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doi:10.1534/genetics.107.081216
A more recent version of this article appeared on March 1, 2008.
REGULAR RESEARCH PAPERS |
Applying Gene Expression, Proteomics and SNP Analysis for Complex Trait Gene Identification
Ioannis M Stylianou 1, Jason P Affourtit 2, Keith R Shockley 2, Rob Y Wilpan 2, Fadi A Abdi 3, Sanjeev Bhardwaj 3, Jarod Rollins 2, Gary A Churchill 2 and Beverly Paigen 2*
1 The Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USA
2 The Jackson Laboratory
3 Applied Biosystems
* To whom correspondence should be addressed. E-mail: bev.paigen{at}jax.org.
Submitted on August 28, 2007
Revised on October 22, 2007
Accepted on 16 January 2008
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 HDL-cholesterol, gallstone formation, and obesity. We searched for candidate genes using three different approaches; mRNA microarray gene expression technology to assess over 45,000 transcripts, publicly available SNPs to locate genes that are not identical by descent and that contain non-synonymous 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.
Key Words: Complex trait, HDL, QTL, microarray, obesity
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