03270naa a2200289 a 450000100080000000500110000800800410001902200140006002400310007410000210010524501720012626000090029850000810030752023010038865000340268965000250272365000280274865000250277670000160280170000160281770000160283370000130284970000120286270000150287470000170288977300740290610500612019-10-23       2014    bl uuuu     u00u1 u    #d  a0022-03027 a10.3168/jds.2013-69162DOI1 aLOURENCO, D.A.L.  aMethods for genomic evaluation of a relatively small genotyped dairy population and effect of genotyped cow information in multiparity analyses.h[electronic resource]  c2014  aArticle history:  Received September 10, 2013. /  Accepted December 6, 2013.  aABSTRACTS.  Methods for genomic prediction were evaluated for an Israeli Holstein dairy population of 713,686 cows and 1,305 progeny-tested bulls with genotypes. Inclusion of genotypes of 343 elite cows in an evaluation method that considers pedigree, phenotypes, and genotypes simultaneously was also evaluated. Two data sets were available: a complete data set with production records from 1985 through 2011, and a reduced data set with records after 2006 deleted. For each production trait, a multitrait animal model was used to compute traditional genetic evaluations for parities 1 through 3 as separate traits. Evaluations were calculated for the reduced and complete data sets. The evaluations from the reduced data set were used to calculate parent average for validation bulls, which was the benchmark for comparing gain in predictive ability from genomics. Genomic predictions for bulls in 2006 were calculated using a Bayesian regression method (BayesC), genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Predictions using BayesC and GBLUP were calculated either with or without an index that included parent average. Genomic predictions that included elite cow genotypes were calculated using ssGBLUP and WssGBLUP. Predictive ability was assessed by coefficients of determination (R2) and regressions of predictions of 135 validation bulls with no daughters in 2006 on deregressed evaluations of those bulls in 2011. A reduction in R2 and regression coefficients was observed from parities 1 through 3. Fat and protein yields had the lowest R2 for all the methods. On average, R2 was lowest for parent averages, followed by GBLUP, BayesC, ssGBLUP, and WssGBLUP. For some traits, R2 for direct genomic values from BayesC and GBLUP were lower than those for parent averages. Genomic estimated breeding values using ssGBLUP were the least biased, and this method appears to be a suitable tool for genomic evaluation of a small genotyped population, as it automatically accounts for parental index, allows for inclusion of female genomic information without preadjustments in evaluations, and uses the same model as in traditional evaluations. Weighted ssGBLUP has the potential for higher evaluation accuracy. © 2014 American Dairy Science Association.  aMEJORAMIENTO GENÉTICO ANIMAL  aMODELOS MATEMÁTICOS  aSELECCIÓN DE GENOTIPOS  aSELECCIÓN GENÓMICA1 aMISZTAL, I.1 aTSURUTA, S.1 aAGUILAR, I.1 aEZRA, E.1 aRON, M.1 aSHIRAK, A.1 aWELLER, J.I.  tJournal of Dairy Science, 2014gv.97, no.3, p.1742-1752. OPEN ACCESS.