Indirect predictions with a large number of genotyped animals using the algorithm for proven and young

Garcia, André; Masuda, Yutaka; Tsuruta, S.; Miller, Stephen; Misztal, I.; Lourenço, Daniela

doi:10.1093/jas/skaa154

Cited by 15 publications

(22 citation statements)

References 20 publications

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“…The G −1 for JER, HOL, JER_HOL, and MIX were obtained using APY. The IP from ssGBLUP have been shown to be stable when using different core animals as long as the size of the core is at least equal to the number of eigenvalues required to explain 98 to 99% of the genomic variation (Garcia et al, 2020), which was the case in this study.…”

Section: Methodsmentioning

confidence: 74%

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

Steyn

González-Peña

Rubio

et al. 2021

Journal of Dairy Science

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The objective of this study was to predict genomic breeding values for milk yield of crossbred dairy cattle under different scenarios using single-step genomic BLUP (ssGBLUP). The data set included 13,880,217 milk yield measurements on 6,830,415 cows. Genotypes of 89,558 Holstein, 40,769 Jersey, and 22,373 Holstein-Jersey crossbred animals were used, of which all Holstein, 9,313 Jersey, and 1,667 crossbred animals had phenotypic records. Genotypes were imputed to 45K SNP markers. The SNP effects were estimated from single-breed evaluations for Jersey (JE), Holstein (HO) and crossbreds (CROSS), and multibreed evaluations including all Jersey and Holstein (JE_HO) or approximately equal proportions of Jersey, Holstein, and crossbred animals (MIX). Indirect predictions (IP) of the validation animals (358 crossbred animals with phenotypes excluded from evaluations) were calculated using the resulting SNP effects. Additionally, breed proportions (BP) of crossbred animals were applied as a weight when IP were estimated based on each pure breed. The predictive ability of IP was calculated as the Pearson correlation between IP and phenotypes of the validation animals adjusted for fixed effects in the model. Regression of adjusted phenotypes on IP was used to assess the inflation of IP. The predictive ability of IP for CROSS, JE, HO, JE_HO, and MIX scenario was 0.50, 0.50, 0.47, 0.50, and 0.46, respectively. Using BP was the least successful, with a predictive ability of 0.32. The inflation of the IP for crossbred animals using CROSS, JE, HO, JE_HO, MIX, and BP scenarios were 1.17, 0.65, 0.55, 0.78, 1.00, and 0.85, respectively. The IP of crossbred animals can be predicted using singlestep GBLUP under a scenario that includes purebred genotypes.

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Section: Methodsmentioning

confidence: 74%

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

Steyn

González-Peña

Rubio

et al. 2021

Journal of Dairy Science

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“…Including all of these animals in the main routine evaluation may not be reasonable because of the computing cost. Garcia et al (2020) reported that the accuracy of IGP was as high as that of GEBV in American Angus data, where about 70% of the genotyped animals had phenotypes. In that study, they did not investigate which animals should be included in the computation of GEBV and IGP or how GEBV for those genotyped animals affect accuracy and bias in IGPs when those genotyped animals have neither phenotype nor progeny.…”

Section: Graphical Abstract Summarymentioning

confidence: 99%

Reducing computational cost of large-scale genomic evaluation by using indirect genomic prediction

et al. 2021

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“…The statistical model for pig traits was as in Steyn et al (2020) , for beef traits was as in Garcia et al (2020) , and for broiler chicken traits was as in Lourenco et al (2015a) . The (co)variance components used in all analyses were provided by PIC, Angus Genetics Inc., and Cobb-Vantress.…”

Section: Methodsmentioning

confidence: 99%

Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs, beef cattle, and broilers

Abdollahi-Arpanahi

Lourenço

Misztal

2021

Journal of Animal Science

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Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the effective starting point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. Three datasets were used for this purpose: a pig dataset with 117k genotypes and 1.3M animals in pedigree, an Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain and backfat. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little divergence for birth weight. In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for average daily gain than for backfat. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with a trivial trend for birth weight. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates onset of the genomic selection. The presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the three species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

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Indirect predictions with a large number of genotyped animals using the algorithm for proven and young

Cited by 15 publications

References 20 publications

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

Indirect genomic predictions for milk yield in crossbred Holstein-Jersey dairy cattle

Reducing computational cost of large-scale genomic evaluation by using indirect genomic prediction

Detecting effective starting point of genomic selection by divergent trends from best linear unbiased prediction and single-step genomic best linear unbiased prediction in pigs, beef cattle, and broilers

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