Conditional analysis was performed by including variants identified in fine mapping with the lowest P-values as covariates in another round of admixture mapping in GWS regions. A conditional analysis P-value >0.01 indicated that the variants included as covariates in admixture mapping were the driving factors of an admixture mapping association signal. We first tested each variant individually. If single variants did not explain the admixture mapping signal, then we tested multiple variants following the framework proposed by Molineros et al (Molineros et al., 2013). Starting with the variant with the lowest P-value, we added the variant having the next lowest P-value and not in LD (defined as r2<0.5) with variants that were already in the model, one at a time, until the conditional P-value was greater than 0.01.
