scholarly journals Identification of Single-Nucleotide Polymorphic Loci Associated with Biomass Yield under Water Deficit in Alfalfa (Medicago sativa L.) Using Genome-Wide Sequencing and Association Mapping

2017 ◽  
Vol 8 ◽  
Author(s):  
Long-Xi Yu
Keyword(s):  
Association Mapping ◽  
Medicago Sativa ◽  
Water Deficit ◽  
Biomass Yield ◽  
Single Nucleotide Polymorphic ◽  
Single Nucleotide ◽  
Polymorphic Loci ◽  
Genome Wide ◽  
Medicago Sativa L

Rapid determination of trisomy 21 from amniotic fluid cells using single-nucleotide polymorphic loci

2005 ◽  
Vol 25 (12) ◽  
pp. 1138-1141 ◽  
Author(s):  
Bálint Nagy ◽  
Zoltán Bán ◽  
Levente Lázár ◽  
Richárd Gyula Nagy ◽  
Csaba Papp ◽  
...  
Keyword(s):  
Amniotic Fluid ◽  
Trisomy 21 ◽  
Rapid Determination ◽  
Single Nucleotide Polymorphic ◽  
Single Nucleotide ◽  
Polymorphic Loci ◽  
Amniotic Fluid Cells

scholarly journals Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Medicago sativa L.)

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3372
Author(s):  
Cesar A. Medina ◽  
Harpreet Kaur ◽  
Ian Ray ◽  
Long-Xi Yu
Keyword(s):  
Salt Stress ◽  
Abiotic Stress ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Breeding Value ◽  
Phenotypic Traits ◽  
Genome Wide ◽  
Medicago Sativa L

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve through conventional breeding approaches. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa (Medicago sativa L.), previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits, such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops, such as alfalfa and potato. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches that use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. Finally, we expended the weighted GBLUP approach to potato and analyzed 13 phenotypic traits and obtained similar results. This is the first report on alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

The Affymetrix Medicago GeneChip® array is applicable for transcript analysis of alfalfa (Medicago sativa)

2006 ◽  
Vol 33 (8) ◽  
pp. 783 ◽  
Author(s):  
Mesfin Tesfaye ◽  
Kevin A. T. Silverstein ◽  
Bruna Bucciarelli ◽  
Deborah A. Samac ◽  
Carroll P. Vance
Keyword(s):  
Medicago Sativa ◽  
Transcript Analysis ◽  
Crop Species ◽  
Trifoliate Leaf ◽  
Model Legume ◽  
Genome Wide ◽  
Medicago Sativa L ◽  
Genome Array ◽  
Medicago Species ◽  
Medicago Genechip

The recently released Affymetrix GeneChip® Medicago Genome Array contains approximately 52 700 probe sets representing genes in both the model legume Medicago truncatula Gaertn. and the closely related crop species Medicago sativa L. (alfalfa). We evaluated the utility of the Medicago GeneChip® for monitoring genome-wide expression of M. truncatula and alfalfa seedlings grown to the first trifoliate leaf stage. We found that approximately 40–54% of the Medicago probes were detected in leaf or root samples of alfalfa or M. truncatula. Approximately 45–59% of the detected Medicago probes were called ‘present’ in all replicate GeneChips of Medicago species, indicating a considerable overlap in the number and type of Medicago probes detected between root and leaf organs. Nevertheless, gene expression differences between roots and leaf organs accounted for approximately 17% of the total variation, regardless of the Medicago species from which the samples were harvested. The result shows that the Medicago GeneChip® is applicable for transcript analysis for both alfalfa and M. truncatula.

scholarly journals Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0169234 ◽  
Author(s):  
Elisa Biazzi ◽  
Nelson Nazzicari ◽  
Luciano Pecetti ◽  
E. Charles Brummer ◽  
Alberto Palmonari ◽  
...  
Keyword(s):  
Association Mapping ◽  
Medicago Sativa ◽  
Genomic Selection ◽  
Forage Quality ◽  
Genome Wide Association ◽  
Quality Traits ◽  
Genome Wide ◽  
Selection For
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