Genomewide Association Analysis in Diverse Inbred Mice: Power and Population Structure

Abstract

The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of an existing set of diverse inbred mice (referred to here as the mouse diversity panel, MDP) as a QTL mapping population. The use of the MDP population has many advantages relative to traditional F 2 mapping populations, including increased phenotypic diversity, a higher recombination frequency, and the ability to collect genotype and phenotype data in community databases. However, these methods are complicated by population structure inherent in the MDP and the lack of an analytical framework to assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the MDP. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTL (eQTL). We utilized these eQTL, and specifically cis-eQTL, to develop a novel nonparametric method for association analysis in structured populations like the MDP. These eQTL data confirmed that the MDP is a suitable mapping population for QTL discovery and that eQTL results can serve as a gold standard for relative measures of statistical power.

Correction for hidden confounders in the genetic analysis of gene expression

“…It is commonly believed that cis-acting SNPs have stronger associations than trans-acting SNPs (3) because the mode of action is more direct. Consequently, it is sometimes assumed that cis associations should tend to get lower p-values than trans associations, and that the extent to which this is so is a reflection of the quality of the analysis (31).…”

“…In order to have enough power to detect differences between different models, we required enough potential cis eQTL (here defined as within 500 Kb of the gene, as was done in refs. 14,30,31) in the set of hypotheses tested and so focused on SNPs and gene probes in chromosome 1 rather than a random set of hypotheses across the genome. We selected every sixth SNP and all gene probes on chromosome 1.…”

Fine Mapping in 94 Inbred Mouse Strains Using a High-Density Haplotype Resource

“…This in silico association mapping has two advantages: (1) it allows us to capture the full spectrum of diversity in the inbred strains rather than a subset used as progenitors of an experimental cross and (2) phenotypic noise can be minimized by performing replicates on genetically identical individuals. In particular, this approach should complement traditional QTL linkage mapping (often successful at locating large chromosomal segments) by providing a higher resolution, association-based component and indeed has already yielded several positive results (Grupe et al 2001;Liao et al 2004;Pletcher et al 2004;Guo et al 2006;Liu et al 2006;Moran et al 2006;Cervino et al 2007;Guo et al 2007;Liu et al 2007;McClurg et al 2007;Tang et al 2008). The basic idea behind this type of study is that a region is first identified through a genetic cross or some other means, resulting in a large QTL region typically in the tens of megabases in length that contains many genes.…”

Divergent and nonuniform gene expression patterns in mouse brain

“…The most appreciated and easily testable of these involve sequence modifications that alter the amino acid coding regions of genes and measurably affect protein function. Quantitative differences in transcript abundance, due to gene dosage or regulatory mutations have also been associated with phenotype (4)(5)(6). However, outside the realm of developmental biology and recent studies of copy number variation (7,8), the spatial distribution of gene expression within defined tissues and cell types remains relatively unexplored as a means by which genetic differences confer phenotypic differences.…”