ABSTRACT
Gene expression plays a key role in health and disease. Estimating the genetic components underlying gene expression can thus help understand disease etiology. Polygenic models termed "transcriptome imputation" are used to estimate the genetic component of gene expression, but these models typically consider only the cis regions of the gene. However, these cis-based models miss large variability in expression for multiple genes. Transcription factors (TFs) that regulate gene expression are natural candidates for looking for additional sources of the missing variability. We developed a hypothesis-driven approach to identify second-tier regulation by variability in TFs. Our approach tested two models representing possible mechanisms by which variations in TFs can affect gene expression: variability in the expression of the TF and genetic variants within the TF that may affect the binding affinity of the TF to the TF-binding site. We tested our TF models in whole blood and skeletal muscle tissues and identified TF variability that can partially explain missing gene expression for 1035 genes, 76% of which explains more than the cis-based models. While the discovered regulation patterns were tissue-specific, they were both enriched for immune system functionality, elucidating complex regulation patterns. Our hypothesis-driven approach is useful for identifying tissue-specific genetic regulation patterns involving variations in TF expression or binding.