Multiethnic insight into the genetic risk for obesity

From the Peters Group, Public Health Sciences Division.

The etiology of obesity is complex, with genetic, environmental, and biological factors all contributing to its development. Understanding the specific factors and how they influence body fat mass is crucial to developing ways to treat and prevent this common metabolic disease. Studies of the role of genetics in body fat mass estimate a high degree of heritability, with more than 50% of adiposity likely explained by genetics alone. Furthermore, the prevalence of obesity differs significantly by race/ethnicity, providing additional evidence for a role for genetics in obesity, although these differences could also be explained by non-genetic factors. Researchers in the Public Health Sciences Division at Fred Hutch undertook a multiethnic genome-wide association study (GWAS) to gain additional insight into the contribution of genetics to obesity. Results from this study were recently published in the International Journal of Obesity.

To discover new genetic variants associated with body mass index (BMI), the authors utilized the Population Architecture using Genomics and Epidemiology (PAGE) consortium, a collaboration of several well-characterized studies and biobanks that together yield a large-scale and highly diverse study population. Dr. Ulrike Peters, Fred Hutch member and senior author of the study, described the strength of the PAGE consortium, “We realized early on that there is a severe European bias in genetic epidemiological studies leading us in PAGE to focus our efforts on non-Europeans. This allowed us to add many important contributions to the literature on trans-ethnic analyses.” The current study included over 100,000 participants that were African American (35,606), Hispanic/Latino (26,048), Asian/Native Hawaiian (22,466), Native American (535), or European American (17,859). Thus, more than 80% of the participants in this study population were from minority groups. Adiposity was estimated using BMI, and all participants had a BMI between 18.5 kg/m2 (normal weight) and 70 kg/m2 (extreme obesity).

Bathroom Scale
Image from Fred Hutch stock photos

The authors used the Metabochip array to assess potential genetic variants associated with BMI. The Metabochip contains approximately 200,000 genetic markers, called single nucleotide polymorphisms (SNPs), of which a high number are known to be associated with metabolic diseases and relevant traits such as indices of obesity. Twenty-one SNPs known to be associated with BMI were included on the Metabochip array at the time of its design. Of these 21 markers, the PAGE study confirmed 15, while the remaining six markers trended towards but did not reach significance. The authors found an additional 14 markers to be associated with BMI in the PAGE cohort, two of which had not previously been reported to be associated with BMI.

The frequency and strength of the association of the minor variants of the two novel markers differed greatly among the various ethnic groups. One of these SNPs was most frequent in African Americans and Hispanics/Latinos and exhibited the strongest association in these two ancestral groups. Interestingly, the other SNP was frequent in all ethnicities and all ethnicities contributed to this signal. From a functional aspect, more in-depth investigation of the two novel SNPs revealed both may be involved in lipid metabolism.

In summarizing the major contribution from this multiethnic GWAS, Dr. Peters emphasized that genetic studies in obesity research for minority groups are lacking, “Our study aims to reduce the disparity in genetic research by focusing on non-European populations. This effort identified two novel loci for BMI.” Dr. Peters also noted, “Our results demonstrate that trans-ethic analyses can contribute important genetic findings and help to fine-tune known genetic risk loci.” Future work will focus on expanding their efforts to conduct genome-wide approaches for common and rare risk loci discovery. Dr. Peters indicated that they also plan to integrate functional genomic data to accelerate new discoveries.