinnings of Obesity
The findings provide insight into the genetic underpinnings of obesity worldwide, the researchers said, explaining that this insight could help guide precision medicine efforts by revealing key genes that might be overlooked in single-population studies.
Importance of Population Diversity in Genetic Studies
"Obesity is a complex trait that is influenced by many genetic and lifestyle factors," said Santhosh Girirajan, T. Ming Chu Professor of Genomics and head of the Department of Biochemistry and Molecular Biology in the Penn State Eberly College of Science, and an author of the paper. "Studies in a single population can lead us to miss important genes that are shared across populations but may not rise to statistical significance in any one of them, even if they are clinically important in that population. New databases that include more representation of individuals with ancestries from around the world are helping to alleviate this bias, but we still need more data from non-European populations."
Large-Scale Cohorts Enable Cross-Ancestry Analysis
For the study, the researchers used data from just over 450,000 adults in the UK Biobank, a biomedical database including genetic, physical and health data from mostly healthy people in the United Kingdom, and nearly 385,000 adults in the All of Us Research Program, a U.S. National Institutes of Health (NIH) precision-medicine initiative with a more inclusive cohort that mirrors U.S. ancestral diversity. The six continental ancestries included were African, American, East Asian, European, Middle Eastern, and South Asian.
Combining Global Databases to Detect Rare Variants
"Even with very large cohorts, rare, damaging variants can be hard to find unless we look across diverse populations," Banerjee said. "The UK Biobank is made up largely of Europeans, with only about 20,000 non-Europeans in our study sample. By combining UK Biobank with All of Us, which contributed about 167,000 non-Europeans, we were able to measure the impact on body mass index (BMI), a measure of body fat percentage used as an indicator for obesity, of genes with rare predicted loss-of-function and deleterious missense variants independently in each of the six ancestral populations."
Focus on Rare, High-Impact Genetic Variants
The researchers explained that they focused on rare, predicted loss-of-function and deleterious missense variants because these are the most likely to have a significant impact on a disease. These variants disrupt the function of a gene and are often found at sites in the genome that are highly conserved through evolution. Their rarity reflects the fact that such harmful changes do not typically occur at high frequency in the population.
Thirteen Genes Linked to Obesity Across Populations
The team combined the non-European populations and performed an association study of all protein-coding regions of the genome with BMI. They identified 13 genes with statistically significant associations to BMI in the European group that replicated in non-Europeans. Of these, eight had been previously associated with obesity, including well-known genes like MC4R and BSN. Five genes, YLPM1, RIF1, GIGYF1, SLC5A3, and GRM7, had not been associated with obesity in prior rare-variant studies. The researchers found that four of these novel genes (YLPM1, RIF1, GIGYF1, and GRM7) increased the risk of obesity and severe obesity with odds ratios up to about two-fold, whereas SLC5A3 showed no enrichment for severe obesity. Like genes previously associated with obesity, the newly identified genes are expressed in the brain and adipose tissue and are linked to obesity traits, such as increased body fat percentage.
Emerging Pathways in Obesity Biology
"The novel genes identified in our study highlight both established and emerging pathways in obesity biology," Banerjee said. "YLPM1, for example, is an understudied transcription factor expressed in brain tissues, with links to mental disorders. It's a clear example of a gene whose lower prevalence in one population may have obscured it historically. In our cross-ancestry analysis, YLPM1 shows a remarkably consistent effect across ancestries, similar to MC4R."
Genetic Links to Comorbid Diseases
The researchers also found that several of these genes contribute to other obesity-related conditions, including Type 2 diabetes, hypertension, and heart disease. Using a statistical method called mediation analysis, they showed different mechanisms through which comorbidity risk increases, helping to explain why obesity often leads to other serious health problems. Mediation analysis helped the team determine whether these genes directly increase the risk of comorbid diseases or indirectly, by first increasing BMI, which in turn drives the comorbid risk. For example, the team found that BSN, GIGYF1, and SLTM increased the risk of Type 2 diabetes through both direct and indirect paths, a phenomenon known as partial mediation, while SLC5A3 showed a direct link to gastroesophageal reflux disease (GERD) that was not mediated by BMI. While both effects were significant, the direct impact of these genes on disease risk was stronger than the indirect effect through BMI.
Plasma Proteins as Biomarkers and Drug Targets
In a subset of individuals whose biobank entries included plasma proteomics data, a comprehensive list of proteins found in their blood plasma, the team also identified changes in circulating proteins linked to the obesity genes that they identified. These changes indicate potential drug targets and biomarkers that could guide future treatments and help track responses to therapy, the researchers said. For example, the study highlighted proteins such as LECT2 and NCAN as possible mediators between genetic variants and BMI.
Advancing Precision Medicine Through Genetic Diversity
"Our findings emphasize the power and importance of cross-ancestry studies," Girirajan said. "Some of the previously discovered obesity genes appear to only have significant association to obesity in Europeans, which could limit their potential as therapeutic targets for a global population. We did still find some of the most talked about obesity genes, like MC4R and BSN, but we also found several new genes with similar effect sizes, most with clear functional connections to obesity. Our cross-ancestry approach is helping us develop a more comprehensive view of the factors involved in obesity, which will hopefully help us develop effective therapies that can be applied through precision medicine."
Combined Genetic Burden and Obesity Susceptibility
The authors also reported that polygenic burden additively increased the risk of obesity in carriers of rare variants, underscoring that both common and rare variants jointly shape obesity susceptibility across ancestries.
Funding and Institutional Support
The U.S. National Institutes of Health (NIH) funded the research, with additional resources from Penn State and the Penn State Huck Institutes of the Life Sciences.
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