Two variance component model improves genetic prediction in family data sets

George Tucker , Po-Ru Loh , Iona M MacLeod , Ben J Hayes , Michael E Goddard , Bonnie Berger , Alkes L Price
doi: http://dx.doi.org/10.1101/016618

Genetic prediction based on either identity by state (IBS) sharing or pedigree information has been investigated extensively using Best Linear Unbiased Prediction (BLUP) methods. However, methods to combine IBS sharing and pedigree information for genetic prediction in humans have not been explored. We introduce a two variance component model for genetic prediction: one component for IBS sharing and one for approximate pedigree structure, both estimated using genetic markers. In simulations using real genotypes from CARe and FHS family cohorts, we demonstrate that the two variance component model achieves gains in prediction r2 over standard BLUP at current sample sizes, and we project based on simulations that these gains will continue to hold at larger sample sizes. Accordingly, in analyses of four quantitative phenotypes from CARe and two quantitative phenotypes from FHS, the two variance component model significantly improves prediction r2 in each case, with up to a 16% relative improvement. We also find that standard mixed model association tests can produce inflated test statistics in datasets with related individuals, whereas the two variance component model corrects for inflation.