Resolving candidate genes of mouse skeletal muscle QTL via RNA-Seq and expression network analysesReport as inadecuate

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BMC Genomics

, 13:592

Transcriptomic methods


BackgroundWe have recently identified a number of Quantitative Trait Loci QTL contributing to the 2-fold muscle weight difference between the LG-J and SM-J mouse strains and refined their confidence intervals. To facilitate nomination of the candidate genes responsible for these differences we examined the transcriptome of the tibialis anterior TA muscle of each strain by RNA-Seq.

Results13,726 genes were expressed in mouse skeletal muscle. Intersection of a set of 1061 differentially expressed transcripts with a mouse muscle Bayesian Network identified a coherent set of differentially expressed genes that we term the LG-J and SM-J Regulatory Network LSRN. The integration of the QTL, transcriptome and the network analyses identified eight key drivers of the LSRN Kdr, Plbd1, Mgp, Fah, Prss23, 2310014F06Rik, Grtp1, Stk10 residing within five QTL regions, which were either polymorphic or differentially expressed between the two strains and are strong candidates for quantitative trait genes QTGs underlying muscle mass. The insight gained from network analysis including the ability to make testable predictions is illustrated by annotating the LSRN with knowledge-based signatures and showing that the SM-J state of the network corresponds to a more oxidative state. We validated this prediction by NADH tetrazolium reductase staining in the TA muscle revealing higher oxidative potential of the SM-J compared to the LG-J strain p<0.03.

ConclusionThus, integration of fine resolution QTL mapping, RNA-Seq transcriptome information and mouse muscle Bayesian Network analysis provides a novel and unbiased strategy for nomination of muscle QTGs.

KeywordsFunctional genomicsQTLSkeletal muscleGene expressionElectronic supplementary materialThe online version of this article doi:10.1186-1471-2164-13-592 contains supplementary material, which is available to authorized users.

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Author: ArimantasLionikas - CarolineMeharg - JonathanMJDerry - AivarasRatkevicius - AndrewMCarroll - DavidJVandenbergh - DavidABlizard


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