Temporal genetic association and temporal genetic causality methods for dissecting complex networks

Luan Lin, Quan Chen, Jeanne P. Hirsch, Seungyeul Yoo, Kayee Yeung, Roger E. Bumgarner, Zhidong Tu, Eric E. Schadt, Jun Zhu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems. Both genetic and temporal models can be used to establish causal relationships among molecular, cellular, or phenotypical traits, but with limitations. To fully utilize high-dimension temporal and genetic data, we develop a multivariate polynomial temporal genetic association (MPTGA) approach for detecting temporal genetic loci (teQTLs) of quantitative traits monitored over time in a population and a temporal genetic causality test (TGCT) for inferring causal relationships between traits linked to the locus. We apply MPTGA and TGCT to simulated data sets and a yeast F2 population in response to rapamycin, and demonstrate increased power to detect teQTLs. We identify a teQTL hotspot locus interacting with rapamycin treatment, infer putative causal regulators of the teQTL hotspot, and experimentally validate RRD1 as the causal regulator for this teQTL hotspot.

Original languageEnglish
Article number3980
JournalNature Communications
Volume9
Issue number1
DOIs
StatePublished - 1 Dec 2018

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