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Mapping genomic regions affecting milk traits in Sarda sheep by using the OvineSNP50 Beadchip and principal components to perform combined linkage and linkage disequilibrium analysis
- Source :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.65. ⟨10.1186/s12711-019-0508-0⟩, Genetics, Selection, Evolution : GSE, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-19 (2019)
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- BackgroundThe detection of regions that affect quantitative traits (QTL), to implement selection assisted by molecular information, remains of particular interest in dairy sheep for which genetic gain is constrained by the high costs of large-scale phenotype and pedigree recording. QTL detection based on the combination of linkage disequilibrium and linkage analysis (LDLA) is the most suitable approach in family-structured populations. The main issue in performing LDLA mapping is the handling of the identity-by-descent (IBD) probability matrix. Here, we propose the use of principal component analysis (PCA) to perform LDLA mapping for milk traits in Sarda dairy sheep.MethodsA resource population of 3731 ewes belonging to 161 sire families and genotyped with the OvineSNP50 Beadchip was used to map genomic regions that affect five milk traits. The paternally and maternally inherited gametes of genotyped individuals were reconstructed and IBD probabilities between them were defined both at each SNP position and at the genome level. A QTL detection model fitting fixed effects of principal components that summarize IBD probabilities was tested at each SNP position. Genome-wide (GW) significance thresholds were determined by within-trait permutations.ResultsPCA resulted in substantial dimensionality reduction, in fact 137 and 32 (on average) principal components were able to capture 99% of the IBD variation at the locus and genome levels, respectively. Overall, 2563 positions exceeded the 0.05 GW significance threshold for at least one trait, which clustered into 75 QTL regions most of which affected more than one trait. The strongest signal was obtained for protein content onOvis aries(OAR) chromosome 6 and overlapped with the region that harbours the casein gene cluster. Additional interesting positions were identified on OAR4 for fat content and on OAR11 for the three yield traits.ConclusionsPCA is a good strategy to summarize IBD probabilities. A large number of regions associated to milk traits were identified. The outputs provided by the proposed method are useful for the selection of candidate genes, which need to be further investigated to identify causative mutations or markers in strong LD with them for application in selection programs assisted by molecular information.
- Subjects :
- Linkage disequilibrium
Candidate gene
lcsh:QH426-470
[SDV]Life Sciences [q-bio]
Population
Quantitative Trait Loci
Locus (genetics)
Quantitative trait locus
Biology
Breeding
Linkage Disequilibrium
03 medical and health sciences
Quantitative Trait, Heritable
Genetic linkage
Genetics
Animals
education
Ecology, Evolution, Behavior and Systematics
lcsh:SF1-1100
030304 developmental biology
2. Zero hunger
Linkage (software)
0303 health sciences
education.field_of_study
Principal Component Analysis
Sheep
Models, Genetic
0402 animal and dairy science
04 agricultural and veterinary sciences
General Medicine
040201 dairy & animal science
Pedigree
lcsh:Genetics
Milk
Principal component analysis
Animal Science and Zoology
lcsh:Animal culture
Genome-Wide Association Study
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 0999193X and 12979686
- Database :
- OpenAIRE
- Journal :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2019, 51 (1), pp.65. ⟨10.1186/s12711-019-0508-0⟩, Genetics, Selection, Evolution : GSE, Genetics Selection Evolution, Vol 51, Iss 1, Pp 1-19 (2019)
- Accession number :
- edsair.doi.dedup.....4f6f713bc7d57fd4e297ffe5c86f1c17