Removal of alleles by genome editing (RAGE) against deleterious load.
Journal
Genetics, selection, evolution : GSE
ISSN: 1297-9686
Titre abrégé: Genet Sel Evol
Pays: France
ID NLM: 9114088
Informations de publication
Date de publication:
17 Apr 2019
17 Apr 2019
Historique:
received:
31
05
2018
accepted:
01
04
2019
entrez:
19
4
2019
pubmed:
19
4
2019
medline:
21
5
2019
Statut:
epublish
Résumé
In this paper, we simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it. Deleterious variants can be identified by bioinformatics screening methods that use sequence conservation and biological prior information about protein function. However, once deleterious variants have been identified, how can they be used in breeding? We simulated a closed animal breeding population that is subject to both natural selection against deleterious load and artificial selection for a quantitative trait representing the breeding goal. Deleterious load was polygenic and was due to either codominant or recessive variants. We compared strategies for removal of deleterious alleles by genome editing (RAGE) to selection against carriers. When deleterious variants were codominant, the best strategy for prioritizing variants was to prioritize low-frequency variants. When deleterious variants were recessive, the best strategy was to prioritize variants with an intermediate frequency. Selection against carriers was inefficient when variants were codominant, but comparable to editing one variant per sire when variants were recessive. Genome editing of deleterious alleles reduces deleterious load, but requires the simultaneous editing of multiple deleterious variants in the same sire to be effective when deleterious variants are recessive. In the short term, selection against carriers is a possible alternative to genome editing when variants are recessive. Our results suggest that, in the future, there is the potential to use RAGE against deleterious load in animal breeding.
Sections du résumé
BACKGROUND
BACKGROUND
In this paper, we simulate deleterious load in an animal breeding program, and compare the efficiency of genome editing and selection for decreasing it. Deleterious variants can be identified by bioinformatics screening methods that use sequence conservation and biological prior information about protein function. However, once deleterious variants have been identified, how can they be used in breeding?
RESULTS
RESULTS
We simulated a closed animal breeding population that is subject to both natural selection against deleterious load and artificial selection for a quantitative trait representing the breeding goal. Deleterious load was polygenic and was due to either codominant or recessive variants. We compared strategies for removal of deleterious alleles by genome editing (RAGE) to selection against carriers. When deleterious variants were codominant, the best strategy for prioritizing variants was to prioritize low-frequency variants. When deleterious variants were recessive, the best strategy was to prioritize variants with an intermediate frequency. Selection against carriers was inefficient when variants were codominant, but comparable to editing one variant per sire when variants were recessive.
CONCLUSIONS
CONCLUSIONS
Genome editing of deleterious alleles reduces deleterious load, but requires the simultaneous editing of multiple deleterious variants in the same sire to be effective when deleterious variants are recessive. In the short term, selection against carriers is a possible alternative to genome editing when variants are recessive. Our results suggest that, in the future, there is the potential to use RAGE against deleterious load in animal breeding.
Identifiants
pubmed: 30995904
doi: 10.1186/s12711-019-0456-8
pii: 10.1186/s12711-019-0456-8
pmc: PMC6472060
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BBS/E/D/30002275
