Matches in SemOpenAlex for { <https://semopenalex.org/work/W2103589178> ?p ?o ?g. }
- W2103589178 endingPage "889" @default.
- W2103589178 startingPage "876" @default.
- W2103589178 abstract "Genomic selection using 50,000 single nucleotide polymorphism (50k SNP) chips has been implemented in many dairy cattle breeding programs. Cheap, low-density chips make genotyping of a larger number of animals cost effective. A commonly proposed strategy is to impute low-density genotypes up to 50,000 genotypes before predicting direct genomic values (DGV). The objectives of this study were to investigate the accuracy of imputation for animals genotyped with a low-density chip and to investigate the effect of imputation on reliability of DGV. Low-density chips contained 384, 3,000, or 6,000 SNP. The SNP were selected based either on the highest minor allele frequency in a bin or the middle SNP in a bin, and DAGPHASE, CHROMIBD, and multivariate BLUP were used for imputation. Genotypes of 9,378 animals were used, from which approximately 2,350 animals had deregressed proofs. Bayesian stochastic search variable selection was used for estimating SNP effects of the 50k chip. Imputation accuracies and imputation error rates were poor for low-density chips with 384 SNP. Imputation accuracies were higher with 3,000 and 6,000 SNP. Performance of DAGPHASE and CHROMIBD was very similar and much better than that of multivariate BLUP for both imputation accuracy and reliability of DGV. With 3,000 SNP and using CHROMIBD or DAGPHASE for imputation, 84 to 90% of the increase in DGV reliability using the 50k chip, compared with a pedigree index, was obtained. With multivariate BLUP, the increase in reliability was only 40%. With 384 SNP, the reliability of DGV was lower than for a pedigree index, whereas with 6,000 SNP, about 93% of the increase in reliability of DGV based on the 50k chip was obtained when using DAGPHASE for imputation. Using genotype probabilities to predict gene content increased imputation accuracy and the reliability of DGV and is therefore recommended for applications of imputation for genomic prediction. A deterministic equation was derived to predict accuracy of DGV based on imputation accuracy, which fitted closely with the observed relationship. The deterministic equation can be used to evaluate the effect of differences in imputation accuracy on accuracy and reliability of DGV." @default.
- W2103589178 created "2016-06-24" @default.
- W2103589178 creator A5011195159 @default.
- W2103589178 creator A5036106191 @default.
- W2103589178 creator A5063527269 @default.
- W2103589178 creator A5067330053 @default.
- W2103589178 date "2012-02-01" @default.
- W2103589178 modified "2023-10-15" @default.
- W2103589178 title "Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle" @default.
- W2103589178 cites W1928998639 @default.
- W2103589178 cites W1971115224 @default.
- W2103589178 cites W1982799467 @default.
- W2103589178 cites W1992436001 @default.
- W2103589178 cites W2003500802 @default.
- W2103589178 cites W2006712471 @default.
- W2103589178 cites W2007069447 @default.
- W2103589178 cites W2008014772 @default.
- W2103589178 cites W2023673366 @default.
- W2103589178 cites W2036326740 @default.
- W2103589178 cites W2043107286 @default.
- W2103589178 cites W2049175056 @default.
- W2103589178 cites W2052395490 @default.
- W2103589178 cites W2056155495 @default.
- W2103589178 cites W2059542829 @default.
- W2103589178 cites W2074637391 @default.
- W2103589178 cites W2076631519 @default.
- W2103589178 cites W2077668635 @default.
- W2103589178 cites W2087036932 @default.
- W2103589178 cites W2089446473 @default.
- W2103589178 cites W2110787179 @default.
- W2103589178 cites W2111940561 @default.
- W2103589178 cites W2115837368 @default.
- W2103589178 cites W2117711225 @default.
- W2103589178 cites W2119372134 @default.
- W2103589178 cites W2120216880 @default.
- W2103589178 cites W2129758928 @default.
- W2103589178 cites W2138345444 @default.
- W2103589178 cites W2157164297 @default.
- W2103589178 cites W2157424635 @default.
- W2103589178 cites W2161040839 @default.
- W2103589178 cites W2162348269 @default.
- W2103589178 cites W2163555145 @default.
- W2103589178 cites W2163566091 @default.
- W2103589178 cites W2171214867 @default.
- W2103589178 cites W4240204556 @default.
- W2103589178 doi "https://doi.org/10.3168/jds.2011-4490" @default.
- W2103589178 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22281352" @default.
- W2103589178 hasPublicationYear "2012" @default.
- W2103589178 type Work @default.
- W2103589178 sameAs 2103589178 @default.
- W2103589178 citedByCount "90" @default.
- W2103589178 countsByYear W21035891782012 @default.
- W2103589178 countsByYear W21035891782013 @default.
- W2103589178 countsByYear W21035891782014 @default.
- W2103589178 countsByYear W21035891782015 @default.
- W2103589178 countsByYear W21035891782016 @default.
- W2103589178 countsByYear W21035891782017 @default.
- W2103589178 countsByYear W21035891782018 @default.
- W2103589178 countsByYear W21035891782019 @default.
- W2103589178 countsByYear W21035891782020 @default.
- W2103589178 countsByYear W21035891782021 @default.
- W2103589178 countsByYear W21035891782022 @default.
- W2103589178 countsByYear W21035891782023 @default.
- W2103589178 crossrefType "journal-article" @default.
- W2103589178 hasAuthorship W2103589178A5011195159 @default.
- W2103589178 hasAuthorship W2103589178A5036106191 @default.
- W2103589178 hasAuthorship W2103589178A5063527269 @default.
- W2103589178 hasAuthorship W2103589178A5067330053 @default.
- W2103589178 hasBestOaLocation W21035891781 @default.
- W2103589178 hasConcept C103545067 @default.
- W2103589178 hasConcept C104317684 @default.
- W2103589178 hasConcept C105795698 @default.
- W2103589178 hasConcept C135763542 @default.
- W2103589178 hasConcept C139275648 @default.
- W2103589178 hasConcept C153209595 @default.
- W2103589178 hasConcept C154945302 @default.
- W2103589178 hasConcept C161584116 @default.
- W2103589178 hasConcept C163691529 @default.
- W2103589178 hasConcept C2992444039 @default.
- W2103589178 hasConcept C31467283 @default.
- W2103589178 hasConcept C33923547 @default.
- W2103589178 hasConcept C41008148 @default.
- W2103589178 hasConcept C54355233 @default.
- W2103589178 hasConcept C58041806 @default.
- W2103589178 hasConcept C81917197 @default.
- W2103589178 hasConcept C86803240 @default.
- W2103589178 hasConcept C9357733 @default.
- W2103589178 hasConceptScore W2103589178C103545067 @default.
- W2103589178 hasConceptScore W2103589178C104317684 @default.
- W2103589178 hasConceptScore W2103589178C105795698 @default.
- W2103589178 hasConceptScore W2103589178C135763542 @default.
- W2103589178 hasConceptScore W2103589178C139275648 @default.
- W2103589178 hasConceptScore W2103589178C153209595 @default.
- W2103589178 hasConceptScore W2103589178C154945302 @default.
- W2103589178 hasConceptScore W2103589178C161584116 @default.
- W2103589178 hasConceptScore W2103589178C163691529 @default.
- W2103589178 hasConceptScore W2103589178C2992444039 @default.
- W2103589178 hasConceptScore W2103589178C31467283 @default.