Variance component analysis of quantitative trait loci for pork carcass composition and meat quality on SSC4 and SSC111

Wijk, H.J. van; Buschbell, H.; Dibbits, Bert; Liefers, S.C.; Harlizius, B.; Heuven, H.C.M.; Knol, E.F.; Bovenhuis, H.; Groenen, Martien A. M.

doi:10.2527/jas.2006-063

Cited by 13 publications

(13 citation statements)

References 29 publications

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“…Our most prominent SNPs associated with meat colour a* and colour b* were on SSC4 und SSC14, respectively, where QTL were previously shown 22-23, 27-29. Further, Duthie and colleagues 21 detected a QTL for the pH of ham 24 hours post-mortem surrounding the same region of SSC14, which was also found in our study.…”

Section: Discussionsupporting

confidence: 87%

Discovery of Candidate Genes for Muscle Traits Based on GWAS Supported by eQTL-analysis

et al. 2014

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Biochemical and biophysical processes that take place in muscle under relaxed and stressed conditions depend on the abundance and activity of gene products of metabolic and structural pathways. In livestock at post-mortem, these muscle properties determine aspects of meat quality and are measurable. The conversion of muscle to meat mimics pathological processes associated with muscle ischemia, injury or damage in humans and it is an economic factor in pork production. Linkage, association, and expression analyses independently contributed to the identification of trait-associated molecular pathways and genes. We aim at providing multiple evidences for the role of specific genes in meat quality by integrating a genome-wide association study (GWAS) for meat quality traits and the detection of eQTL based on trait-correlated expressed genes and trait-associated markers. The GWAS revealed 51 and 200 SNPs significantly associated with meat quality in a crossbred Pietrain×(German Landrace×Large White) (Pi×(GL×LW)) and a purebred German Landrace (GL) population, respectively. Most significant SNPs in Pi×(GL×LW) were located on chromosomes (SSC) 4 and 6. The data of 47,836 eQTLs at a significance level of p<10-5 were used to scale down the number candidate genes located in these regions. These SNPs on SSC4 showed association with expression levels of ZNF704, IMPA1, and OXSR1; SSC6 SNPs were associated with expression of SIGLEC10 and PIH1D1. Most significant SNPs in GL were located on SSC6 and associated with expression levels of PIH1D1, SIGLEC10, TBCB, LOC100518735, KIF1B, LOC100514845, and two unknown genes. The abundance of transcripts of these genes in muscle, in turn, is significantly correlated with meat quality traits. We identified several genes with evidence for their candidacy for meat quality arising from the integrative approach of a genome-wide association study and eQTL analysis.

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Section: Discussionsupporting

confidence: 87%

Discovery of Candidate Genes for Muscle Traits Based on GWAS Supported by eQTL-analysis

et al. 2014

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“…for the same trait ranging from 0.19 to 0.55. Regarding backfat thickness, studies using the traditional restricted maximum likelihood (REML) method observed a heritability of 0.45 (van Wijk et al 2005). However, this study found lower heritability values for this trait (0.13-0.37).…”

Section: Resultsmentioning

confidence: 99%

Supervised independent component analysis as an alternative method for genomic selection in pigs

Azevedo

Silva

Resende

et al. 2014

J Animal Breeding Genetics

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The objective of this work was to evaluate the efficiency of the supervised independent component regression (SICR) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross-validation. These values were also compared with other methods generally used for the same purposes, such as RR-BLUP, SPCR, SPLS, ICR, PCR and PLS. The SICR method was found to have the most accurate prediction values.

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“…The first two QTL regions have moderate effects on meat color traits (about 0.33 STD), while the SSC13 QTL has an effect of 0.75 STD on meat lightness. Three QTL for meat quality traits were previously reported for the SSC1b, SSC4 and SSC8 regions by Ponsuksilii et al [47] and van Wijk et al [48] in crossbred populations, while the SSC1d QTL region has been shown to influence meat quality traits in Landrace pigs [49]. …”

Section: Discussionmentioning

confidence: 99%

A genome-wide association study of production traits in a commercial population of Large White pigs: evidence of haplotypes affecting meat quality

et al. 2014

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BackgroundNumerous quantitative trait loci (QTL) have been detected in pigs over the past 20 years using microsatellite markers. However, due to the low density of these markers, the accuracy of QTL location has generally been poor. Since 2009, the dense genome coverage provided by the Illumina PorcineSNP60 BeadChip has made it possible to more accurately map QTL using genome-wide association studies (GWAS). Our objective was to perform high-density GWAS in order to identify genomic regions and corresponding haplotypes associated with production traits in a French Large White population of pigs.MethodsAnimals (385 Large White pigs from 106 sires) were genotyped using the PorcineSNP60 BeadChip and evaluated for 19 traits related to feed intake, growth, carcass composition and meat quality. Of the 64 432 SNPs on the chip, 44 412 were used for GWAS with an animal mixed model that included a regression coefficient for the tested SNPs and a genomic kinship matrix. SNP haplotype effects in QTL regions were then tested for association with phenotypes following phase reconstruction based on the Sscrofa10.2 pig genome assembly.ResultsTwenty-three QTL regions were identified on autosomes and their effects ranged from 0.25 to 0.75 phenotypic standard deviation units for feed intake and feed efficiency (four QTL), carcass (12 QTL) and meat quality traits (seven QTL). The 10 most significant QTL regions had effects on carcass (chromosomes 7, 10, 16, 17 and 18) and meat quality traits (two regions on chromosome 1 and one region on chromosomes 8, 9 and 13). Thirteen of the 23 QTL regions had not been previously described. A haplotype block of 183 kb on chromosome 1 (six SNPs) was identified and displayed three distinct haplotypes with significant (0.0001 < P < 0.03) associations with all evaluated meat quality traits.ConclusionsGWAS analyses with the PorcineSNP60 BeadChip enabled the detection of 23 QTL regions that affect feed consumption, carcass and meat quality traits in a LW population, of which 13 were novel QTL. The proportionally larger number of QTL found for meat quality traits suggests a specific opportunity for improving these traits in the pig by genomic selection.

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Variance component analysis of quantitative trait loci for pork carcass composition and meat quality on SSC4 and SSC111

Cited by 13 publications

References 29 publications

Discovery of Candidate Genes for Muscle Traits Based on GWAS Supported by eQTL-analysis

Discovery of Candidate Genes for Muscle Traits Based on GWAS Supported by eQTL-analysis

Supervised independent component analysis as an alternative method for genomic selection in pigs

A genome-wide association study of production traits in a commercial population of Large White pigs: evidence of haplotypes affecting meat quality

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