Single-step SNP-BLUP with on-the-fly imputed genotypes and residual polygenic effects
| Autor: | Esa Mäntysaari, Matti Taskinen, Ismo Strandén |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Mixed model Multifactorial Inheritance Genotype lcsh:QH426-470 Iterative method [SDV]Life Sciences [q-bio] Best linear unbiased prediction Type (model theory) Biology Residual Polymorphism Single Nucleotide 03 medical and health sciences Matrix (mathematics) Well Linear Unbiased Prediction Genetics Animals Applied mathematics Genetics(clinical) Relationship Matrix Impute Genotype Ecology Evolution Behavior and Systematics lcsh:SF1-1100 Sparse matrix 0402 animal and dairy science 04 agricultural and veterinary sciences General Medicine Random effects model Quantitative Biology::Genomics 040201 dairy & animal science lcsh:Genetics 030104 developmental biology Sparse Matrice Animal Science and Zoology lcsh:Animal culture Algorithms Research Article Genome-Wide Association Study Genomic Well Linear Unbiased Prediction |
| Zdroj: | Genetics Selection Evolution Genetics Selection Evolution, BioMed Central, 2017, 49 (1), pp.36. ⟨10.1186/s12711-017-0310-9⟩ Genetics Selection Evolution, Vol 49, Iss 1, Pp 1-15 (2017) Genetics, Selection, Evolution : GSE |
| ISSN: | 0999-193X 1297-9686 |
| DOI: | 10.1186/s12711-017-0310-9⟩ |
| Popis: | International audience; AbstractBackgroundSingle-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\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\mathbf{H}}$$\end{document}, which has a dense matrix block for genotyped animals.MethodsTo 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\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${\mathbf{H}}$$\end{document} 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.ResultsAll 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.ConclusionsInversion 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. |
| Databáze: | OpenAIRE |
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