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Items: 1 to 20 of 192

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.

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.

5.

Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy.

Bolormaa S, Gore K, van der Werf JH, Hayes BJ, Daetwyler HD.

Anim Genet. 2015 Oct;46(5):544-56. doi: 10.1111/age.12340. Epub 2015 Sep 11.

PMID:
26360638
6.

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
7.

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.

van Binsbergen R, Calus MP, Bink MC, van Eeuwijk FA, Schrooten C, Veerkamp RF.

Genet Sel Evol. 2015 Sep 17;47:71. doi: 10.1186/s12711-015-0149-x.

8.

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
9.

Fast imputation using medium or low-coverage sequence data.

VanRaden PM, Sun C, O'Connell JR.

BMC Genet. 2015 Jul 14;16:82. doi: 10.1186/s12863-015-0243-7.

10.

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.

11.

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
12.

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.

13.

Rare variant genotype imputation with thousands of study-specific whole-genome sequences: implications for cost-effective study designs.

Pistis G, Porcu E, Vrieze SI, Sidore C, Steri M, Danjou F, Busonero F, Mulas A, Zoledziewska M, Maschio A, Brennan C, Lai S, Miller MB, Marcelli M, Urru MF, Pitzalis M, Lyons RH, Kang HM, Jones CM, Angius A, Iacono WG, Schlessinger D, McGue M, Cucca F, Abecasis GR, Sanna S.

Eur J Hum Genet. 2015 Jul;23(7):975-83. doi: 10.1038/ejhg.2014.216. Epub 2014 Oct 8.

PMID:
25293720
14.

Potential of genotyping-by-sequencing for genomic selection in livestock populations.

Gorjanc G, Cleveland MA, Houston RD, Hickey JM.

Genet Sel Evol. 2015 Mar 1;47:12. doi: 10.1186/s12711-015-0102-z.

15.

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.

16.

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.

17.

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
18.

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.

19.

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.

20.

Accuracy of imputation using the most common sires as reference population in layer chickens.

Heidaritabar M, Calus MP, Vereijken A, Groenen MA, Bastiaansen JW.

BMC Genet. 2015 Aug 18;16:101. doi: 10.1186/s12863-015-0253-5.

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