There exists a large class of methods for interrogating genetic overlap via GWAS that focus only on genome-wide significant SNPs. One of the most influential methods in this class is Mendelian randomization, which uses significantly associated SNPs as instrumental variables to attempt quantify causal relationships between risk factors and disease[1, 2]. Methods that focus on significant SNPs are effective for traits where there are many significant associations that account for a substantial fraction of heritability [8, 9]. For many complex traits, heritability is distributed over thousands of variants with small effects, and the proportion of heritability accounted for by significantly associated variants at current sample sizes is small [10]. In such situations, one can often obtain more accurate results by using genome-wide data, rather than just significantly associated variants [11].
