Literature DB >> 24963006

Discovery and refinement of muscle weight QTLs in B6 × D2 advanced intercross mice.

P Carbonetto1, R Cheng2, J P Gyekis3, C C Parker4, D A Blizard3, A A Palmer1, A Lionikas5.   

Abstract

The genes underlying variation in skeletal muscle mass are poorly understood. Although many quantitative trait loci (QTLs) have been mapped in crosses of mouse strains, the limited resolution inherent in these conventional studies has made it difficult to reliably pinpoint the causal genetic variants. The accumulated recombination events in an advanced intercross line (AIL), in which mice from two inbred strains are mated at random for several generations, can improve mapping resolution. We demonstrate these advancements in mapping QTLs for hindlimb muscle weights in an AIL (n = 832) of the C57BL/6J (B6) and DBA/2J (D2) strains, generations F8-F13. We mapped muscle weight QTLs using the high-density MegaMUGA SNP panel. The QTLs highlight the shared genetic architecture of four hindlimb muscles and suggest that the genetic contributions to muscle variation are substantially different in males and females, at least in the B6D2 lineage. Out of the 15 muscle weight QTLs identified in the AIL, nine overlapped the genomic regions discovered in an earlier B6D2 F2 intercross. Mapping resolution, however, was substantially improved in our study to a median QTL interval of 12.5 Mb. Subsequent sequence analysis of the QTL regions revealed 20 genes with nonsense or potentially damaging missense mutations. Further refinement of the muscle weight QTLs using additional functional information, such as gene expression differences between alleles, will be important for discerning the causal genes.
Copyright © 2014 the American Physiological Society.

Entities:  

Keywords:  QTL mapping; advanced intercross line; complex trait QTLs; forward genetics; genetic architecture; genome-wide association studies; hypertrophy; inbred strains; linear mixed models; mouse genetics; muscle wasting; quantitative trait loci

Mesh:

Substances:

Year:  2014        PMID: 24963006      PMCID: PMC4137148          DOI: 10.1152/physiolgenomics.00055.2014

Source DB:  PubMed          Journal:  Physiol Genomics        ISSN: 1094-8341            Impact factor:   3.107


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