As highlighted above (see Improvements in PRS estimation), several techniques can be leveraged to improve PRS in one’s own data, most of which do not require any, or only minimal, additional data. While they require additional work to compute, the potential pay-off cannot be overstated, as the improvement in accuracy will result in a substantially better-powered study. Here we describe one hypothetical workflow for how these techniques may be integrated into future analyses. First, MTAG may be used to generate summary statistics for polygenic risk scores by jointly analyzing the trait of interest with genetically-correlated traits (Turley et al., 2017). Instead of relying on summary statistics from a single GWAS study, MTAG may be used to integrate information from genetically-correlated phenotypes to improve the precision of summary statistics that can be leveraged for PRS construction. Importantly, summary statistic files can come from any study, as long as they are all of the same ethnicity - this includes partially or fully overlapping samples. The primary restriction on which studies can be included is that none of them should have substantially greater power than the study of the trait of interest, as this will increase false-positives.
