Genome wide association studies (GWAS) identified thousands of genetic loci associated with a variety of complex traits and diseases. Nonetheless, deciphering how most GWAS variants are linked to diseases remains exceptionally challenging, as the majority of these variants are noncoding. Noncoding variation can affect the regulation of genes that are either physically nearby (in cis), or physically distant (in trans). Mounting evidence suggest that genetic regulation in trans plays a dominant role in the control of gene expression and disease risk. Thus, high-quality trans-eQTL and trans regulatory networks are critically needed to fully understand how disease-associated variants flow through gene networks to affect causal genes and pathways. However, most studies to date have solely focused on studying genetic regulation in cis owing to the extreme difficulty in detecting trans-QTLs. Therefore, the lack of high-quality trans-eQTL maps represents a significant gap in our understanding of disease mechanisms. In this grant, I propose to address this gap by developing powerful statistical methods to produce high-quality, comprehensive maps of trans-QTLs in multiple human tissues and cell types. We will use these maps to uncover major mechanisms that underlie trans genetic regulation. Finally, we will develop novel methods to identify disease genes using our high-quality maps of trans-eQTLs.
Most genetic effects that impact gene regulation and complex traits act in trans, but very little is known about trans-genetic effects, largely due to the difficulty in detecting trans-QTLs. We propose to develop powerful methods to generate high-quality, comprehensive maps of trans-QTLs in multiple human tissues. We will use these maps to understand the molecular mechanisms of trans gene regulation and to identify new disease genes.
