Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets
2016
Significance We present a transcription-factor–centric computational method that utilizes data on natural variation in mRNA abundance to identify genetic loci that influence the responsiveness of genes to variation in the activities of their regulators. We call these connectivity quantitative trait loci, or cQTLs. When testing our method in yeast, we identified a polymorphism that modulates the mating response and confirmed our prediction experimentally. Our approach provides a sophisticated and nuanced approach to understanding the influence of genetic variation on interactions within regulatory networks. Applying our approach to human data may improve our understanding of the impact of genetic variation in contributing to phenotypic differences among individuals.
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