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Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions

Macedo, Fernando and Astruc, J. M. and Meuwissen, T. H. E. and Legarra, A. (2022). Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions. Journal of Dairy Science. 105 :3 , 2439-2452
[Research article]

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Bias in dairy genetic evaluations, when it exists, has to be understood and properly addressed. The origin of biases is not always clear. We analyzed 40 yr of records from the Lacaune dairy sheep breeding program to evaluate the extent of bias, assess possible corrections, and emit hypotheses on its origin. The data set included 7 traits (milk yield, fat and protein contents, somatic cell score, teat angle, udder cleft, and udder depth) with records from 600,000 to 5 million depending on the trait,-1,900,000 animals, and-5,900 genotyped elite artificial insemination rams. For the-8% animals with missing sire, we fit 25 unknown parent groups. We used the linear regression method to compare "partial" and "whole" predictions of young rams before and after progeny testing, with 7 cut-off points, and we obtained estimates of their bias, (over)dispersion, and accuracy in early proofs. We tried (1) several scenarios as follows: multiple or single trait, the "official" (routine) evalua-tion, which is a mixture of both single and multiple trait, and "deletion" of data before 1990; and (2) sev-eral models as follows: BLUP and single-step genomic (SSG)BLUP with fixed unknown parent groups or metafounders, where, for metafounders, their relation-ship matrix gamma was estimated using either a model for inbreeding trend, or base allele frequencies esti-mated by peeling. The estimate of gamma obtained by modeling the inbreeding trend resulted in an estimated increase of inbreeding, based on markers, faster than the pedigree-based one. The estimated genetic trends were similar for most models and scenarios across all traits, but were shrunken when gamma was estimated by peeling. This was due to shrinking of the estimates of metafounders in the latter case. Across scenarios, all traits showed bias, generally as an overestimate of genetic trend for milk yield and an underestimate for the other traits. As for the slope, it showed overdisper-sion of estimated breeding values for all traits. Using multiple-trait models slightly reduced the overestimate of genetic trend and the overdispersion, as did including genomic information (i.e., SSGBLUP) when the gam-ma matrix was estimated by the model for inbreeding trend. However, only deletion of historical data before 1990 resulted in elimination of both kind of biases. The SSGBLUP resulted in more accurate early proofs than BLUP for all traits. We considered that a snowball ef-fect of small errors in each genetic evaluation, combined with selection, may have resulted in biased evaluations. Improving statistical methods reduced some bias but not all, and a simple solution for this data set was to remove historical records.

Authors/Creators:Macedo, Fernando and Astruc, J. M. and Meuwissen, T. H. E. and Legarra, A.
Title:Removing data and using metafounders alleviates biases for all traits in Lacaune dairy sheep predictions
Series Name/Journal:Journal of Dairy Science
Year of publishing :2022
Page range:2439-2452
Number of Pages:14
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 402 Animal and Dairy Science > Animal and Dairy Science.
Keywords:genomic prediction, bias, accuracy, historical data, multiple trait
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Additional ID:
Type of IDID
Web of Science (WoS)000759207400026
ID Code:27436
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Animal Breeding and Genetics
Deposited By: SLUpub Connector
Deposited On:25 Mar 2022 10:25
Metadata Last Modified:25 Mar 2022 10:31

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