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.
Highlights
A large amount of panomic data has been generated in populations for understanding causal relationships in complex biological systems
For example (Fig. 1c), if two traits are related via a negative feedback loop, the sign of their correlation and the direction of the causal relationship inferred from a Genetics-based causal (GC) approach would be determined by the average strength of the genetic perturbations on each trait in the population[9]
We can consider a quantitative trait following a polynomial function with regard of time and employ a straightforward regression approach to model the trait with respect to a given genetic locus
Summary
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. A transcription factor binding to a stretch of DNA12 and facilitating the transcription of a gene that in turn activates a given biological pathway[13] Another type of causal relationships inferred through statistical causality tests has achieved widespread utility[5,14]. This type of causal relationships is considered as a weak form of causality and experimental follow-ups are generally needed to validate them. This weak causality enables us to orient the vast sea of correlations observed among hundreds of thousands of molecular phenotypes that can be simultaneously assayed, according to the direction of information flow
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