[12]–[14]. That is, the phenotype is affected by many genetic variants with small or modest effects effects that cannot be confirmed individually via statistical significance, which is usually referred to as “polygenicity”. The polygenicity of complex traits is further supported by recent GWAS with larger sample sizes, in which more associated common SNPs with moderate effects have been identified (e.g., [15]). Clearly, the emerging polygenic genetic architecture imposes a great challenge of identifying risk genetic variants: a larger sample size is required to identify genetic variants with smaller effect sizes. However, sample recruitment may be expensive and time-consuming. It would be desirable to find a way to increase power to detect variants that miss significance on standard GWAS without extensive additional subject recruitment requirements. Integrative analysis of genomic data could be a promising direction, including combining GWAS data of multiple genetically related phenotypes and incorporating relevant biological information.
