Our proposed JLS testing framework, based on the working model of Equation 2, tests the following null hypothesis:H0joint:βG=0andσi=σjforalli≠j,i,j=0,1,2.The alternative hypothesis of interest isH1joint:βG≠0orσi≠σjforsomei≠j.For a SNP under study, different JLS test statistics can be considered. Let pL be the p value for the location test of choice (i.e., testing H0location:βG=0 using, for example, ordinary least-squares regression), and pS be the p value for the scale test of choice (i.e., testing H0scale:σi=σjforalli≠j using, for example, Levene’s test). We first consider Fisher’s method (JLS-Fisher) to combine the association evidence from the individual location and scale tests. The JLS-Fisher statistic is defined asWF=−2(log(pL)+log(pS)).