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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

Prior knowledge guided eQTL mapping for identifying candidate genes.

Yunli Wang¹, Rene Richard², Youlian Pan³

¹National Research Council Canada, 1200 Montreal Rd., Ottawa, K1A 0R6, Canada. Yunli.Wang@nrc-cnrc.gc.ca.

BMC Bioinformatics

|December 15, 2016

Summary

This summary is machine-generated.

This study introduces a data-driven approach for expression quantitative trait loci (eQTL) mapping using prior knowledge to identify candidate genes. This method enhances accuracy in complex trait analysis, outperforming traditional eQTL mapping techniques.

Keywords:

Candidate genes Lasso Prior knowledge eQTL mapping

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Area of Science:

Genetics
Bioinformatics
Plant Science

Background:

Expression quantitative trait loci (eQTL) mapping identifies genetic loci linked to traits.
Traditional eQTL methods often treat genes independently, overlooking complex trait interactions.
Limited biological prior knowledge hinders eQTL mapping in many species.

Purpose of the Study:

To develop a data-driven, prior knowledge-guided eQTL mapping method for identifying candidate genes.
To improve the accuracy of eQTL mapping by integrating quantitative trait loci (QTL) and gene co-expression data.
To validate the proposed method using simulations and barley stem rust resistance case studies.

Main Methods:

Utilized quantitative trait loci (QTL) analysis to identify trait-associated single nucleotide polymorphisms (SNPs).
Generated co-expressed gene modules and selected those significantly associated with traits.
Applied prior knowledge from QTL mapping to guide eQTL analysis on selected gene modules.

Main Results:

Prior knowledge-guided eQTL mapping outperformed methods without prior knowledge in simulations and barley case studies.
Identified gene modules enriched with defense response Gene Ontology (GO) terms in barley stem rust resistance.
Successfully mapped a probe to Rpg1, a known stem rust resistance gene, in one case study.

Conclusions:

Prior knowledge-guided eQTL mapping is an effective and robust method for candidate gene identification.
This approach significantly improves upon traditional eQTL mapping methods lacking prior biological insights.
Demonstrated utility in identifying disease resistance genes in barley, highlighting its practical applications.