Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships among genetic loci and across traits. Methods that embrace pleiotropy (the effect of one locus on more than one trait), gene-environment (GxE) and gene-gene (GxG) interactions will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on an individual's genotype and environment. Additionally, a richer picture of the genetic and environmental aspects that impact complex disease will inform environmental regulations to protect vulnerable populations. Three key limitations of GWAS lead to an inability to robustly model trait prediction in a manner that reflects biological complexity: 1) GWAS explore traits in isolation, one phenotype at a time, preventing investigators from uncovering relationships that exist among multiple traits; 2) GWAS do not account for the exposome; rather, they simply explore the effect of genetic loci on an outcome; and 3) GWAS do not allow for interactions between genetic loci, despite the complexity that exists in biology. The aims described in this dissertation address these limitations. Methods employed in each aim have the potential to: uncover genetic interactions, unveil complex biology behind phenotype networks, inform public poolicy decisions concerning environmental exposures, and ultimately assess individual disease-risk.