دورية أكاديمية
The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1.
| العنوان: | The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1. |
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| المؤلفون: | Sollero BP; Embrapa Pecuária Sul, Bagé, RS, Brazil., Howard JT; Smithfield Premium Genetics, Rose Hill., Spangler ML; Animal Science Department, University of Nebraska-Lincoln, Lincoln, NE. |
| المصدر: | Journal of animal science [J Anim Sci] 2019 Jul 02; Vol. 97 (7), pp. 2780-2792. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Society of Animal Science Country of Publication: United States NLM ID: 8003002 Publication Model: Print Cited Medium: Internet ISSN: 1525-3163 (Electronic) Linking ISSN: 00218812 NLM ISO Abbreviation: J Anim Sci Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Champaign, IL : American Society of Animal Science |
| مواضيع طبية MeSH: | Gene Frequency* , Genomics*, Cattle/*genetics , Polymorphism, Single Nucleotide/*genetics , Quantitative Trait Loci/*genetics, Alleles ; Animal Husbandry ; Animals ; Breeding ; Data Accuracy ; Female ; Genotype ; Genotyping Techniques/veterinary ; Linkage Disequilibrium ; Male ; Pedigree ; Phenotype ; Pregnancy ; Random Allocation |
| مستخلص: | The largest gains in accuracy in a genomic selection program come from genotyping young selection candidates who have not yet produced progeny and who might, or might not, have a phenotypic record recorded. To reduce genotyping costs and to allow for an increased amount of genomic data to be available in a population, young selection candidates may be genotyped with low-density (LD) panels and imputed to a higher density. However, to ensure that a reasonable imputation accuracy persists overtime, some parent animals originally genotyped at LD must be re-genotyped at a higher density. This study investigated the long-term impact of selectively re-genotyping parents with a medium-density (MD) SNP panel on the accuracy of imputation and on the genetic predictions using ssGBLUP in a simulated beef cattle population. Assuming a moderately heritable trait (0.25) and a population undergoing selection, the simulation generated sequence data for a founder population (100 male and 500 female individuals) and 9,000 neutral markers, considered as the MD panel. All selection candidates from generation 8 to 15 were genotyped with LD panels corresponding to a density of 0.5% (LD_0.5), 2% (LD_2), and 5% (LD_5) of the MD. Re-genotyping scenarios chose parents at random or based on EBV and ranged from 10% of male parents to re-genotyping all male and female parents with MD. Ranges in average imputation accuracy at generation 15 were 0.567 to 0.936, 0.795 to 0.985, and 0.931 to 0.995 for the LD_0.5, LD_2, and LD_5, respectively, and the average EBV accuracies ranged from 0.453 to 0.735, 0.631 to 0.784, and 0.748 to 0.807 for LD_0.5, LD_2, and LD_5, respectively. Re-genotyping parents based on their EBV resulted in higher imputation and EBV accuracies compared to selecting parents at random and these values increased with the size of LD panels. Differences between re-genotyping scenarios decreased when the density of the LD panel increased, suggesting fewer animals needed to be re-genotyped to achieve higher accuracies. In general, imputation and EBV accuracies were greater when more parents were re-genotyped, independent of the proportion of males and females. In practice, the relationship between the density of the LD panel used and the target panel must be considered to determine the number (proportion) of animals that would need to be re-genotyped to enable sufficient imputation accuracy. (© The Author(s) 2019. 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.) |
| References: | Genet Sel Evol. 2002 Jul-Aug;34(4):409-21. (PMID: 12270102) J Dairy Sci. 2008 Nov;91(11):4414-23. (PMID: 18946147) Genet Sel Evol. 2010 Jan 27;42:2. (PMID: 20105297) J Dairy Sci. 2010 Feb;93(2):743-52. (PMID: 20105546) Genet Sel Evol. 2010 Oct 16;42:37. (PMID: 20950478) J Dairy Sci. 2010 Nov;93(11):5487-94. (PMID: 20965364) Genet Sel Evol. 2011 Mar 02;43:10. (PMID: 21366914) Genetics. 2011 Sep;189(1):317-27. (PMID: 21705746) J Anim Breed Genet. 2011 Dec;128(6):422-8. (PMID: 22059575) J Dairy Sci. 2012 Feb;95(2):876-89. (PMID: 22281352) J Dairy Sci. 2012 Sep;95(9):5412-5421. (PMID: 22916948) J Anim Sci. 2012 Dec;90(13):4716-22. (PMID: 23372045) J Anim Sci. 2013 Sep;91(9):4090-8. (PMID: 23893997) BMC Genomics. 2014 Jun 17;15:478. (PMID: 24935670) J Dairy Sci. 2014 Sep;97(9):5822-32. (PMID: 24996280) Genet Sel Evol. 2014 Oct 10;46:69. (PMID: 25927950) J Anim Sci. 2015 Jun;93(6):2653-62. (PMID: 26115253) J Anim Sci. 2015 Jun;93(6):2693-705. (PMID: 26115257) Genet Sel Evol. 2015 Jul 02;47:56. (PMID: 26133806) BMC Genet. 2015 Aug 07;16:99. (PMID: 26250698) Animal. 2016 Jun;10(6):1018-24. (PMID: 26711815) J Anim Sci. 2016 Sep;94(9):3613-3623. (PMID: 27898889) G3 (Bethesda). 2017 Apr 3;7(4):1377-1383. (PMID: 28250015) J Anim Breed Genet. 2017 Dec;134(6):553-563. (PMID: 28464287) Genet Sel Evol. 2017 Oct 24;49(1):76. (PMID: 29065868) G3 (Bethesda). 2018 Jan 4;8(1):113-121. (PMID: 29133511) J Anim Breed Genet. 2018 Feb;135(1):14-27. (PMID: 29345073) J Anim Sci. 2018 Jun 29;96(7):2579-2595. (PMID: 29741705) J Anim Sci. 2018 Nov 21;96(11):4532-4542. (PMID: 30107560) |
| فهرسة مساهمة: | Keywords: beef cattle; genomic selection; genotyping strategy; imputation; low-density panels |
| تواريخ الأحداث: | Date Created: 20190523 Date Completed: 20190920 Latest Revision: 20200701 |
| رمز التحديث: | 20221213 |
| مُعرف محوري في PubMed: | PMC6606532 |
| DOI: | 10.1093/jas/skz147 |
| PMID: | 31115442 |
| قاعدة البيانات: | MEDLINE |
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