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Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less
Vinícius Silva Junqueira1,2, Daniela Lourenco3, Yutaka Masuda3
1Breeding Research Department, Bayer Crop Science, Uberlândia, Minas Gerais, Brazil.
Journal of Animal Science
|March 15, 2022
Summary
The Algorithm for Proven and Young (APY) can reduce computational costs in single-step genomic best linear unbiased prediction (ssGLUE) by creating a sparse genomic relationship matrix. Optimal core animal selection is crucial for accurate variance component estimation.
Area of Science:
- Animal Breeding and Genetics
- Quantitative Genetics
- Bioinformatics
Background:
- Genomic relationship matrices (G) in genomic REML (GREML) and single-step GREML (ssGREML) are computationally intensive due to their dense nature.
- The Algorithm for Proven and Young (APY) offers a solution by creating a sparse inverse of G (G~APY~-1), reducing computational demands.
Purpose of the Study:
- To evaluate the efficiency of APY in reducing ssGREML computational costs using truncated pedigrees and phenotypes.
- To assess the impact of core animal selection within APY on variance component estimation accuracy.
Main Methods:
- Simulations involving 150K animals across 10 generations with phenotypes and genotypes.
- Comparison of Average Information REML and ssGREML using G~-1~ and G~APY~-1~ with varying core animal sizes (1K, 5K, 9K, 14K).
- Analysis of computational time for matrix inversion, numerical factorization, and ordering.
Main Results:
- APY successfully generated the inverse of the genomic relationship matrix for ssGREML.
- Variance component estimation was sensitive to the core group size in APY, with optimal performance linked to eigenvalues explaining ~98% of G's variation.
- Truncating pedigrees with APY reduced computational time for ordering and symbolic factorization without affecting estimates.
Conclusions:
- APY is a viable method for reducing computational burden in ssGREML.
- Careful selection of the core animal group size in APY is essential for reliable variance component estimation.
- Pedigree truncation in conjunction with APY offers computational savings in ssGREML without compromising estimation accuracy.
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