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 and Jun Zhu ()
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Luan Lin: Icahn School of Medicine at Mount Sinai
Quan Chen: Icahn School of Medicine at Mount Sinai
Jeanne P. Hirsch: Icahn School of Medicine at Mount Sinai
Seungyeul Yoo: Icahn School of Medicine at Mount Sinai
Kayee Yeung: University of Washington
Roger E. Bumgarner: University of Washington
Zhidong Tu: Icahn School of Medicine at Mount Sinai
Eric E. Schadt: Icahn School of Medicine at Mount Sinai
Jun Zhu: Icahn School of Medicine at Mount Sinai

Nature Communications, 2018, vol. 9, issue 1, 1-15

Abstract: 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.

Date: 2018
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DOI: 10.1038/s41467-018-06203-3

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