Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects

被引:30
|
作者
Taskinen, Matti [1 ]
Mantysaari, Esa A. [1 ]
Stranden, Ismo [1 ]
机构
[1] Nat Resources Inst Finland Luke, Myllytie 1, Jokioinen, Finland
关键词
GENOMIC RELATIONSHIP MATRIX; FULL PEDIGREE; US HOLSTEINS; PREDICTIONS; ANIMALS; POPULATIONS; INFORMATION; SELECTION; COMPUTE; INVERSE;
D O I
10.1186/s12711-017-0310-9
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
Background: Single-step genomic best linear unbiased prediction (BLUP) evaluation combines relationship information from pedigree and genomic marker data. The inclusion of the genomic information into mixed model equations requires the inverse of the combined relationship matrix H, which has a dense matrix block for genotyped animals. Methods: To avoid inversion of dense matrices, single-step genomic BLUP can be transformed to single-step single nucleotide polymorphism BLUP (SNP-BLUP) which have observed and imputed marker coefficients. Simple block LDL type decompositions of the single-step relationship matrix H were derived to obtain different types of linearly equivalent single-step genomic mixed model equations with different sets of reparametrized random effects. For non-genotyped animals, the imputed marker coefficient terms in the single-step SNP-BLUP were calculated on-the-fly during the iterative solution using sparse matrix decompositions without storing the imputed genotypes. Residual polygenic effects were added to genotyped animals and transmitted to non-genotyped animals using relationship coefficients that are similar to imputed genotypes. The relationships were further orthogonalized to improve convergence of iterative methods. Results: All presented single-step SNP-BLUP models can be solved efficiently using iterative methods that rely on iteration on data and sparse matrix approaches. The efficiency, accuracy and iteration convergence of the derived mixed model equations were tested with a small dataset that included 73,579 animals of which 2885 were genotyped with 37,526 SNPs. Conclusions: Inversion of the large and dense genomic relationship matrix was avoided in single-step evaluation by using fully orthogonalized single-step SNP-BLUP formulations. The number of iterations until convergence was smaller in single-step SNP-BLUP formulations than in the original single-step GBLUP when heritability was low, but increased above that of the original single-step when heritability was high.
引用
收藏
页数:15
相关论文
共 16 条
  • [1] Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects
    Matti Taskinen
    Esa A. Mäntysaari
    Ismo Strandén
    [J]. Genetics Selection Evolution, 49
  • [2] Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups
    Slomian, Dawid
    Zukowski, Kacper
    Szyda, Joanna
    [J]. GENETICS SELECTION EVOLUTION, 2023, 55 (01)
  • [3] Heterogeneity in convergence behaviour of the single-step SNP-BLUP model across different effects and animal groups
    Dawid Słomian
    Kacper Żukowski
    Joanna Szyda
    [J]. Genetics Selection Evolution, 55
  • [4] Using Monte Carlo method to include polygenic effects in calculation of SNP-BLUP model reliability
    Ben Zaabza, H.
    Mantysaari, E. A.
    Stranden, I
    [J]. JOURNAL OF DAIRY SCIENCE, 2020, 103 (06) : 5170 - 5182
  • [5] Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants
    Santana, Bruna Folegatti
    Riser, Molly
    Hay, El Hamidi A.
    Fragomeni, Breno de Oliveira
    [J]. JOURNAL OF ANIMAL BREEDING AND GENETICS, 2023, 140 (06) : 679 - 694
  • [6] Alternative SNP weighting for multi-step and single-step genomic BLUP in the presence of causative variants
    Santana, Bruna
    Riser, Molly
    Fragomeni, Breno O.
    [J]. JOURNAL OF ANIMAL SCIENCE, 2021, 99 : 228 - 228
  • [7] Different methods to calculate genomic predictions-Comparisons of BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP)
    Koivula, M.
    Stranden, I.
    Su, G.
    Mantysaari, E. A.
    [J]. JOURNAL OF DAIRY SCIENCE, 2012, 95 (07) : 4065 - 4073
  • [8] Genomic prediction with single-step genomic BLUP using a subset of genotypes in US Holstein.
    Masuda, Y.
    Tsuruta, S.
    Misztal, I.
    [J]. JOURNAL OF DAIRY SCIENCE, 2020, 103 : 141 - 141
  • [9] Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats
    Arnal, M.
    Robert-Granie, C.
    Ducrocq, V.
    Larroque, H.
    [J]. JOURNAL OF DAIRY SCIENCE, 2023, 106 (07) : 4813 - 4824
  • [10] Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes
    Fragomeni, B. O.
    Lourenco, D. A. L.
    Tsuruta, S.
    Masuda, Y.
    Aguilar, I.
    Legarra, A.
    Lawlor, T. J.
    Misztal, I.
    [J]. JOURNAL OF DAIRY SCIENCE, 2015, 98 (06) : 4090 - 4094