The Human Genome Project, HapMap, and other large collaborative projects motivated the development of new technologies and directly contributed to shared computational and genomic data resources that dramatically accelerated the ability to perform GWAS. DNA microarrays enabled cost-effective association testing of common genetic variants across the whole genome with a disease or other trait (Table S1). The success of GWAS prompted the development of analytic methods for leveraging genome-wide variation to estimate heritability and individual-level genetic risk, among other applications (6). Here we focus on risk prediction, but the goals, approaches, and advances in GWAS have also been reviewed (7; 8).
