GEMMA12 was used separately in each Yale-Penn subgroup of EAs and AAs, with adjustment for sex, age, body mass index (BMI), the first three PCs of ancestry, and the degree of relatedness among subjects. BMI was used as covariant because of the consideration that it could be a potential confounding factor affecting Ca-HL. GEMMA uses linear mixed model to determine the association between SNP and phenotype; it can account for relatedness among participants and can control for population stratification and other confounding factors12. We used GEMMA because it allowed us to account for ancestry. A correction for inflation was not necessary, because the inflation was low (all λ < 1.1) in our GWAS. For summary statistics from GEMMA, the inverse variance method implemented in PLINK1.9 was used to generate fixed-effects meta-analysis P-values (meta-P) for all variants by matching their chromosomal positions and two alleles among the GWAS datasets from EAs and AAs separately. We used a GWS threshold of P < 5.0 × 10−8. GWAS summary statistics data for Yale-Penn samples are freely available upon request.
