Literature DB >> 35451025

Imputation of non-genotyped F1 dams to improve genetic gain in swine crossbreeding programs.

Garrett M See1, Justin S Fix2, Clint R Schwab2, Matthew L Spangler1.   

Abstract

This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (rg = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known.
© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  crossbreeding; genomic selection; imputation accuracy; swine

Mesh:

Year:  2022        PMID: 35451025      PMCID: PMC9126202          DOI: 10.1093/jas/skac148

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.338


  35 in total

1.  Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.

Authors:  M Erbe; B J Hayes; L K Matukumalli; S Goswami; P J Bowman; C M Reich; B A Mason; M E Goddard
Journal:  J Dairy Sci       Date:  2012-07       Impact factor: 4.034

2.  Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes.

Authors:  D P Berry; S McParland; J F Kearney; M Sargolzaei; M P Mullen
Journal:  Animal       Date:  2014-06       Impact factor: 3.240

3.  Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle.

Authors:  A P W de Roos; B J Hayes; R J Spelman; M E Goddard
Journal:  Genetics       Date:  2008-07-13       Impact factor: 4.562

4.  A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes.

Authors:  John M Hickey; Brian P Kinghorn; Bruce Tier; James F Wilson; Neil Dunstan; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2011-03-10       Impact factor: 4.297

5.  The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes.

Authors:  Samuel A Clark; John M Hickey; Hans D Daetwyler; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2012-02-09       Impact factor: 4.297

6.  Design of reference populations for genomic selection in crossbreeding programs.

Authors:  Ilse E M van Grevenhof; Julius H J van der Werf
Journal:  Genet Sel Evol       Date:  2015-03-07       Impact factor: 4.297

7.  Accuracy of genome-wide imputation in Braford and Hereford beef cattle.

Authors:  Mario L Piccoli; José Braccini; Fernando F Cardoso; Medhi Sargolzaei; Steven G Larmer; Flávio S Schenkel
Journal:  BMC Genet       Date:  2014-12-29       Impact factor: 2.797

8.  Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon.

Authors:  Hsin-Yuan Tsai; Oswald Matika; Stefan McKinnon Edwards; Roberto Antolín-Sánchez; Alastair Hamilton; Derrick R Guy; Alan E Tinch; Karim Gharbi; Michael J Stear; John B Taggart; James E Bron; John M Hickey; Ross D Houston
Journal:  G3 (Bethesda)       Date:  2017-04-03       Impact factor: 3.154

9.  Imputation of non-genotyped individuals based on genotyped relatives: assessing the imputation accuracy of a real case scenario in dairy cattle.

Authors:  Aniek C Bouwman; John M Hickey; Mario P L Calus; Roel F Veerkamp
Journal:  Genet Sel Evol       Date:  2014-02-03       Impact factor: 4.297

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