Genome-wide Association Analysis Tracks Bacterial Leaf Blight Resistance Loci In Rice Diverse Germplasm

Genome-wide association analysis of bacterial blight resistance to nine Xoo strains in 198 indica genotypes based on Efficient Mixed-Model Association eXpedited Model (EMMAX). Manhattan plots for nine Xoo strains (a) PXO61, (b) PXO86, (c) PXO79, (d) PXO71, (e) PXO112, (f) PXO99, (g) PXO339, (h) PXO349, and (i) PXO341. X-axis shows the SNPs along each chromosome; y axis is the − log10 (P-value) for the association. Significant SNPs are those beyond the red line having P-value < 1 × 10 −5. Quantile-quantile plots for nine Xoo strains (j) PXO61, (k) PXO86, (l) PXO79, (m) PXO71, (n) PXO112, (o) PXO99, (p) PXO339, (q) PXO349, and (r) PXO341. (PPTX 521 kb

Additional File 5

Additional file 5. Figure S1. Heat map of the genomic relationship (kinship) matrix of the 204 chile pepper (Capsicum spp.) genotypes derived from 14,922 SNP markers.</p

Genomic Selection—Considerations for Successful Implementation in Wheat Breeding Programs

In order to meet the goal of doubling wheat yield by 2050, breeders must work to improve breeding program efficiency while also implementing new and improved technologies in order to increase genetic gain. Genomic selection (GS) is an expansion of marker assisted selection which uses a statistical model to estimate all marker effects for an individual simultaneously to determine a genome estimated breeding value (GEBV). Breeders are thus able to select for performance based on GEBVs in the absence of phenotypic data. In wheat, genomic selection has been successfully implemented for a number of key traits including grain yield, grain quality and quantitative disease resistance, such as that for Fusarium head blight. For this review, we focused on the ways to modify genomic selection to maximize prediction accuracy, including prediction model selection, marker density, trait heritability, linkage disequilibrium, the relationship between training and validation sets, population structure, and training set optimization methods. Altogether, the effects of these different factors on the accuracy of predictions should be thoroughly considered for the successful implementation of GS strategies in wheat breeding programs

Phenotypic evaluation of USDA peanut (<i>Arachis hypogaea</i> L.) mini-core collection for resistance against stem rot caused by <i>Sclerotium rolfsii</i> Sacc.

Sclerotium rolfsii is a necrotrophic fungus that causes devastating stem rot disease on peanuts under high temperature and humidity conditions. Developing more resistant stem rot varieties is a better disease management strategy from an economic and environmental point of view. In this study, 105 peanut accessions from the USDA mini-core collection were screened for resistance against stem rot under growth chamber and greenhouse conditions at the New Mexico State University Agricultural Science Center in Clovis, NM, USA. The accessions were inoculated with a virulent isolate of S. rolfsii, and disease development was monitored and scored at 5, 7, 9, 11, and 17-days post-inoculation (DPI). Mean disease score, lesion length and broad sense heritability were calculated, and cluster analysis was performed. Though the accessions were not significantly different for the mean disease parameters, numerical differences were observed in disease score and lesion length among the accessions at 17-DPI. Eleven accessions showed moderate resistance and performed better than stem rot resistant commercial cultivar, ‘G03L’, and ninety-four accessions were susceptible based on mean disease scores under both growing conditions. Low broad-sense heritability for disease score (0.05 to 0.06) and lesion length (0.03 to 0.06) under different environments indicates that most of the variance for these traits is due to environmental effects, and that stem rot resistance can be a complex trait. The identified resistant accessions can be used for genotyping and finding major QTLs through genome-wide association mapping to dissect genetic basis of stem rot resistance in peanuts. This information would be helpful to the peanut breeding program to develop more resistant varieties.</p