Towards the Genetic Architecture of Complex Gene Expression Traits: Challenges and Prospects for eQTL Mapping in Humans

Abstract

The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model- and deep learning-based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.

Keywords: complex phenotype; expression quantitative trait locus; regulation of gene expression; statistical genetics; target gene.

Figure 1

Obstacles to expression quantitative trait locus (eQTL) identification. General obstacles to both cis-eQTL and trans-eQTL identification are presented in red, whereas obstacles to only trans-eQTL identification are presented in dark green. The asterisk indicates obstacles that increase the difficulty of inference of causality.