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Weighted single-step genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: a quantitative trait influenced by a major gene
- Source :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2018, 50 (1), pp.31. ⟨10.1186/s12711-018-0400-3⟩, Genetics Selection Evolution (50), Non Paginé. (2018), Genetics, Selection, Evolution : GSE, Genetics Selection Evolution, Vol 50, Iss 1, Pp 1-12 (2018)
- Publication Year :
- 2018
- Publisher :
- HAL CCSD, 2018.
-
Abstract
- In 2017, genomic selection was implemented in French dairy goats using the single-step genomic best linear unbiased prediction (ssGBLUP) method, which assumes that all single nucleotide polymorphisms explain the same fraction of genetic variance. However, ssGBLUP is not suitable for protein content, which is controlled by a major gene, i.e. α s 1 casein. This gene explains about 40% of the genetic variation in protein content. In this study, we evaluated the accuracy of genomic prediction using different genomic methods to include the effect of the α s 1 casein gene. Genomic evaluation for protein content was performed with data from the official genetic evaluation on 2955 animals genotyped with the Illumina goat SNP50 BeadChip, 7202 animals genotyped at the α s 1 casein gene and 6,767,490 phenotyped females. Pedigree-based BLUP was compared with regular unweighted ssGBLUP and with three weighted ssGBLUP methods (WssGBLUP, WssGBLUPMax and WssGBLUPSum), which give weights to SNPs according to their effect on protein content. Two other methods were also used: trait-specific marker-derived relationship matrix (TABLUP) using pre-selected SNPs associated with protein content and gene content based on a multiple-trait genomic model that includes α s 1 casein genotypes. We estimated accuracies of predicted genomic estimated breeding values (GEBV) in two populations of goats (Alpine and Saanen). Accuracies of GEBV with ssGBLUP improved by + 5 to + 7 percent points over accuracies from the pedigree-based BLUP model. With the WssGBLUP methods, SNPs that are located close to the α s 1 casein gene had the biggest weights and contributed substantially to the capture of signals from quantitative trait loci. Improvement in accuracy of genomic predictions using the three weighted ssGBLUP methods delivered up to + 6 percent points of accuracy over ssGBLUP. A similar accuracy was obtained for ssGBLUP and TABLUP considering the 20,000 most important SNPs. Incorporating information on the α s 1 casein genotypes based on the gene content method gave similar results as ssGBLUP. The three weighted ssGBLUP methods were efficient for detecting SNPs associated with protein content and for a better prediction of genomic breeding values than ssGBLUP. They also combined fast computing, simplicity and required ssGBLUP to be run only twice.
- Subjects :
- Male
0301 basic medicine
Genotyping Techniques
[SDV]Life Sciences [q-bio]
derivation
population
Breeding
Genome
information
relationship matrix
full pedigree
selection
prediction
cattle
sheep
regression
Polymorphism (computer science)
Genotype
lcsh:SF1-1100
2. Zero hunger
Goats
Caseins
04 agricultural and veterinary sciences
General Medicine
Milk Proteins
Major gene
Pedigree
Phenotype
Female
Algorithms
Research Article
lcsh:QH426-470
Quantitative Trait Loci
Single-nucleotide polymorphism
Computational biology
Biology
Best linear unbiased prediction
Quantitative trait locus
Polymorphism, Single Nucleotide
03 medical and health sciences
Genetic variation
Genetics
Animals
Ecology, Evolution, Behavior and Systematics
0402 animal and dairy science
040201 dairy & animal science
lcsh:Genetics
030104 developmental biology
Animal Science and Zoology
lcsh:Animal culture
Subjects
Details
- Language :
- English
- ISSN :
- 0999193X and 12979686
- Database :
- OpenAIRE
- Journal :
- Genetics Selection Evolution, Genetics Selection Evolution, BioMed Central, 2018, 50 (1), pp.31. ⟨10.1186/s12711-018-0400-3⟩, Genetics Selection Evolution (50), Non Paginé. (2018), Genetics, Selection, Evolution : GSE, Genetics Selection Evolution, Vol 50, Iss 1, Pp 1-12 (2018)
- Accession number :
- edsair.doi.dedup.....36d23987691fbfa258283bd31cc63e32