Title: "Mixed Model Quantitative Trait Loci Analysis"
Speaker: Cherie A. Ochsenfeld, Department of Statistics, Purdue University
Place: LILY G126; April 14, 2009, Tuesday, 4:30pm


Quantitative Trait Loci (QTL) Analysis has been an effective tool for locating regions of the genome associated with a trait. Due to the unknown nature of the error terms and the complexity of the data, asymptotic thresholds have been difficult to derive. Permutation testing has successfully provided significance thresholds; although, due to the need for exchangeability, QTL analyses using permutation thresholds have been limited to simple linear models. This limitation does not allow researchers to include important covariates into the analysis.

In order to address this limitation, a mixed model that incorporate the ability to include both fixed and random effects into a QTL analyses is proposed. A bootstrapping algorithm is employed to establish significance thresholds that are appropriate for a Mixed Model QTL analysis. Simulation studies demonstrate an improved detection and estimation of additive effects in QTL studies when influential covariates are incorporated into the analysis model.

This work is advised by: Kristofer Jennings and R.W. Doerge

Recommended Reading:
Doerge, R.W., 2002 Mapping and analysis of quantitative trait loci in experimental populations. Nature Reviews. Genetics 3: 43-52.

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