QTL for microstructural and biophysical muscle properties and body composition in pigs.
BACKGROUND
The proportion of muscle fibre types and their size affect muscularity as well as functional properties of the musculature and meat quality. We aimed to identify QTL for microstructural muscle properties including muscle fibre size, their numbers and fibre type proportions as well as biophysical parameters of meat quality and traits related to body composition, i.e. pH, conductivity, area of M. longissimus dorsi and lean meat content. A QTL scan was conducted in a porcine experimental population that is based on Duroc and Berlin Miniature Pig.


RESULTS
Least square regression interval mapping revealed five significant and 42 suggestive QTL for traits related to muscle fibre composition under the line-cross model as well as eight significant and 40 suggestive QTL under the half-sib model. For traits related to body composition and biophysical parameters of meat quality five and twelve significant plus nine and 22 suggestive QTL were found under the line-cross and half-sib model, respectively. Regions with either significant QTL for muscle fibre traits or significant QTL for meat quality and muscularity or both were detected on SSC1, 2, 3, 4, 5, 13, 14, 15, and 16. QTL for microstructural properties explained a larger proportion of variance than did QTL for meat quality and body composition.


CONCLUSIONS
Microstructural properties of pig muscle and meat quality are governed by genetic variation at many loci distributed throughout the genome. QTL analysis under both, the line-cross and half-sib model, allows detecting QTL in case of fixation or segregation of the QTL alleles among the founder populations and thus provide comprehensive insight into the genetic variation of the traits under investigation. Genomic regions affecting complex traits of muscularity and meat quality as well as microstructural properties might point to QTL that in first instance affect muscle fibre traits and by this in second instance meat quality. Disentangling complex traits in their constituent phenotypes might facilitate the identification of QTL and the elucidation of the pleiotropic nature of QTL effects.
Wimmers
Klaus
K
Research Unit Molecular Biology, Research Institute for the Biology of Farm Animals (FBN), 18196 Dummerstorf, Germany. wimmers@fbn-dummerstorf.de
Fiedler
Ilse
I
Hardge
Torsten
T
Murani
Eduard
E
Schellander
Karl
K
Ponsuksili
Siriluck
S
D000818:Animals; D001823:Body Composition / Q000235:genetics*; D002874:Chromosome Mapping / Q000662:veterinary; D005260:Female; D005819:Genetic Markers; D014644:Genetic Variation; D016018:Least-Squares Analysis; D008297:Male; D008460:Meat; D018485:Muscle Fibers, Skeletal / Q000648:ultrastructure; D018482:Muscle, Skeletal / Q000033:anatomy & histology / Q000254:growth & development; D040641:Quantitative Trait Loci; D013552:Swine; D013556:Swine, Miniature / Q000235:genetics* / Q000254:growth & development
D005819:Genetic Markers