Abstract
Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL “hotspots”), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock.
Highlights
Quantitative trait locus (QTL) analysis has been the primary tool for geneticists to study complex genetic traits in experimental organisms
Our results indicate that association analyses in mice are a powerful approach to the dissection of complex traits and their underlying molecular networks
The second filtering criterion was based on the recent report by Walter et al [18] which they showed that for the Affymetrix platform the presence of SNP within the 25mer probe sequence may affect the hybridization of transcripts and lead to artifactual detection of local expression QTL (eQTL). To investigate if this applies to the 50mer probe sequences of the Illumina microarrays used in the current study, we examined the degree of enrichment of SNPs in probes with local eQTL vs probes with no local eQTL
Summary
Quantitative trait locus (QTL) analysis has been the primary tool for geneticists to study complex genetic traits in experimental organisms. While such QTL mapping has great power to identify loci controlling the traits, resolution of mapping is usually quite low and as a result few candidate genes have been successfully identified using this approach. Some of the recent successes of this integrative approach have been identification of causal genes underlying the QTL for clinically relevant trait [1,2,3], the identification of genomic loci regulating the expression of biological pathway genes[4], the identification of genomic hotspots harboring master regulators [5,6,7], and prioritization of candidate genes underlying physiological trait QTLs [8]. Mathematical models have been developed to construct gene expression networks [9,10], deduce the causal relationship between different components of the network [11], and understand the transcriptional regulation of the genes [12]
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