Abstract
Standard genetic mapping techniques scan chromosomal segments for location of genetic linkage and association signals. The majority of these methods consider only correlations at single markers and/or phenotypes with explicit detailing of the genetic structure. These methods tend to be limited by their inability to consider the effect of large numbers of model variables jointly. In contrast, we propose a Bayesian analysis of variance (ANOVA) method to categorize individuals based on similarity of multidimensional profiles and attempt to analyze all variables simultaneously. Using Problem 1 of the Genetic Analysis Workshop 15 data set, we demonstrate the method's utility for joint analysis of gene expression levels and single-nucleotide polymorphism genotypes. We show that the method extracts similar information to that of previous genetic mapping analyses, and suggest extensions of the method for mining unique information not previously found.
Highlights
The extent to which the natural variation in gene expression is heritable has recently been the subject of some intriguing studies [1,2,3,4,5] the most notable being a study by Morley et al [6] in which expression phenotypes were analyzed as quantitative traits, yielding significant evidence for linkage between expression levels and singlenucleotide polymorphism (SNP) genotypes.The Problem 1 data set for the Genetic Analysis Workshop 15 (GAW15) consists of 3554 lymphoblastoid gene expression values observed in a sample of 194 healthy individuals from 14 three-generation CEPH (Centre d'Etude du Polymorphisme Humain) Utah families
In this paper we present our analysis of the GAW15 Problem 1 data set based on a variable-selection method tied to a Bayesian analysis of variance (ANOVA) model that has been shown to be effective in separating signal from noise within expression data
In order to determine which genes were most influential in the clustering we examined each gene via a one-way ANOVA F statistic using the assigned latent cluster labels as group indicators
Summary
The extent to which the natural variation in gene expression is heritable has recently been the subject of some intriguing studies [1,2,3,4,5] the most notable being a study by Morley et al [6] in which expression phenotypes were analyzed as quantitative traits, yielding significant evidence for linkage between expression levels and singlenucleotide polymorphism (SNP) genotypes. The Problem 1 data set for the Genetic Analysis Workshop 15 (GAW15) consists of 3554 lymphoblastoid gene expression values observed in a sample of 194 healthy individuals from 14 three-generation CEPH (Centre d'Etude du Polymorphisme Humain) Utah families. In this paper we present our analysis of the GAW15 Problem 1 data set based on a variable-selection method tied to a Bayesian ANOVA model that has been shown to be effective in separating signal from noise within expression data.
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