Methodologically, gene-based approaches and multi-marker association tests have been developed as alternatives to traditional single-variant tests. By conducting tests of association on biologically informed aggregates of SNPs, such tests seek to evaluate a priori functionally relevant units of the genome and, in many cases, reduce the multiple-testing penalty that plague single-variant approaches, by 10 to 100 fold. The incorporation of -omics data, such as those being generated by high-resolution transcriptome studies, provides a means to extend genome-wide association studies by addressing the functional gap. Technological advances in high-throughput methods have reinforced the important finding that intermediate molecular phenotypes are under significant genetic regulation, with expression quantitative trait loci (eQTLs) as the predominant example. However, approaches that fully leverage the comprehensive regulatory knowledge generated by transcriptome studies are relatively lacking despite the fact that these studies have the potential to dramatically improve our understanding of the genetic basis of complex traits13.
