Within-sibship GWAS improve estimates of direct genetic effects

Abstract

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

Using Mendelian randomization analysis to better understand the relationship between mental health and substance use: a systematic review

“…This can be prevented by performing MR with genetic estimates from within-family GWAS, as these will be corrected for all factors shared within families. Finding large enough family samples will be an important challenge in coming years [the first of such efforts recently became available (Howe et al, 2021 ). Finally, it is important to acknowledge that almost all MR studies were based on cohorts including participants of European descent.…”

Geographic Confounding in Genome-Wide Association Studies

“…One efficient way to control for SES-related gene-environment correlations is by conducting within family GWAS analyses. [23][24][25] Within family analyses, however, do not allow us to identify and study the specific source of gene-environment correlations, where our analyses do. In addition, while family-datasets are growing, large sample sizes of genotyped families are harder to attain, which results in less powerful within-family GWASs with noisier estimates of genetic effects (Figure 4).…”

Geographic Confounding in Genome-Wide Association Studies

“…One efficient way to control for SES-related gene-environment correlations is by conducting within family GWAS analyses. [23][24][25] Within family analyses, however, do not allow us to identify and study the specific source of geneenvironment correlations, where our analyses do. In addition, while family-datasets are growing, large sample sizes of genotyped families are harder to attain, which results in less powerful within-family GWASs with noisier estimates of genetic effects (Figure 4).…”