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/N015339/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/L020467/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/M009254/1
Pays : United Kingdom
Organisme : Svenska Forskningsrådet Formas
ID : Dnr 2016-01386
Références
Plant Genome. 2017 Jul;10(2):
pubmed: 28724082
Proc Natl Acad Sci U S A. 2015 Mar 24;112(12):3823-8
pubmed: 25775595
Nature. 2012 Nov 1;491(7422):56-65
pubmed: 23128226
PLoS Comput Biol. 2010 Dec 02;6(12):e1001025
pubmed: 21152010
Genet Res. 1997 Aug;70(1):27-34
pubmed: 9369096
PLoS One. 2013 Dec 20;8(12):e82909
pubmed: 24376603
Genet Sel Evol. 2019 Mar 5;51(1):9
pubmed: 30836944
Anim Genet. 2014 Oct;45(5):618-28
pubmed: 24975026
J Dairy Sci. 2016 Sep;99(9):7274-7288
pubmed: 27394947
Nucleic Acids Res. 2003 Jul 1;31(13):3812-4
pubmed: 12824425
Genet Sel Evol. 2015 Nov 30;47:94
pubmed: 26620491
Genet Sel Evol. 2003 Jul-Aug;35(4):353-68
pubmed: 12927071
Genome Res. 2009 Sep;19(9):1553-61
pubmed: 19602639
Cell. 2013 May 9;153(4):910-8
pubmed: 23643243
BMC Genomics. 2015 Dec 09;16:1043
pubmed: 26645365
Genetics. 1972 Oct;72(2):335-55
pubmed: 4630587
Genome Biol. 2016 Jun 06;17(1):122
pubmed: 27268795
Genome Res. 2005 Aug;15(8):1034-50
pubmed: 16024819
PLoS One. 2013;8(1):e54872
pubmed: 23349982
Nat Commun. 2015 Feb 18;6:6244
pubmed: 25692716
Mol Biol Evol. 2010 Jan;27(1):177-92
pubmed: 19759235
Science. 2017 Sep 22;357(6357):1303-1307
pubmed: 28798043
Nat Commun. 2020 Nov 20;11(1):5918
pubmed: 33219223
Genet Sel Evol. 2001 May-Jun;33(3):209-29
pubmed: 11403745
Trends Biotechnol. 2013 Jul;31(7):397-405
pubmed: 23664777
Genetics. 2011 Feb;187(2):553-66
pubmed: 21098719
Genetics. 2000 Sep;156(1):297-304
pubmed: 10978293
Genet Sel Evol. 2018 Apr 16;50(1):18
pubmed: 29661133
J Anim Breed Genet. 2007 Dec;124(6):323-30
pubmed: 18076469
Nat Genet. 2008 Apr;40(4):449-54
pubmed: 18344998
PLoS Genet. 2008 May 30;4(5):e1000083
pubmed: 18516229
Nat Rev Genet. 2009 Nov;10(11):783-96
pubmed: 19834483
Proc Natl Acad Sci U S A. 2013 Aug 20;110(34):13904-9
pubmed: 23918387
Genetics. 2007 Dec;177(4):2251-61
pubmed: 18073430
Nature. 2016 Aug 17;536(7616):285-91
pubmed: 27535533
BMC Genomics. 2017 Nov 09;18(1):858
pubmed: 29121877
Proc Natl Acad Sci U S A. 2003 Nov 11;100(23):13402-6
pubmed: 14597721
PLoS Genet. 2017 Sep 27;13(9):e1007019
pubmed: 28953891
Genetics. 2004 Aug;167(4):1529-36
pubmed: 15342495
Genet Sel Evol. 2015 Jul 02;47:55
pubmed: 26133579
Nature. 1999 Jan 28;397(6717):344-7
pubmed: 9950425
PLoS One. 2010 Nov 30;5(11):e15116
pubmed: 21152099
G3 (Bethesda). 2014 Jan 10;4(1):163-71
pubmed: 24281428
Fly (Austin). 2012 Apr-Jun;6(2):80-92
pubmed: 22728672
Biol Lett. 2006 Sep 22;2(3):426-30
pubmed: 17148422
Nat Rev Genet. 2005 Jun;6(6):507-12
pubmed: 15931173
Hum Mutat. 1993;2(3):229-34
pubmed: 8364591
Genet Res. 1966 Dec;8(3):269-94
pubmed: 5980116
Bioinformatics. 2015 Mar 1;31(5):761-3
pubmed: 25338716
Nat Methods. 2015 Oct;12(10):931-4
pubmed: 26301843
Nat Genet. 2014 Mar;46(3):310-5
pubmed: 24487276
Science. 2012 Feb 17;335(6070):823-8
pubmed: 22344438
Nat Methods. 2010 Apr;7(4):248-9
pubmed: 20354512
Genome Res. 2015 Jul;25(7):970-81
pubmed: 26063737
PLoS One. 2015 Mar 19;10(3):e0118867
pubmed: 25789620
Hum Mutat. 2016 Mar;37(3):235-41
pubmed: 26555599
Nat Genet. 2014 Aug;46(8):858-65
pubmed: 25017103
PLoS One. 2013 Jun 07;8(6):e65550
pubmed: 23762392
PLoS One. 2016 Apr 29;11(4):e0154602
pubmed: 27128314
Science. 2012 Jul 6;337(6090):64-9
pubmed: 22604720
Nat Genet. 2017 Jun;49(6):959-963
pubmed: 28416819
Cell Stem Cell. 2014 Aug 7;15(2):215-226
pubmed: 24931489
Genetics. 2006 Jun;173(2):891-900
pubmed: 16547091
Genome Res. 2009 Jan;19(1):136-42
pubmed: 19029539
J Dairy Sci. 2011 Dec;94(12):6153-61
pubmed: 22118103
Genet Sel Evol. 2003 Jan-Feb;35(1):103-18
pubmed: 12605853
Genome Res. 2016 Oct;26(10):1333-1341
pubmed: 27646536