Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd

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dc.citation.issue1
dc.citation.volume11
dc.contributor.authorScholtens M
dc.contributor.authorLopez-Villalobos N
dc.contributor.authorLehnert K
dc.contributor.authorSnell R
dc.contributor.authorGarrick D
dc.contributor.authorBlair HT
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-01-04T01:47:16Z
dc.date.accessioned2024-07-25T06:38:23Z
dc.date.available2020-12-25
dc.date.available2024-01-04T01:47:16Z
dc.date.available2024-07-25T06:38:23Z
dc.date.issued2021-01
dc.description.abstractSelection on genomic breeding values (GBVs) is now readily available for ranking candidates in improvement schemes. Our objective was to quantify benefits in terms of accuracy of prediction from including genomic information in the single-trait estimation of breeding values (BVs) for a New Zealand mixed breed dairy goat herd. The dataset comprised phenotypic and pedigree records of 839 does. The phenotypes comprised estimates of 305-day lactation yields of milk, fat, and protein and average somatic cell score from the 2016 production season. Only 388 of the goats were genotyped with a Caprine 50K SNP chip and 41,981 of the single nucleotide polymorphisms (SNPs) passed quality control. Pedigree-based best linear unbiased prediction (PBLUP) was used to obtain across-breed breeding values (EBVs), whereas a single-step BayesC model (ssBC) was used to estimate across-breed GBVs. The average prediction accuracies ranged from 0.20 to 0.22 for EBVs and 0.34 to 0.43 for GBVs. Accuracies of GBVs were up to 103% greater than EBVs. Breed effects were more reliably estimated in the ssBC model compared with the PBLUP model. The greatest benefit of genomic prediction was for individuals with no pedigree or phenotypic records. Including genomic information improved the prediction accuracy of BVs compared with the current pedigree-based BLUP method currently implemented in the New Zealand dairy goat population.
dc.format.pagination1-13
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/33375575
dc.identifier.citationScholtens M, Lopez-Villalobos N, Lehnert K, Snell R, Garrick D, Blair HT. (2020). Advantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd.. Animals (Basel). 11. 1. (pp. 1-13).
dc.identifier.doi10.3390/ani11010024
dc.identifier.eissn2076-2615
dc.identifier.elements-typejournal-article
dc.identifier.issn2076-2615
dc.identifier.numberARTN 24
dc.identifier.piiani11010024
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70595
dc.languageeng
dc.publisherMDPI (Basel, Switzerland)
dc.relation.isPartOfAnimals (Basel)
dc.rights(c) 2020 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBayesC
dc.subjectaccuracy
dc.subjectgenetic evaluation
dc.subjectgenomic prediction
dc.subjectgoat
dc.titleAdvantage of including Genomic Information to Predict Breeding Values for Lactation Yields of Milk, Fat, and Protein or Somatic Cell Score in a New Zealand Dairy Goat Herd
dc.typeJournal article
pubs.elements-id436703
pubs.organisational-groupOther
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