A general issue for genetic risk prediction is that individual-level genotype and phenotype data are often subject to strict ethical and regulatory protections that limit access. Summary statistics from GWAS consortia are more commonly available, and some methods can predict genetic risk using these statistics rather than individual-level data. Methods that use individual-level genotype data can slightly outperform methods that use summary statistics as input, in part because they model data jointly rather than as marginal summary statistics, providing direct access to precise measures of correlation between variants rather than from reference panel estimates (29; 30). However, because this accuracy gain tends to be small, computationally efficient methods relying on summary statistics5 (Table 1) have been favored.
