Polygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, rendering external summary statistics superfluous. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied to PGS calculations, with one of the consequences being the conflation of genetic correlation with environmental correlation. Our simulations show that the impact of overfitting due to the overlap between the Target and the Validation data (OTV) is much less than overfitting due to the overlap between the Target and the Discovery data (OTD), and that a large sample size can vastly reduce OTD in terms of correlation. However, tests of genetic correlations will still be affected by OTD due to increased power. A proposal called cross prediction is offered whereby both OTD and OTV can be avoided when calculating PGS without external summary statistics. Software is made available for implementation of the methods.2

We present lassosum2, a new version of the polygenic score method lassosum, which we re-implement in R package bigsnpr. This new version uses the exact same input data as LDpred2 and is also very fast, which means that it can be run with almost no extra coding nor computational time when already running LDpred2. It can also be more robust than LDpred2, e.g. in the case of a large GWAS sample size misspecification. Therefore, lassosum2 is complementary to LDpred2.