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Journal of Animal Science Abstract - Animal Genetics and Genomics

Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient1

 

This article in JAS

  1. Vol. 95 No. 1, p. 49-52
     
    Received: July 05, 2016
    Accepted: Aug 16, 2016
    Published: January 5, 2017


    2 Corresponding author(s): yutaka@uga.edu
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doi:10.2527/jas.2016.0699
  1. Y. Masuda 2*,
  2. I. Misztal*,
  3. A. Legarra,
  4. S. Tsuruta*,
  5. D. A. L. Lourenco*,
  6. B. O. Fragomeni* and
  7. I. Aguilar
  1. * Department of Animal and Dairy Science, University of Georgia, Athens 30602
     INRA, UR631 SAGA, BP 52627, 31326 Castanet-Tolosan Cedex, France
     Instituto Nacional de Investigación Agropecuaria, Canelones, Uruguay 90200

Abstract

This paper evaluates an efficient implementation to multiply the inverse of a numerator relationship matrix for genotyped animals () by a vector (q). The computation is required for solving mixed model equations in single-step genomic BLUP (ssGBLUP) with the preconditioned conjugate gradient (PCG). The inverse can be decomposed into sparse matrices that are blocks of the sparse inverse of a numerator relationship matrix (A−1) including genotyped animals and their ancestors. The elements of A−1 were rapidly calculated with the Henderson’s rule and stored as sparse matrices in memory. Implementation of was by a series of sparse matrix–vector multiplications. Diagonal elements of , which were required as preconditioners in PCG, were approximated with a Monte Carlo method using 1,000 samples. The efficient implementation of was compared with explicit inversion of A22 with 3 data sets including about 15,000, 81,000, and 570,000 genotyped animals selected from populations with 213,000, 8.2 million, and 10.7 million pedigree animals, respectively. The explicit inversion required 1.8 GB, 49 GB, and 2,415 GB (estimated) of memory, respectively, and 42 s, 56 min, and 13.5 d (estimated), respectively, for the computations. The efficient implementation required <1 MB, 2.9 GB, and 2.3 GB of memory, respectively, and <1 sec, 3 min, and 5 min, respectively, for setting up. Only <1 sec was required for the multiplication in each PCG iteration for any data sets. When the equations in ssGBLUP are solved with the PCG algorithm, is no longer a limiting factor in the computations.

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