“Improved Genetic Prediction of Complex Traits from Individual-Level Data or Summary Statistics”, 2020-08-24 (; backlinks; similar):
At present, most tools for constructing genetic prediction models begin with the assumption that all genetic variants contribute equally towards the phenotype. However, this represents a sub-optimal model for how heritability is distributed across the genome. Here we construct prediction models for 14 phenotypes from the UK Biobank (200,000 individuals per phenotype) using 4 of the most popular prediction tools: lasso, ridge regression, Bolt-LMM and BayesR.
When we improve the assumed heritability model, prediction accuracy always improves (ie. for all 4 tools and for all 14 phenotypes). When we construct prediction models using individual-level data, the best-performing tool is Bolt-LMM; if we replace its default heritability model with the most realistic model currently available, the average proportion of phenotypic variance explained increases by 19% (s.d. 2), equivalent to increasing the sample size by about a quarter.
When we construct prediction models using summary statistics, the best tool depends on the phenotype. Therefore, we develop MegaPRS, a summary statistic prediction tool for constructing lasso, ridge regression, Bolt-LMM and BayesR prediction models, that allows the user to specify the heritability model.