Research highlight
QTL to candidate genes using a multiple-approach process
When working with complex traits, it’s essential to quickly and effectively sort through dozens, perhaps hundreds, of genes in a QTL to identify candidate genes. Researchers led by Beverly Paigen, Professor at The Jackson Laboratory, having identified QTL for a variety of complex traits, employed a systematic, multiple-approach process that both reduced the candidate genes to a manageable number and identified candidates that would have been missed using a single approach. The published results first appeared online in Genetics in February 2008.
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.
Stylianou IM, Affourtit JP, Shockley KR, Wilpan RY, Abdi FA, Bhardwaj S, Rollins J, Churchill GA, Paigen B. Applying gene expression, proteomics and single-nucleotide polymorphism analysis for complex trait gene identification. Genetics. 2008 Mar;178(3):1795-805. Epub 2008 Feb 3.
