Lin Chen
In this talk, I will propose a causal framework that utilizes the naturally
randomized genotypes to rigorously infer causal regulatory relationships
among genes and traits. Based on experiments in which genotyping and
expression profiling are performed, we propose a non-parametric empirical
Bayesian method to estimate the posterior regulatory probability for any
directed pair of gene transcripts in the genome. The resulting posterior
probabilities can further be used to build transcriptional regulatory networks.
We demonstrate this method on an experiment in yeast, in which genes
known to be in the same processes and functions are recovered in the
resulting transcriptional regulatory network. Building on the causal
framework, I will present a second method to identify the genes whose
transcription levels are causal for a quantitative trait of interest, resulting in a
p-value calculation for each potential causal relationship. This method also
involves extending the theoretical framework to accommodate hidden
variables with large-scale effects. I will present applications of this method
to experiments in yeast and mouse.
