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Results: 1 to 20 of 152

Similar articles for PubMed (Select 23549338)

1.

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions.

Druet T, Macleod IM, Hayes BJ.

Heredity (Edinb). 2014 Jan;112(1):39-47. doi: 10.1038/hdy.2013.13. Epub 2013 Apr 3.

2.

Accuracy of genotype imputation in sheep breeds.

Hayes BJ, Bowman PJ, Daetwyler HD, Kijas JW, van der Werf JH.

Anim Genet. 2012 Feb;43(1):72-80. doi: 10.1111/j.1365-2052.2011.02208.x. Epub 2011 May 27.

PMID:
22221027
3.

Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.

van Binsbergen R, Bink MC, Calus MP, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF.

Genet Sel Evol. 2014 Jul 15;46:41. doi: 10.1186/1297-9686-46-41.

4.

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations.

Pryce JE, Johnston J, Hayes BJ, Sahana G, Weigel KA 2nd, McParland S, Spurlock D, Krattenmacher N, Spelman RJ, Wall E, Calus MP.

J Dairy Sci. 2014 Mar;97(3):1799-811. doi: 10.3168/jds.2013-7368. Epub 2014 Jan 25.

PMID:
24472132
5.

Assets of imputation to ultra-high density for productive and functional traits.

Jiménez-Montero JA, Gianola D, Weigel K, Alenda R, González-Recio O.

J Dairy Sci. 2013 Sep;96(9):6047-58. doi: 10.3168/jds.2013-6793. Epub 2013 Jun 28.

PMID:
23810591
6.

Genomic relationships based on X chromosome markers and accuracy of genomic predictions with and without X chromosome markers.

Su G, Guldbrandtsen B, Aamand GP, Strandén I, Lund MS.

Genet Sel Evol. 2014 Jul 30;46:47. doi: 10.1186/1297-9686-46-47.

7.

Imputation of genotypes with low-density chips and its effect on reliability of direct genomic values in Dutch Holstein cattle.

Mulder HA, Calus MP, Druet T, Schrooten C.

J Dairy Sci. 2012 Feb;95(2):876-89. doi: 10.3168/jds.2011-4490.

PMID:
22281352
8.

Strategies and utility of imputed SNP genotypes for genomic analysis in dairy cattle.

Khatkar MS, Moser G, Hayes BJ, Raadsma HW.

BMC Genomics. 2012 Oct 8;13:538. doi: 10.1186/1471-2164-13-538.

9.

Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies.

Hao K, Chudin E, McElwee J, Schadt EE.

BMC Genet. 2009 Jun 16;10:27. doi: 10.1186/1471-2156-10-27.

10.

Genomic prediction in maize breeding populations with genotyping-by-sequencing.

Crossa J, Beyene Y, Kassa S, Pérez P, Hickey JM, Chen C, de los Campos G, Burgueño J, Windhausen VS, Buckler E, Jannink JL, Lopez Cruz MA, Babu R.

G3 (Bethesda). 2013 Nov 6;3(11):1903-26. doi: 10.1534/g3.113.008227.

11.

Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.

Badke YM, Bates RO, Ernst CW, Fix J, Steibel JP.

G3 (Bethesda). 2014 Apr 16;4(4):623-31. doi: 10.1534/g3.114.010504.

12.

Evaluation of measures of correctness of genotype imputation in the context of genomic prediction: a review of livestock applications.

Calus MP, Bouwman AC, Hickey JM, Veerkamp RF, Mulder HA.

Animal. 2014 Nov;8(11):1743-53. doi: 10.1017/S1751731114001803. Epub 2014 Jul 21. Review.

PMID:
25045914
13.

Imputation of missing genotypes from sparse to high density using long-range phasing.

Daetwyler HD, Wiggans GR, Hayes BJ, Woolliams JA, Goddard ME.

Genetics. 2011 Sep;189(1):317-27. doi: 10.1534/genetics.111.128082. Epub 2011 Jul 29.

14.

Strategies for imputation to whole genome sequence using a single or multi-breed reference population in cattle.

Brøndum RF, Guldbrandtsen B, Sahana G, Lund MS, Su G.

BMC Genomics. 2014 Aug 27;15:728. doi: 10.1186/1471-2164-15-728.

15.

High-throughput genomics in sorghum: from whole-genome resequencing to a SNP screening array.

Bekele WA, Wieckhorst S, Friedt W, Snowdon RJ.

Plant Biotechnol J. 2013 Dec;11(9):1112-25. doi: 10.1111/pbi.12106. Epub 2013 Aug 7.

PMID:
23919585
16.

Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture.

Pimentel EC, Wensch-Dorendorf M, König S, Swalve HH.

Genet Sel Evol. 2013 Apr 26;45:12. doi: 10.1186/1297-9686-45-12.

17.

Consequences of splitting whole-genome sequencing effort over multiple breeds on imputation accuracy.

Bouwman AC, Veerkamp RF.

BMC Genet. 2014 Oct 3;15:105. doi: 10.1186/s12863-014-0105-8.

18.

GIGI: an approach to effective imputation of dense genotypes on large pedigrees.

Cheung CY, Thompson EA, Wijsman EM.

Am J Hum Genet. 2013 Apr 4;92(4):504-16. doi: 10.1016/j.ajhg.2013.02.011.

19.

The use of family relationships and linkage disequilibrium to impute phase and missing genotypes in up to whole-genome sequence density genotypic data.

Meuwissen T, Goddard M.

Genetics. 2010 Aug;185(4):1441-9. doi: 10.1534/genetics.110.113936. Epub 2010 May 17.

20.

Reliability of genomic prediction for German Holsteins using imputed genotypes from low-density chips.

Segelke D, Chen J, Liu Z, Reinhardt F, Thaller G, Reents R.

J Dairy Sci. 2012 Sep;95(9):5403-11. doi: 10.3168/jds.2012-5466.

PMID:
22916947
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