Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification.

Document Type

Article

Publication Date

2008

Keywords

Blotting-Western, Codon, Crosses-Genetic, Gene-Expression-Profiling, Gene-Expression-Regulation, Mass-Spectrometry, Mice-Inbred-Strains, Open-Reading-Frames, Polymorphism-Single-Nucleotide, Proteins, Proteomics, Quantitative-Trait-Loci, Quantitative-Trait-Heritable, RNA-Messenger

First Page

1795

Last Page

1805

JAX Source

Genetics 2008 Mar; 178(3):1795-805.

Abstract

Previous quantitative trait locus (QTL) analysis of an intercross involving the inbred mouse strains NZB/BlNJ and SM/J revealed QTL for a variety of complex traits. Many QTL have large intervals containing hundreds of genes, and methods are needed to rapidly sort through these genes for probable candidates. We chose nine QTL: the three most significant for high-density lipoprotein (HDL) cholesterol, gallstone formation, and obesity. We searched for candidate genes using three different approaches: mRNA microarray gene expression technology to assess >45,000 transcripts, publicly available SNPs to locate genes that are not identical by descent and that contain nonsynonymous coding differences, and a mass-spectrometry-based proteomics technology to interrogate nearly 1000 proteins for differential expression in the liver of the two parental inbred strains. This systematic approach reduced the number of candidate genes within each QTL from hundreds to a manageable list. Each of the three approaches selected candidates that the other two approaches missed. For example, candidate genes such as Apoa2 and Acads had differential protein levels although the mRNA levels were similar. We conclude that all three approaches are important and that focusing on a single approach such as mRNA expression may fail to identify a QTL gene.

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