Large-scale cross-ancestry study explains why obesity risk varies around the world

By analyzing genetic data from more than 800,000 individuals across six ancestries, researchers identified a previously unknown obesity gene, opening a new path to global ancestry-informed treatments.

study: Discovery of obesity genes through cross-ancestry analysis. Image credit: CI Photos / Shutterstock

Obesity is a global epidemic that affects millions of people every day and is associated with a variety of complications, from heart disease and type 2 diabetes to osteoarthritis and social stigma. Although lifestyle factors such as diet and exercise influence obesity, years of genetic research have identified approximately 20 genes that have a significant impact on the likelihood of developing obesity.

Genetic research beyond ancestry reveals new obesity gene

New research published in journal nature communications Researchers at Penn State University studied 839,110 adults from six continental ancestries and identified 13 genes associated with obesity across these ancestries. Eight of these genes had been identified in previous studies, but five were discovered for the first time and had no previous association with obesity. The researchers also analyzed in detail how these genes influence obesity-related comorbidities, such as type 2 diabetes and heart failure risk.

Addressing bias in genetic research

“Obesity affects millions of people, but most research focuses on a small number of people,” said Deeplo Banerjee, a graduate student in Penn State's Bioinformatics and Genomics Program and lead author of the study. “Previous studies have relied primarily on populations of European ancestry, reflecting ancestry bias and missing opportunities to discover additional genes with mutations that may be more prevalent in other ancestries but are still clinically important for Europeans.”

Insights into the global genetic basis of obesity

The researchers said this discovery provides insight into the genetic basis of obesity around the world, explaining that this insight could help guide precision medicine efforts by uncovering important genes that may be overlooked in single population studies.

The importance of population diversity in genetic research

“Obesity is a complex trait that is influenced by many genetic and lifestyle factors,” said study author Santosh Girirajan, chair of the Department of Biochemistry and Molecular Biology and T. Ming Chu Professor of Genomics at Penn State Eberly College of Science. “Studies in a single population may miss important genes that are shared across the population, but any gene, even if clinically important, may not be statistically significant in that population. New databases with greater representation of individuals with ancestry from around the world will help alleviate this bias, but more data from non-European populations are still needed.”

Large cohorts enable cross-ancestry analysis

In this study, researchers used data from just over 450,000 adults. UK Biobanka biomedical database containing genetic, physical, and health data from mostly healthy people in the United Kingdom and approximately 385,000 adults participating in the National Institutes of Health's All of Us Research Program (NIH (National Institutes of Health)) Precision medicine efforts with more inclusive cohorts that reflect the ancestry diversity of the United States. The six continental ancestries included were African, American, East Asian, European, Middle Eastern, and South Asian.

Combining global databases to detect rare variants

“Even in very large cohorts, rare and deleterious mutations are difficult to find unless you study a diverse population,” Banerjee said. “The UK Biobank is comprised primarily of Europeans, and our study sample only includes around 20,000 non-Europeans. By combining the UK Biobank with All of Us, which has around 167,000 non-Europeans, we were able to measure the impact on BMI (BMI), a measure of body fat percentage used as an indicator of obesity, for genes with rare predicted loss of function and deleterious missense mutations, independently in each of the six ancestral populations. ”

Focus on rare and high-impact genetic mutations

The researchers explained that they focused on rare, predicted loss-of-function and deleterious missense variants because these are most likely to have a significant impact on disease. These variants disrupt gene function and are often found at sites in the genome that are highly conserved throughout evolution. Its rarity reflects the fact that such deleterious changes typically do not occur with high frequency in a population.

13 genes associated with obesity across populations

The research team combined non-European populations to find all protein-coding regions of the genome; BMI. They identified 13 genes with statistically significant associations. BMI in European groups that were also replicated in non-European groups. Of these, eight have been previously associated with obesity, including well-known genes such as: MC4R and B.S.N.. 5 genes, YLPM1, RIF1, GIGYF1, SLC5A3, and GRM7was not associated with obesity in previous rare mutation studies. The researchers found that four of these novel genes (YLPM1, RIF1, GIGYF1, and GRM7) The risk of obesity and severe obesity increased by up to approximately 2-fold in odds ratio; SLC5A3 No enhancement of severe obesity was shown. Like genes previously associated with obesity, the newly identified genes are expressed in the brain and adipose tissue, and are associated with obesity traits such as increased body fat percentage.

New pathways in obesity biology

“The novel genes identified in our study highlight both established and emerging pathways in obesity biology,” Banerjee said. ”YLPM1For example, transcription factors expressed in brain tissue are poorly studied and have been linked to psychiatric disorders. This is a clear example of a gene that may have historically been obscured due to low prevalence in a population. In our heterogeneous ancestry analysis, YLPM1 shows surprisingly consistent effects across ancestry. MC4R. ”

Genetic relationship with comorbidities

Researchers also found that some of these genes contribute to other obesity-related conditions, such as type 2 diabetes, high blood pressure, and heart disease. They used a statistical method called mediation analysis to show different mechanisms by which the risk of comorbidities increases, helping to explain why obesity often leads to other serious health problems. Mediation analysis helped the team determine whether these genes directly or indirectly increase the risk of comorbidities. BMI, This also poses a risk of complications. For example, the team discovered that: B.S.N., GIGYF1and SLTM The risk of type 2 diabetes increases through direct and indirect pathways (a phenomenon known as partial mediation). SLC5A3 showed a direct relationship with gastroesophageal reflux disease (Reflux esophagitis) was not mediated by BMI. Although both effects were important, the direct influence of these genes on disease risk was stronger than their indirect influence. BMI.

Plasma proteins as biomarkers and drug targets

The research team also identified changes in circulating proteins associated with the obesity genes they identified in a subset of individuals whose biobank entries included plasma proteomics data (a comprehensive list of proteins found in plasma). These changes point to potential drug targets and biomarkers that could help guide future treatments and track response to treatment, the researchers said. For example, this study highlights proteins such as LECT2 and NCAN as potential mediators between genetic mutations and genes. BMI.

Advancing precision medicine through genetic diversity

“Our findings highlight the power and importance of research beyond ancestry,” Girirajan said. “Some of the obesity genes discovered so far appear to have significant associations only with obesity in Europeans, potentially limiting their potential as therapeutic targets for the world's population. We still discovered some of the most talked about obesity genes. MC4R and B.S.N.However, several new genes with similar effect sizes were also discovered, most of which had clear functional associations with obesity. Our cross-ancestry approach is expected to help develop a more comprehensive view of the factors involved in obesity and help develop effective treatments that can be applied through precision medicine. ”

Combination of genetic burden and obesity susceptibility

The authors also reported that polygenic load additively increases obesity risk in rare variant carriers, highlighting that both common and rare variants jointly shape obesity susceptibility across ancestry.

Funding and institutional support

National Institutes of Health (NIH (National Institutes of Health)) funded the research, with additional resources from Penn State University and the Penn State Huck Institute for Life Sciences.