Decoding genotype-phenotype relationships using temporal gene expression profiling

Himanshu Sinha, Department of Biotechnology and Initiative for Biological Systems Engineering, IIT Madras

The causal path from a genetic variant to a complex phenotype such as disease progression is often not known. Studying static gene expression variation, a commonly used technique, cannot distinguish causative from correlative genes. Moreover, this technique becomes a challenge especially when studying developmental and physiological traits, since they involve dynamic processes contributing to the variation. We addressed the challenge of how dynamic phenotypes are modulated by causal variants using yeast as a model. By studying temporal genome-wide gene expression variation, in presence of a single variant in MKT1 and TAO3 mitotic genes, novel causal networks and pathways regulating meiosis and sporulation were identified. Our results demonstrate that studying the causal effects of genetic variation on the underlying molecular network has the potential to provide novel and a more extensive understanding of the pathways driving a complex trait.