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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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

Ioannis M Stylianou¹, Jason P Affourtit, Keith R Shockley

¹The Jackson Laboratory, Bar Harbor, Maine 04609, USA.

|February 5, 2008

Summary

Identifying candidate genes for complex traits requires multiple methods. Combining gene expression, SNP analysis, and proteomics effectively narrows down quantitative trait loci (QTL) for better genetic discovery.

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Area of Science:

Genetics
Genomics
Molecular Biology

Background:

Quantitative trait locus (QTL) analysis identifies genomic regions associated with complex traits.
Large QTL intervals contain numerous genes, necessitating efficient candidate gene identification methods.

Purpose of the Study:

To develop and evaluate a systematic approach for identifying candidate genes within significant QTL.
To compare the effectiveness of mRNA expression, SNP analysis, and proteomics in candidate gene discovery.

Main Methods:

Selected nine significant QTL for high-density lipoprotein cholesterol, gallstone formation, and obesity from an NZB/BlNJ x SM/J mouse cross.
Employed mRNA microarray analysis for gene expression profiling (>45,000 transcripts).
Utilized publicly available single nucleotide polymorphisms (SNPs) for nonsynonymous coding differences.
Applied mass spectrometry-based proteomics to assess differential protein expression in liver tissue (~1000 proteins).

Main Results:

The integrated approach significantly reduced candidate gene numbers within QTL from hundreds to a manageable list.
Each method identified unique candidate genes missed by the others (e.g., Apoa2, Acads showed differential protein but not mRNA levels).
Demonstrated the complementary nature of gene expression, SNP, and proteomic data in candidate gene identification.

Conclusions:

A multi-pronged strategy integrating gene expression, SNP, and proteomic data is crucial for effective candidate gene discovery within QTL.
Relying on a single method, such as mRNA expression alone, may lead to failure in identifying relevant QTL genes.
This systematic approach enhances the ability to pinpoint causative genes for complex traits.