702 related articles for article (PubMed ID: 26830693)
21. Accuracy of prediction of simulated polygenic phenotypes and their underlying quantitative trait loci genotypes using real or imputed whole-genome markers in cattle.
Hassani S; Saatchi M; Fernando RL; Garrick DJ
Genet Sel Evol; 2015 Dec; 47():99. PubMed ID: 26698091
[TBL] [Abstract][Full Text] [Related]
22. Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments.
Kumar S; Molloy C; Muñoz P; Daetwyler H; Chagné D; Volz R
G3 (Bethesda); 2015 Oct; 5(12):2711-8. PubMed ID: 26497141
[TBL] [Abstract][Full Text] [Related]
23. Genotype Imputation To Improve the Cost-Efficiency of Genomic Selection in Farmed Atlantic Salmon.
Tsai HY; Matika O; Edwards SM; Antolín-Sánchez R; Hamilton A; Guy DR; Tinch AE; Gharbi K; Stear MJ; Taggart JB; Bron JE; Hickey JM; Houston RD
G3 (Bethesda); 2017 Apr; 7(4):1377-1383. PubMed ID: 28250015
[TBL] [Abstract][Full Text] [Related]
24. Prediction accuracies for growth and wood attributes of interior spruce in space using genotyping-by-sequencing.
Gamal El-Dien O; Ratcliffe B; Klápště J; Chen C; Porth I; El-Kassaby YA
BMC Genomics; 2015 May; 16(1):370. PubMed ID: 25956247
[TBL] [Abstract][Full Text] [Related]
25. 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]
26. Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass.
Ramstein GP; Lipka AE; Lu F; Costich DE; Cherney JH; Buckler ES; Casler MD
G3 (Bethesda); 2015 Mar; 5(5):891-909. PubMed ID: 25770100
[TBL] [Abstract][Full Text] [Related]
27. Generating High Density, Low Cost Genotype Data in Soybean [
Happ MM; Wang H; Graef GL; Hyten DL
G3 (Bethesda); 2019 Jul; 9(7):2153-2160. PubMed ID: 31072870
[TBL] [Abstract][Full Text] [Related]
28. Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle.
Veerkamp RF; Bouwman AC; Schrooten C; Calus MP
Genet Sel Evol; 2016 Dec; 48(1):95. PubMed ID: 27905878
[TBL] [Abstract][Full Text] [Related]
29. Identification of novel loci associated with maturity and yield traits in early maturity soybean plant introduction lines.
Copley TR; Duceppe MO; O'Donoughue LS
BMC Genomics; 2018 Mar; 19(1):167. PubMed ID: 29490606
[TBL] [Abstract][Full Text] [Related]
30. Genomic predictions based on animal models using genotype imputation on a national scale in Norwegian Red cattle.
Meuwissen TH; Svendsen M; Solberg T; Ødegård J
Genet Sel Evol; 2015 Oct; 47():79. PubMed ID: 26464226
[TBL] [Abstract][Full Text] [Related]
31. Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar).
Bangera R; Correa K; Lhorente JP; Figueroa R; Yáñez JM
BMC Genomics; 2017 Jan; 18(1):121. PubMed ID: 28143402
[TBL] [Abstract][Full Text] [Related]
32. 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; 3(11):1903-26. PubMed ID: 24022750
[TBL] [Abstract][Full Text] [Related]
33. Accuracy of genomic selection for a sib-evaluated trait using identity-by-state and identity-by-descent relationships.
Vela-Avitúa S; Meuwissen TH; Luan T; Ødegård J
Genet Sel Evol; 2015 Feb; 47(1):9. PubMed ID: 25888184
[TBL] [Abstract][Full Text] [Related]
34. Contribution of rare and low-frequency whole-genome sequence variants to complex traits variation in dairy cattle.
Zhang Q; Calus MPL; Guldbrandtsen B; Lund MS; Sahana G
Genet Sel Evol; 2017 Aug; 49(1):60. PubMed ID: 28764638
[TBL] [Abstract][Full Text] [Related]
35. Estimation of variance and genomic prediction using genotypes imputed from low-density marker subsets for carcass traits in Japanese black cattle.
Ogawa S; Matsuda H; Taniguchi Y; Watanabe T; Sugimoto Y; Iwaisaki H
Anim Sci J; 2016 Sep; 87(9):1106-13. PubMed ID: 26685777
[TBL] [Abstract][Full Text] [Related]
36. Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea).
Dong L; Xiao S; Wang Q; Wang Z
BMC Genomics; 2016 Jun; 17():460. PubMed ID: 27301965
[TBL] [Abstract][Full Text] [Related]
37. Effects of number of training generations on genomic prediction for various traits in a layer chicken population.
Weng Z; Wolc A; Shen X; Fernando RL; Dekkers JC; Arango J; Settar P; Fulton JE; O'Sullivan NP; Garrick DJ
Genet Sel Evol; 2016 Mar; 48():22. PubMed ID: 26992471
[TBL] [Abstract][Full Text] [Related]
38. Genome-wide association and genomic prediction identifies associated loci and predicts the sensitivity of Tobacco ringspot virus in soybean plant introductions.
Chang HX; Brown PJ; Lipka AE; Domier LL; Hartman GL
BMC Genomics; 2016 Feb; 17():153. PubMed ID: 26924079
[TBL] [Abstract][Full Text] [Related]
39. An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.
Sun C; Wu XL; Weigel KA; Rosa GJ; Bauck S; Woodward BW; Schnabel RD; Taylor JF; Gianola D
Genet Res (Camb); 2012 Jun; 94(3):133-50. PubMed ID: 22809677
[TBL] [Abstract][Full Text] [Related]
40. Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.
Frischknecht M; Pausch H; Bapst B; Signer-Hasler H; Flury C; Garrick D; Stricker C; Fries R; Gredler-Grandl B
BMC Genomics; 2017 Dec; 18(1):999. PubMed ID: 29284405
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
