Title	Heteroskedastic linear models for functional genomics
Creator	Engelhardt, Barbara
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2015-08-03T15:37
Description	Heteroskedasticity in linear models refers to the situation where a predictor -- here, a single nucleotide polymorphism (SNP) -- is correlated with the residual error of a linear model fitted to that SNP and a quantitative trait response. Previous work in functional genomics has identified examples of a genotype affecting not (only) the mean of the quantitative trait, but (also) its variance, implying a heteroskedastic association. Methods to identify these variance QTLs (vQTLs) are based on ANOVA tests or two-stage linear models. Here, we develop a test for heteroskedasticity based on a Bayesian heteroskedastic linear model. We show the power of this test for identifying vQTLs for various cellular traits. We extend the model to pairs of quantitative traits to address questions about causal relationships among cellular phenotypes.
Extent	34 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Princeton University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2017-01-29
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0340370
URI	http://hdl.handle.net/2429/60015
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Heteroskedastic linear models for functional genomics Engelhardt, Barbara

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