Core-dependent changes in genomic predictions using the Algorithm for Proven and Young in single-step genomic best linear unbiased prediction.

Journal of Animal Science
I MisztalD A L Lourenco

Abstract

Single-step genomic best linear unbiased prediction with the Algorithm for Proven and Young (APY) is a popular method for large-scale genomic evaluations. With the APY algorithm, animals are designated as core or noncore, and the computing resources to create the inverse of the genomic relationship matrix (GRM) are reduced by inverting only a portion of that matrix for core animals. However, using different core sets of the same size causes fluctuations in genomic estimated breeding values (GEBVs) up to one additive standard deviation without affecting prediction accuracy. About 2% of the variation in the GRM is noise. In the recursion formula for APY, the error term modeling the noise is different for every set of core animals, creating changes in breeding values. While average changes are small, and correlations between breeding values estimated with different core animals are close to 1.0, based on the normal distribution theory, outliers can be several times bigger than the average. Tests included commercial datasets from beef and dairy cattle and from pigs. Beyond a certain number of core animals, the prediction accuracy did not improve, but fluctuations decreased with more animals. Fluctuations were much smaller than the ...Continue Reading

References

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Sep 21, 2019·Journal of Animal Science·Yvette SteynIgnacy Misztal

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Citations

Feb 6, 2021·Journal of Animal Science·Jorge HidalgoIgnacy Misztal
Mar 28, 2021·Journal of Animal Science·Ignacy MisztalMiguel Toro

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Software Mentioned

BLUPF90
BLUP
BLUP90IOD
APY
GEBV
GRM
GENUS
ssGTBLUP
ssGBLUP

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