Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping.
dairy breeding program
genomic selection
optimized investment
return on investment
small populations
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2021
2021
Historique:
received:
02
12
2020
accepted:
14
01
2021
entrez:
8
3
2021
pubmed:
9
3
2021
medline:
9
3
2021
Statut:
epublish
Résumé
This paper evaluates the potential of maximizing genetic gain in dairy cattle breeding by optimizing investment into phenotyping and genotyping. Conventional breeding focuses on phenotyping selection candidates or their close relatives to maximize selection accuracy for breeders and quality assurance for producers. Genomic selection decoupled phenotyping and selection and through this increased genetic gain per year compared to the conventional selection. Although genomic selection is established in well-resourced breeding programs, small populations and developing countries still struggle with the implementation. The main issues include the lack of training animals and lack of financial resources. To address this, we simulated a case-study of a small dairy population with a number of scenarios with equal available resources yet varied use of resources for phenotyping and genotyping. The conventional progeny testing scenario collected 11 phenotypic records per lactation. In genomic selection scenarios, we reduced phenotyping to between 10 and 1 phenotypic records per lactation and invested the saved resources into genotyping. We tested these scenarios at different relative prices of phenotyping to genotyping and with or without an initial training population for genomic selection. Reallocating a part of phenotyping resources for repeated milk records to genotyping increased genetic gain compared to the conventional selection scenario regardless of the amount and relative cost of phenotyping, and the availability of an initial training population. Genetic gain increased by increasing genotyping, despite reduced phenotyping. High-genotyping scenarios even saved resources. Genomic selection scenarios expectedly increased accuracy for young non-phenotyped candidate males and females, but also proven females. This study shows that breeding programs should optimize investment into phenotyping and genotyping to maximize return on investment. Our results suggest that any dairy breeding program using conventional progeny testing with repeated milk records can implement genomic selection without increasing the level of investment.
Identifiants
pubmed: 33679899
doi: 10.3389/fgene.2021.637017
pmc: PMC7928407
doi:
Banques de données
figshare
['10.6084/m9.figshare.c.5281625.v1']
Types de publication
Journal Article
Langues
eng
Pagination
637017Informations de copyright
Copyright © 2021 Obšteter, Jenko and Gorjanc.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Références
Genet Sel Evol. 2011 May 17;43:18
pubmed: 21575265
G3 (Bethesda). 2013 Mar;3(3):481-91
pubmed: 23450123
J Dairy Sci. 2014 Dec;97(12):7905-15
pubmed: 25453600
Genet Sel Evol. 2012 Aug 03;44:26
pubmed: 22862849
J Anim Sci. 2017 Aug;95(8):3391-3395
pubmed: 28805917
J Dairy Sci. 2014;97(6):3943-52
pubmed: 24679933
Front Genet. 2019 Jan 09;9:694
pubmed: 30687382
J Dairy Sci. 2009 Jan;92(1):382-91
pubmed: 19109296
Genetics. 2001 Apr;157(4):1819-29
pubmed: 11290733
J Anim Breed Genet. 2006 Aug;123(4):218-23
pubmed: 16882088
J Dairy Sci. 2017 Apr;100(4):2892-2904
pubmed: 28189326
J Dairy Sci. 2010 Nov;93(11):5455-66
pubmed: 20965361
J Dairy Sci. 1994 Apr;77(4):1114-25
pubmed: 8201046
Genet Sel Evol. 2012 Jul 31;44:25
pubmed: 22849718
J Dairy Sci. 1999 Jul;82(7):1555-64
pubmed: 10416171
J Dairy Sci. 2014;97(1):458-70
pubmed: 24239076
PLoS One. 2008;3(10):e3395
pubmed: 18852893
J Anim Sci. 2018 Nov 21;96(11):4490-4500
pubmed: 30165381
Front Genet. 2018 Jul 13;9:251
pubmed: 30057590
J Dairy Sci. 2011 Jan;94(1):493-500
pubmed: 21183061
Genet Sel Evol. 2012 Aug 16;44:27
pubmed: 22898324
Genet Sel Evol. 2011 Jun 21;43:23
pubmed: 21693035
Genet Sel Evol. 2014 Jun 24;46:40
pubmed: 24962065
Theor Appl Genet. 2013 Nov;126(11):2835-48
pubmed: 23982591
BMC Genomics. 2017 May 30;18(1):425
pubmed: 28558656
J Dairy Sci. 1989 Jul;72(7):1933-6
pubmed: 2778173
Proc Natl Acad Sci U S A. 2016 Jul 12;113(28):E3995-4004
pubmed: 27354521
J Dairy Sci. 2014 Sep;97(9):5822-32
pubmed: 24996280
Genet Sel Evol. 2020 Jul 29;52(1):42
pubmed: 32727349
Annu Rev Anim Biosci. 2017 Feb 8;5:309-327
pubmed: 27860491
J Dairy Sci. 2019 Nov;102(11):9971-9982
pubmed: 31477287
Genet Sel Evol. 2018 Feb 28;50(1):6
pubmed: 29490611
Front Genet. 2019 Apr 24;10:297
pubmed: 31105735
J Dairy Sci. 2017 Jan;100(1):439-452
pubmed: 27837974
J Dairy Sci. 1987 Apr;70(4):842-9
pubmed: 3584618
G3 (Bethesda). 2017 Oct 5;7(10):3543-3556
pubmed: 28860185
Sci Rep. 2019 Feb 5;9(1):1446
pubmed: 30723226
Animal. 2012 Jun;6(6):880-6
pubmed: 22558957
J Anim Breed Genet. 2011 Dec;128(6):409-21
pubmed: 22059574
Genetica. 2009 Jun;136(2):245-57
pubmed: 18704696
Nat Rev Genet. 2002 Jan;3(1):22-32
pubmed: 11823788
J Anim Breed Genet. 2007 Dec;124(6):369-76
pubmed: 18076474