NEW YORK – An international team led by investigators at the University of Bristol has demonstrated that sibling-centered genome-wide association studies can more clearly delineate inherited, or direct, genetic effects on traits or conditions, while weeding out biases related to factors such as population stratification, assortative mating, or other parental influences.
"The main advantage of sibling-based designs is that they can control for sources of bias that can affect designs using samples of unrelated individuals," co-senior and co-corresponding author Neil Davies, a senior research fellow with the University of Bristol's MRC Integrative Epidemiology Unit, explained in an email.
"Our study illustrates the importance of collecting genome-wide data from families to understand the effects of inherited genetic variation on phenotypes that are affected by assortative mating, population stratification, and indirect genetic effects," he and his colleagues wrote.
Using data for nearly 178,100 siblings from 77,832 sibling sets, or sibships, that were enrolled in 19 research cohorts, the investigators searched for variants associated with more than two dozen phenotypes, comparing associations within and across families to distinguish between sibship genome-wide associations and population GWAS signals. For each trait of interest, they relied on data for between 13,375 and 163,748 participants, or roughly 80,000 participants per trait, on average.
"A key aim of GWAS is to estimate direct genetic effects on phenotypes, but other sources of genetic associations can be extremely informative," the authors noted. "For example, knowledge of indirect genetic effects can be used to elucidate maternal effects or the extent to which health outcomes are mediated by family environments."
The findings, published in Nature Genetics on Monday, highlighted traits or conditions with GWAS results that are either more or less affected by the sibling-centered strategy — from a pared down collection of SNP associations with traits such as height or educational attainment to apparent heritability or potential genetic ties between different conditions.
"We found that within-sibship meta-analysis GWAS [SNP association] estimates are smaller than population estimates for seven phenotypes (height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms, and smoking)," Davies and his co-authors wrote. "We show that these differences in GWAS estimates, which are likely to partially reflect demographic and indirect genetic effects, can affect downstream analyses such as estimates of heritability, genetic correlations, and [Mendelian randomization]."
When it came to some traits, such as height, the team's sibship or cross-population GWAS meta-analysis on individuals of European ancestry pointed to pronounced polygenic effects.
The finding also supported the notion that non-direct genetic effects, rather than direct impacts of some trait- or disease-linked variants, may account for a significant proportion of associations detected during typical GWAS analyses, Davis explained, noting that this seemed to be the case for social or psychological traits in particular.
Even so, he noted that the sibship GWAS approach would not be ideal in every circumstance. While the method can help in delineating the direct biological effects of specific variants, for example, SNP-based disease risk predictions are expected to be more accurate when they include direct and indirect factors that coincide with the condition of interest in a given population.
"If you're interested in prediction … you're better off with very large samples of unrelated individuals (the bias actually helps with prediction)," Davies noted. "But if you're interested in etiology and how the genome affects peoples' outcomes, [the sibling-informed GWAS approach] is super important."