Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

Yuna Blum,Sandrine Lagarrigue,David Causeur,Olivier Filangi,Guillaume Le Mignon,Pascale Le Roy,Olivier Demeure,Colette Désert

doi:10.1186/1471-2164-12-567

Yuna Blum, Sandrine Lagarrigue + Show 6 more

Open Access

https://doi.org/10.1186/1471-2164-12-567

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Abstract

BackgroundIntegrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM.ResultsUsing this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region.ConclusionOur results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.

Highlights

Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits
In the last decade, integrative genomics approaches that take into account genotypic, molecular profiling and complex traits in segregating populations have been developed to dissect the genetics of complex traits such as human diseases or economically important traits in livestock or plants
We propose to identify these genes using a method called Factor Analysis for Multiple Testing (FAMT) which takes into account the hidden dependence structure that may result from population structure or/and technical artefact of gene expression profiling experiment, independent of the trait of interest ([11], [13])

Summary

Introduction

Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation Another approach includes defining subtypes for a complex trait using transcriptome profiles and performing QTL mapping using some of these subtypes. One strategy commonly used by authors working in this context was based on the identification of genes having an eQTL that colocalizes with the QTL responsible for the complex trait of interest Such a strategy considers the expression level of each gene available on a microarray as a quantitative trait and uses genetic markers to identify genomic regions that regulate gene expression phenotypes; these regions are named eQTL (expression Quantitative Trait Loci). When combined with mathematical modeling proposed by Schadt et al [3], this strategy becomes very efficient for distinguishing causal from reactive genes for the complex trait and for identifying the “driver” genes and pathways that are responsible for a complex trait

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