Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds.
Journal
Genetics, selection, evolution : GSE
ISSN: 1297-9686
Titre abrégé: Genet Sel Evol
Pays: France
ID NLM: 9114088
Informations de publication
Date de publication:
07 Jul 2020
07 Jul 2020
Historique:
received:
01
12
2019
accepted:
26
06
2020
entrez:
9
7
2020
pubmed:
9
7
2020
medline:
3
2
2021
Statut:
epublish
Résumé
Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10 Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.
Sections du résumé
BACKGROUND
BACKGROUND
Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision.
RESULTS
RESULTS
To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10
CONCLUSIONS
CONCLUSIONS
Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.
Identifiants
pubmed: 32635893
doi: 10.1186/s12711-020-00556-4
pii: 10.1186/s12711-020-00556-4
pmc: PMC7339598
doi:
Substances chimiques
Lipids
0
Milk Proteins
0
Types de publication
Journal Article
Meta-Analysis
Langues
eng
Sous-ensembles de citation
IM
Pagination
37Références
Front Genet. 2013 Nov 07;4:216
pubmed: 24223579
Nat Protoc. 2009;4(1):44-57
pubmed: 19131956
Genetics. 2003 Jan;163(1):253-66
pubmed: 12586713
Proc Natl Acad Sci U S A. 2004 Feb 24;101(8):2398-403
pubmed: 14983021
Genetics. 2008 Jul;179(3):1503-12
pubmed: 18622038
BMC Genomics. 2014 Jun 17;15:478
pubmed: 24935670
Genet Sel Evol. 2016 Feb 16;48:14
pubmed: 26883850
Nat Genet. 2018 Mar;50(3):362-367
pubmed: 29459679
Proc Natl Acad Sci U S A. 2019 Sep 24;116(39):19398-19408
pubmed: 31501319
Genet Sel Evol. 2016 Nov 4;48(1):83
pubmed: 27809758
Nat Rev Genet. 2013 Jun;14(6):379-89
pubmed: 23657481
Genome Biol. 2016 Jun 06;17(1):122
pubmed: 27268795
Nat Genet. 2016 Oct;48(10):1284-1287
pubmed: 27571263
Genet Sel Evol. 2017 Sep 18;49(1):68
pubmed: 28923017
Nat Genet. 2012 Mar 18;44(4):369-75, S1-3
pubmed: 22426310
PLoS One. 2012;7(7):e40711
pubmed: 22792397
Sci Rep. 2016 Aug 10;6:31109
pubmed: 27506634
Commun Biol. 2019 Jun 18;2:212
pubmed: 31240250
G3 (Bethesda). 2016 Aug 09;6(8):2553-61
pubmed: 27317779
BMC Genomics. 2019 Apr 15;20(1):291
pubmed: 30987590
Nat Genet. 2010 Apr;42(4):348-54
pubmed: 20208533
Am J Hum Genet. 2011 Jan 7;88(1):76-82
pubmed: 21167468
Anim Genet. 2009 Dec;40(6):909-16
pubmed: 19719788
Bioinformatics. 2010 Sep 1;26(17):2190-1
pubmed: 20616382
BMC Genomics. 2017 Nov 09;18(1):853
pubmed: 29121857
PLoS One. 2011;6(6):e21400
pubmed: 21731731
Front Genet. 2018 Nov 06;9:522
pubmed: 30459810
Genet Sel Evol. 2019 Jul 1;51(1):34
pubmed: 31262251
Nucleic Acids Res. 2009 Jan;37(1):1-13
pubmed: 19033363
Anim Genet. 2009 Dec;40(6):832-51
pubmed: 19508288
Commun Biol. 2020 Feb 28;3(1):88
pubmed: 32111961
G3 (Bethesda). 2014 Feb 19;4(2):341-7
pubmed: 24362310
Mamm Genome. 2016 Feb;27(1-2):81-97
pubmed: 26613780
Nat Genet. 2014 Aug;46(8):858-65
pubmed: 25017103
J Anim Breed Genet. 2015 Apr;132(2):121-34
pubmed: 25823838
J Dairy Sci. 2016 Nov;99(11):8932-8945
pubmed: 27568046
BMC Genomics. 2014 Jun 06;15:436
pubmed: 24903263
Bioinformatics. 2011 Aug 15;27(16):2300-1
pubmed: 21697123
Genome Res. 2005 Jul;15(7):936-44
pubmed: 15998908
Nat Genet. 2018 May;50(5):746-753
pubmed: 29662166
Nucleic Acids Res. 2012 May;40(9):3777-84
pubmed: 22241776
Sci Rep. 2016 May 05;6:25376
pubmed: 27146958
BMC Genomics. 2018 Jul 4;19(1):521
pubmed: 29973141
J Dairy Sci. 2007 Sep;90(9):4458-65
pubmed: 17699067
J Dairy Sci. 2018 Apr;101(4):3126-3139
pubmed: 29428760