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 the trait of interest. It performs an adjustment for LD and updates effect sizes using a simple machine learning algorithm. Similarly, Lassosum (Mak et al., 2017) uses machine learning, penalized regression, to correct for LD structure and to update effect sizes. It is particularly notable as it can use cross-validation to replace an external training dataset.
