225 related articles for article (PubMed ID: 31307973)
21. The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa.
Aliloo H; Mrode R; Okeyo AM; Ni G; Goddard ME; Gibson JP
J Dairy Sci; 2018 Oct; 101(10):9108-9127. PubMed ID: 30077450
[TBL] [Abstract][Full Text] [Related]
22. Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes.
Berry DP; McParland S; Kearney JF; Sargolzaei M; Mullen MP
Animal; 2014 Jun; 8(6):895-903. PubMed ID: 24840560
[TBL] [Abstract][Full Text] [Related]
23. Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle.
Boison SA; Utsunomiya ATH; Santos DJA; Neves HHR; Carvalheiro R; Mészáros G; Utsunomiya YT; do Carmo AS; Verneque RS; Machado MA; Panetto JCC; Garcia JF; Sölkner J; da Silva MVGB
J Dairy Sci; 2017 Jul; 100(7):5479-5490. PubMed ID: 28527809
[TBL] [Abstract][Full Text] [Related]
24. Design of low density SNP chips for genotype imputation in layer chicken.
Herry F; Hérault F; Picard Druet D; Varenne A; Burlot T; Le Roy P; Allais S
BMC Genet; 2018 Dec; 19(1):108. PubMed ID: 30514201
[TBL] [Abstract][Full Text] [Related]
25. Technical note: Characteristics and use of the Illumina BovineLD and GeneSeek Genomic Profiler low-density bead chips for genomic evaluation.
Wiggans GR; Cooper TA; Van Tassell CP; Sonstegard TS; Simpson EB
J Dairy Sci; 2013 Feb; 96(2):1258-63. PubMed ID: 23261376
[TBL] [Abstract][Full Text] [Related]
26. Strategies for genotype imputation in composite beef cattle.
Chud TC; Ventura RV; Schenkel FS; Carvalheiro R; Buzanskas ME; Rosa JO; Mudadu Mde A; da Silva MV; Mokry FB; Marcondes CR; Regitano LC; Munari DP
BMC Genet; 2015 Aug; 16():99. PubMed ID: 26250698
[TBL] [Abstract][Full Text] [Related]
27. Imputation of high-density genotypes in the Fleckvieh cattle population.
Pausch H; Aigner B; Emmerling R; Edel C; Götz KU; Fries R
Genet Sel Evol; 2013 Feb; 45(1):3. PubMed ID: 23406470
[TBL] [Abstract][Full Text] [Related]
28. Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.
Piccoli ML; Brito LF; Braccini J; Cardoso FF; Sargolzaei M; Schenkel FS
BMC Genet; 2017 Jan; 18(1):2. PubMed ID: 28100165
[TBL] [Abstract][Full Text] [Related]
29. A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle.
Rowan TN; Hoff JL; Crum TE; Taylor JF; Schnabel RD; Decker JE
Genet Sel Evol; 2019 Dec; 51(1):77. PubMed ID: 31878893
[TBL] [Abstract][Full Text] [Related]
30. 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. PubMed ID: 22221027
[TBL] [Abstract][Full Text] [Related]
31. 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; McParland S; Spurlock D; Krattenmacher N; Spelman RJ; Wall E; Calus MP
J Dairy Sci; 2014 Mar; 97(3):1799-811. PubMed ID: 24472132
[TBL] [Abstract][Full Text] [Related]
32. Effect of imputing markers from a low-density chip on the reliability of genomic breeding values in Holstein populations.
Dassonneville R; Brøndum RF; Druet T; Fritz S; Guillaume F; Guldbrandtsen B; Lund MS; Ducrocq V; Su G
J Dairy Sci; 2011 Jul; 94(7):3679-86. PubMed ID: 21700057
[TBL] [Abstract][Full Text] [Related]
33. Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation.
Cleveland MA; Hickey JM
J Anim Sci; 2013 Aug; 91(8):3583-92. PubMed ID: 23736050
[TBL] [Abstract][Full Text] [Related]
34. Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs.
Grossi DA; Brito LF; Jafarikia M; Schenkel FS; Feng Z
Animal; 2018 Nov; 12(11):2235-2245. PubMed ID: 29706144
[TBL] [Abstract][Full Text] [Related]
35. Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.
Wu XL; Xu J; Feng G; Wiggans GR; Taylor JF; He J; Qian C; Qiu J; Simpson B; Walker J; Bauck S
PLoS One; 2016; 11(9):e0161719. PubMed ID: 27583971
[TBL] [Abstract][Full Text] [Related]
36. Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.
Lopes FB; Wu XL; Li H; Xu J; Perkins T; Genho J; Ferretti R; Tait RG; Bauck S; Rosa GJM
J Anim Breed Genet; 2018 Feb; 135(1):14-27. PubMed ID: 29345073
[TBL] [Abstract][Full Text] [Related]
37. Short communication: Imputation performances of 3 low-density marker panels in beef and dairy cattle.
Dassonneville R; Fritz S; Ducrocq V; Boichard D
J Dairy Sci; 2012 Jul; 95(7):4136-40. PubMed ID: 22720970
[TBL] [Abstract][Full Text] [Related]
38. Justification for setting the individual animal genotype call rate threshold at eighty-five percent.
Purfield DC; McClure M; Berry DP
J Anim Sci; 2016 Nov; 94(11):4558-4569. PubMed ID: 27898963
[TBL] [Abstract][Full Text] [Related]
39. Hot topic: performance of bovine high-density genotyping platforms in Holsteins and Jerseys.
Rincon G; Weber KL; Eenennaam AL; Golden BL; Medrano JF
J Dairy Sci; 2011 Dec; 94(12):6116-21. PubMed ID: 22118099
[TBL] [Abstract][Full Text] [Related]
40. The impact of multi-generational genotype imputation strategies on imputation accuracy and subsequent genomic predictions.
Judge MM; Purfield DC; Sleator RD; Berry DP
J Anim Sci; 2017 Apr; 95(4):1489-1501. PubMed ID: 28464096
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]