Most fine-mapping algorithms (Huang et al., 2017; Schaid et al., 2018) can be applied to samples from multiple ancestries combined through meta-analysis. However, this strategy does not take full advantage of genomic diversity across populations. An alternate Bayesian fine-mapping strategy (Lam et al., 2018) more precisely mapped the schizophrenia genetic associations through explicitly modeling diversity in LD between East Asian and European samples. This approach works on a presumption that the causal variants and their effect sizes are identical across populations, which is not always true. PAINTOR (Kichaev and Pasaniuc, 2015) relaxes this presumption by allowing the effect size to vary across populations, although the causal variant still needs to be the same. Fine-mapping methods will benefit from continued development that appropriately models LD and relies on fewer assumptions.
