Location: Plant Introduction Research
Project Number: 5030-21000-064-006-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2019
End Date: Aug 31, 2022
Objective:
Evaluate and screen Stemphylium leaf spot resistance in USDA spinach germplasm accessions in GRIN (Germplasm Resource Information Network), and conduct genome wide association studies (GWAS) to identify single nucleotide polymorphism (SNP) markers for leaf spot resistance in USDA spinach germplasm in order to conduct marker assisted selection (MAS) and genomic selection (GS) in a spinach breeding program to select plants and lines for Stemphylium leaf spot resistance.
Approach:
The ridge regression best linear unbiased prediction (RR-BLUP) will be used to predict genomic estimated breeding value (GEBV) in genomic selection (GS) and performed in the rrBLUP statistical package with the R software Version 3.5.0 (https://cran.r-project.org/bin/windows/base/rtest.html). RR-BLUP is an effective and accurate prediction method as demonstrated in a wide range of traits and crops. Each training population subset will be randomly selected from the association panel of the 460 spinach genotypes, a total of 368 will be chosen as the training set and the remaining 92 accessions as the validation set with a ratio of 4:1 between the two sets. Single nucleotide polymorphisms (SNP) markers associated with each trait with LOD>=2.0 from GWAS will be used to predict GEBV for leaf spot resistance in each spinach genotype. The prediction accuracy will be estimated using the average Pearson’s correlation coefficient (r) between the GEBVs and observed values for leaf spot disease severity in validation set. The average r value of the 100 times will be calculated for each trait. The r value indicates prediction accuracy and the selection efficiency of GS. The higher of the r value, the more prediction accuracy will be and the better of the selection efficiency in GS. Besides RR-BLUP, researchers may also conduct GS performed with other GS models such as the genomic best linear unbiased prediction (gBLUP)and extended to compressed best linear unbiased prediction (cBLUP) by using the Compressed Mixed Linear Model (CMLM) approach in GAPIT to validate whether the methods provide similar results.
SNP markers with high effect identified from GWAS will be validated through KASP SNP genotyping in the 460 spinach genotypes. Any spinach genotype with high leaf spot resistance will be selected and may be released as a germplasm.
