NEW YORK (GenomeWeb) – Six new genetic loci that are linked to body shape have been uncovered by a composite phenotype genome-wide association meta-analysis, an approach that could be useful for discovering genes connected to other correlated traits.
For the study, which was reported in Nature Communications today, an international team led by investigators in Germany and the US took a principal component meta-analysis approach to find loci linked to body shape. The group considered body shape as a composite trait made up of half a dozen measurements — body mass index, height, weight, waist circumference, hip circumference, and waist-to-hip ratio — that were brought together in averaged principal components. It performed a genome-wide association meta-analysis of data from 65 prior studies to narrow in on six loci influencing three of the average principal components.
"Our findings suggest that the body shape composite phenotype, assessed by [averaged principal components], represents information that is not fully captured by individual (anthropometric) traits," the authors, led by Martina Müller-Nurasyid at the Helmholtz Zentrum München-German Research Center for Environmental Health and Ruth Loos at the Icahn School of Medicine at Mount Sinai's Charles Bronfman Institute for Personalized Medicine, wrote.
Hundreds of loci have already been implicated in body shape through individual trait-focused GWAS, including close to 100 loci linked to body mass index (BMI), some 600 height-associated loci, and 160 loci associated with obesity-related features such as waist-to-hip ratio, the team explained.
To try to assess several related traits together, the researchers considered measurements for the six anthropometric traits from 20 population studies. Based on information for as many as 82,355 individuals, they came up with six averaged principal components for considering body shape traits in a multi-dimensional, composite manner.
The team focused in on four of the averaged principal components, which appeared to be heritable and together explained at least 99 percent of variance in the traits considered. Starting with genotyped and imputed SNP patterns for almost 133,400 individuals from 43 past studies, the first stage of the GWAS meta-analysis highlighted 385 suspicious loci.
When they tested lead SNPs at candidate sites with genome-wide SNP data for 7,734 individuals from 10 more studies and Cardio-MetaboChip array profiles for 32,170 individual profiled for a dozen other studies, the researchers identified independent genome-wide associations for 183 previously detected loci.
They also tracked down six new loci associated with three of the averaged principle components: sites in and around the LEMD2, CD47, GANAB, RPS6KA5/C14orf159, ANP32, and ARL15 genes.
The team also took a look at the effectiveness of considering body shape traits as averaged principal components. For example, it found that 97 loci implicated in past studies of BMI appeared to explain almost 9 percent of variation in the averaged principal component related to weight. Variants at the same loci are estimated to explain less than 3 percent of variation in BMI when considered as a single trait.
The same approach is expected to prove useful for complementing single-trait GWAS focused on other features or conditions in the future, the researchers explained, though they noted that the composite phenotype approach is not intended to entirely replace those more traditional types of analyses.
"Large-scale GWAS meta-analyses of the [averaged principal components] identified six new loci that were not identified by previous singe-trait GWAS that were twice as large in sample size," the authors wrote. "This PCA approach could maximize gene discovery for other correlated traits, such as cancer, immune disease, hematologic traits, and so on, and may identify genes that point towards shared physiological pathways."