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Re:Seminar - Prof. Jiahua Chen - Nov 1



Friendly reminder of tomorrows seminar

From: "Jinko Graham" <jgraham@sfu.ca>
To: stat-sem@sfu.ca
Sent: Saturday, October 26, 2013 9:39:31 AM
Subject: Seminar - Prof. Jiahua Chen - Nov 1

Jiahua Chen, Professor, Department of Statistics, UBC

All are Welcome to the Seminar and “Meet-the-Speaker”

Date:        November 01, 2013
Time:        1:30 p.m.
“Meet-the-Speaker” session directly after the talk
Room: ASB 10900
Light refreshments at 1:15 p.m. – IRMACS Atrium
No registration required

TITLE:
Imprinting Test for Disease-Associated SNPs Under Mixture Model
ABSTRACT:

Genomic imprinting is a known aspect of the etiology of schizophrenia, a serious and common neuropsychiatric disease. The imprinting phenomenon depicts differential _expression_ levels of the allele depending on its parental origin. When the parental origin is unknown, the _expression_ level has a finite normal mixture distribution. In such applications, a random sample of _expression_ levels consists of three subsamples according to the number of minor alleles an individual possesses, of which one is the mixture and the other two are homogeneous. This understanding leads to a likelihood ratio test (LRT) for the presence of imprinting. Because of the nonregularity of the finite mixture model, the classical asymptotic conclusions on likelihood-based inference are not applicable. We show that the maximum likelihood estimator of the mixing distribution remains consistent. More interestingly, thanks to the homogeneous subsamples, the LRT statistic has an elegant and rather distinct null limiting distribution. Simulation studies confirm that the limiting distribution provides precise approximations of the finite sample distributions under various parameter settings. The LRT is applied to _expression_ data sets for the schizophrenia susceptibility gene GABRB2. Our analyses provide evidence for imprinting for a number of isoform expressions of its GABA_A receptor beta_2 subunit protein encoded by the GABRB2 gene.

Based on joint work with: Shaoting Li, Jianhua Guo, Bing-Yi Jing, and Hong Xue