Genomic Selection for Ascochyta Blight Resistance in Pea
| Autor: | Gail M. Timmerman-Vaughan, Rebecca D. Cooper, Margaret A. Carpenter, David S. Goulden, Tonya J. Frew, Susan Thomson, Carmel Woods, Fernand Kenel |
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| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
0301 basic medicine disease resistance pea Population Plant Science lcsh:Plant culture Best linear unbiased prediction Plant disease resistance 01 natural sciences genomic selection 03 medical and health sciences Statistics genotyping-by-sequencing Blight lcsh:SB1-1110 Plant breeding education Original Research education.field_of_study biology food and beverages Ascochyta biology.organism_classification Missing data 030104 developmental biology ascochyta blight Trait 010606 plant biology & botany |
| Zdroj: | Frontiers in Plant Science Frontiers in Plant Science, Vol 9 (2018) |
| ISSN: | 1664-462X |
| DOI: | 10.3389/fpls.2018.01878 |
| Popis: | Genomic selection (GS) is a breeding tool, which is rapidly gaining popularity for plant breeding, particularly for traits that are difficult to measure. One such trait is ascochyta blight resistance in pea (Pisum sativum L.), which is difficult to assay because it is strongly influenced by the environment and depends on the natural occurrence of multiple pathogens. Here we report a study of the efficacy of GS for predicting ascochyta blight resistance in pea, as represented by ascochyta blight disease score (ASC), and using nucleotide polymorphism data acquired through genotyping-by-sequencing. The effects on prediction accuracy of different GS models and different thresholds for missing genotypic data (which modified the number of single nucleotide polymorphisms used in the analysis) were compared using cross-validation. Additionally, the inclusion of marker × environment interactions in a genomic best linear unbiased prediction (GBLUP) model was evaluated. Finally, different ways of combining trait data from two field trials using bivariate, spatial, and single-stage analyses were compared to results obtained using a mean value. The best prediction accuracy achieved for ASC was 0.56, obtained using GBLUP analysis with a mean value for ASC and data quality threshold of 70% (i.e., missing SNP data in |
| Databáze: | OpenAIRE |
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