Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.
about
Bulked sample analysis in genetics, genomics and crop improvementWhen more is better: how data sharing would accelerate genomic selection of crop plants.Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement.Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials.Seed Quality Traits Can Be Predicted with High Accuracy in Brassica napus Using Genomic Data.Genomic Prediction of Gene Bank Wheat LandracesNatural antisense transcripts are significantly involved in regulation of drought stress in maize.Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.Genome-enabled prediction using probabilistic neural network classifiersIdentification of QTL for Early Vigor and Stay-Green Conferring Tolerance to Drought in Two Connected Advanced Backcross Populations in Tropical Maize (Zea mays L.).Numerous genetic loci identified for drought tolerance in the maize nested association mapping populations.Application of Population Sequencing (POPSEQ) for Ordering and Imputing Genotyping-by-Sequencing Markers in Hexaploid WheatMolecular characterization of CIMMYT maize inbred lines with genotyping-by-sequencing SNPs.Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.Development of a maize 55 K SNP array with improved genome coverage for molecular breeding.Enhancing genetic gain in the era of molecular breeding.Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant BreedingGenomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.Rapid Cycling Genomic Selection in a Multiparental Tropical Maize Population.Genomic prediction for grain zinc and iron concentrations in spring wheat.Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction.Practical application of genomic selection in a doubled-haploid winter wheat breeding program.Genotyping-by-Sequencing Derived High-Density Linkage Map and its Application to QTL Mapping of Flag Leaf Traits in Bread Wheat.Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.Optimum breeding strategies using genomic selection for hybrid breeding in wheat, maize, rye, barley, rice and triticale.Haplotype based genotyping-by-sequencing in oat genome research.Genomic-based-breeding tools for tropical maize improvement.Mapping and Predicting Non-linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation.Genetic Diversity and Population Structure of F3:6 Nebraska Winter Wheat Genotypes Using Genotyping-By-Sequencing.Genomic prediction applied to high-biomass sorghum for bioenergy production.Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations.Genomic selection of agronomic traits in hybrid rice using an NCII population.Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding ProgramAssociation mapping by aerial drone reveals 213 genetic associations for Sorghum bicolor biomass traits under droughtGenomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing
P2860
Q26753827-87935E11-A3B2-4C94-A8F3-FD908B136489Q30490151-FA943B71-EFD5-459E-9212-B467EA9E5BB9Q31043848-A5BC5D78-E54A-47F9-9939-556B88FB88ECQ31122477-D661CD9B-015E-4D40-855F-AC8435ADFBA2Q31144412-FF7529DE-BCCD-48BF-ADA0-BC2AFA273130Q33458233-912D8A43-8761-4F9F-B76E-FEC8DF806243Q33701645-9395BA27-7A0B-4315-84AA-E30462D7DACBQ35677058-F9A5E2DC-D412-4F15-86A6-28D286691560Q35950254-A5DC8035-DBE8-4069-8C15-D18B00ACA0E8Q35964894-F893F0E9-1060-43E9-B6B6-576A4D918197Q36185909-529571DD-8BFF-4F22-8069-E5416654F69AQ36383084-35666DB4-1CDE-4629-8D2D-A0D109DCBA30Q36704513-B780BC26-B8F0-4452-819D-14CF87C240FBQ37629307-9DE2D680-D96F-4A63-B274-D4B71576DAE9Q37643974-D28A746B-F94D-4508-8880-7D1CAE7CF1D9Q38638723-727C7C5A-3FDE-43E2-9FAE-29AAF151CD01Q38771067-5B7DF4F7-6CEB-4FFC-AAF9-5F934AC1F537Q38813502-2D69EDAB-AE15-4571-A927-35BBCF6E8155Q39627315-213ABF19-FD18-4CC8-9997-C06B5CAA1A02Q39773448-DCD9B1FC-0749-4169-81C2-F370E5A22585Q40889525-590D212A-93D3-4001-9F15-4FD21249F8F3Q41598835-5C20AB0D-0323-4D08-93F6-C084A1B8563AQ47134257-23CCB0D4-1122-4D11-83A7-4DDB1CAF7BF1Q47135104-40CFB767-A317-4C6E-988C-D412CE1F1B84Q48169111-F2569662-24B6-49C4-9998-016DDA6CA2B8Q49968757-44DFD1D2-B1BA-49FB-A41D-131E099BCE6FQ50217968-0A12B357-CC5E-40D4-B032-BD14F3C032A0Q50422423-E5E62BD9-D322-4990-A5BF-FA93EADAE51FQ52338678-4DA85A98-F6CE-4437-B204-1B61156D45CFQ54943768-42546E8E-08FD-4853-A4F7-56BA122CFC4FQ54967125-C1F59649-5764-4DBA-A834-8041655DEDF4Q55220537-3C122652-EA31-4057-91DE-A2E1841FD213Q58119784-4C0AEE51-4EDC-4101-BFAD-C41091AA7D42Q58732572-E92CBD96-40B5-47B8-8E1D-E8BDD33D545DQ58777242-3C47ADE7-6575-4D2F-A67A-63E62324ABC5
P2860
Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
Genomic prediction in biparent ...... sing low-density and GBS SNPs.
@en
type
label
Genomic prediction in biparent ...... sing low-density and GBS SNPs.
@en
prefLabel
Genomic prediction in biparent ...... sing low-density and GBS SNPs.
@en
P2093
P2860
P50
P356
P1433
P1476
Genomic prediction in biparent ...... sing low-density and GBS SNPs.
@en
P2093
B M Prasanna
F San Vicente
M A López-Cruz
P2860
P2888
P304
P356
10.1038/HDY.2014.99
P407
P577
2014-11-19T00:00:00Z
P5875
P6179
1013880919