Using Obviously-Related Instrumental Variables to Increase the Predictive Power of Polygenic Scores

ABSTRACT

The conventional way of boosting the predictive power of polygenic scores is to increase the GWAS sample size by meta-analyzing GWAS results of multiple cohorts. In this paper, we challenge this convention. Through simulations, we show that Instrumental Variable (IV) regression using two polygenic scores constructed from independent GWAS summary statistics outperforms the typical Ordinary Least Squares (OLS) model employing a single meta-analysis based polygenic score in terms of bias, root mean squared error, and statistical power. We verify the empirical validity of the simulations by predicting educational attainment (EA) and height in a sample of siblings from the UK Biobank. We show that IV regression between-families approaches the SNP-based heritability, and improves the predictive power of polygenic scores by 12% (height) to 22% (EA). Furthermore, IV regression within-families provides the tightest lower bound for the direct genetic effect, increasing the lower bound for EA from 2.0% to 3.4%, and for height from 28.9% to 37.7%.