Literature DB >> 32418688

Metafounder approach for single-step genomic evaluations of Red Dairy cattle.

A A Kudinov1, E A Mäntysaari2, G P Aamand3, P Uimari4, I Strandén2.   

Abstract

Single-step genomic BLUP (ssGBLUP) is a powerful approach for breeding value prediction in populations with a limited number of genotyped animals. However, conflicting genomic (G) and pedigree (A22) relationship matrices complicate the implementation of ssGBLUP into practice. The metafounder (MF) approach is a recently proposed solution for this problem and has been successfully used on simulated and real multi-breed pig data. Advantages of the method are easily seen across breed evaluations, where pedigrees are traced to several pure breeds, which are thereafter used as MF. Application of the MF method to ruminants is complicated due to multi-breed pedigree structures and the inability to transmit existing unknown parent groups (UPG) to MF. In this study, we apply the MF approach for ssGBLUP evaluation of Finnish Red Dairy cattle treated as a single breed. Relationships among MF were accounted for by a (co)variance matrix (Γ) computed using estimated base population allele frequencies. The attained Γ was used to calculate a relationship matrix A22Γ for the genotyped animals. We tested the influence of SNP selection on the Γ matrix by applying a minor allele frequency (MAF) threshold (ΓMAF) where accepted markers had an MAF ≥0.05. Elements in the ΓMAF matrix were slightly lower than in the Γ matrix. Correlation between diagonal elements of the genomic and pedigree relationship matrices increased from 0.53 (A22) to 0.76 ( A22Γ and [Formula: see text] ). Average diagonal elements of A22Γ and [Formula: see text] matrices increased to the same level as in the G matrix. The ssGBLUP breeding values (GEBV) were solved using either the original 236 or redefined 8 UPG, or 8 MF computed with or without the MAF threshold. For bulls, the GEBV validation test results for the 8 UPG and 8 MF gave the same validation reliability (R2; 0.31) and over-dispersion (0.73, measured by regression coefficient b1). No significant R2 increase was observed in cows. Thus, the MF greatly influenced the pedigree relationship matrices but not the GEBV. Selection of SNP according to MAF had a notable effect on the Γ matrix and made the A22 and G matrices more similar.
Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  base population; genetic group; metafounder; single-step genomic BLUP

Year:  2020        PMID: 32418688     DOI: 10.3168/jds.2019-17483

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  5 in total

1.  Single-step genomic BLUP with genetic groups and automatic adjustment for allele coding.

Authors:  Ismo Strandén; Gert P Aamand; Esa A Mäntysaari
Journal:  Genet Sel Evol       Date:  2022-06-02       Impact factor: 5.100

2.  Comparison of models for missing pedigree in single-step genomic prediction.

Authors:  Yutaka Masuda; Shogo Tsuruta; Matias Bermann; Heather L Bradford; Ignacy Misztal
Journal:  J Anim Sci       Date:  2021-02-01       Impact factor: 3.159

3.  Single-step genomic evaluation of Russian dairy cattle using internal and external information.

Authors:  Andrei A Kudinov; Esa A Mäntysaari; Timo J Pitkänen; Ekaterina I Saksa; Gert P Aamand; Pekka Uimari; Ismo Strandén
Journal:  J Anim Breed Genet       Date:  2021-11-28       Impact factor: 3.271

4.  Accounting for population structure in genomic predictions of Eucalyptus globulus.

Authors:  Andrew N Callister; Matias Bermann; Stephen Elms; Ben P Bradshaw; Daniela Lourenco; Jeremy T Brawner
Journal:  G3 (Bethesda)       Date:  2022-08-25       Impact factor: 3.542

5.  Correcting for base-population differences and unknown parent groups in single-step genomic predictions of Norwegian Red cattle.

Authors:  Tesfaye K Belay; Leiv S Eikje; Arne B Gjuvsland; Øyvind Nordbø; Thierry Tribout; Theo Meuwissen
Journal:  J Anim Sci       Date:  2022-09-01       Impact factor: 3.338

  5 in total

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