Second, PRS could be generated using MTAG generated summary statistics by LDpred, which As mentioned above, leverages LD structure and results in improved PRS predictive ability relative to traditional PRS computation. Notably, there are several other computational approaches for improving PRS accuracy, both by adjusting effect-sizes and by modeling LD-structure. These approaches all require additional training data, and as yet a single approach which reliably out-performs all others has yet to emerge. Thus, the choice of adjustment algorithm will largely depend on the availability and nature of training data. LDpred (Vilhjalmsson et al., 2015) is the most well-vetted of these algorithms, and is thus the one we can most confidently recommend. It requires a moderately sized reference panel (N≥1,000) and is the most computationally intense, but importantly, only additional genetic reference panel data are required. This may prove to be particularly useful for studies examining less easily-obtained phenotypes. More recently developed approaches also hold great promise, though they need further validation. For example, GraBLD (Pare et al., 2017) is notable for requiring only a small training dataset (N≥200) that includes
