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Activity Number: 186
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #312256
Title: Multivariate Polynomial Temporal Genetic Association and Genetic Causality Methods
Author(s): Luan Lin*+ and Kayee Yeung and Roger E. Bumgarner and Eric E. Schadt and Jun E. Zhu
Companies: Icahn school of medicine at Mount Sinai and University of Washington and University of Washington and Icahn School of Medicine at Mount Sinai and Icahn School of Medicine at Mount Sinai
Keywords: time series ; causal inference ; temporal ; genomic association
Abstract:

Today it is possible to score many millions of features in living systems using advanced technologies and it is becoming routine to score them over time. The integrated nature of biological systems, with the many omics interacting in complex ways, gives rise to a complex array of correlations that underlie the living systems. One of the primary limitation is elucidating the cause-effect relationships among the many millions of variables. Causal inferences from time series and genetic perturbation data have become instrumental in resolving causal relationships. Here we develop a multivariate approach for detecting time-sensitive genetic loci for quantitative traits monitored over time in a population, and then integrate these time-based genetic perturbations with the corresponding quantitative traits to infer causal relationships. We demonstrate the power of this approach by applying it to a population of yeast that was molecularly profiled over time in response to the drug rapamycin. We demonstrate significantly increased power to detect genetic loci modulating gene expression traits over time and to resolve the causal regulators of dynamic expression quantitative loci hot spots.


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