EFFECTS OF MARKER DENSITY AND TRAINING SET SIZE ON THE GENOMIC SELECTION ACCURACY FOR PREDICTING SHEATH BLIGHT RESISTANCE IN RICE (ORYZA SATIVA L.)Mahantesh, K. Ganesamurthy, Sayan Das, R. Saraswathi, C. Gopalakrishnan and R. GnanamAbstract The success of genomic selection mainly depends on the extent of linkage disequilibrium between markers and quantitative trait loci, size of training set, heritability of the trait etc. The extent of linkage disequilibrium depends on the genetic structure of the population and marker density. This study was conducted to determine the effects of marker density and size of training set on prediction accuracy using 1545 recombinant inbred lines derived from eleven bi-parental rice populations. All RILs were genotyped with 6564 SNPs and screened in two hot spot locations to assess reaction against sheath blight.Bayesian B model was used to train the statistical model for calculation of marker effects and genomic estimated breeding values. To evaluate the genomic prediction accuracy, various levels of training set size (300, 500, 700, 900 and 1200 lines) and marker density (500, 800, 1100, 1400, 1700, 2000, 4000 and 6000 markers) were considered. In our study, the prediction accuracy increased with increase in training set size, however, average prediction accuracy of 0.717 was obtained for the training set comprising of 900 lines before reaching plateau with marginal increase in prediction accuracy with higher training set sizes. The predictive ability increased dramatically with more SNPs included in the analysis until 2000 markers with average prediction accuracy of 0.681, no significant improvement beyond this was observed. The results indicate that training set with approximately 900 lines and 2000 uniformly distributed SNP markers with good amount of polymorphism across populations would be enough to reach achievable accuracy to predict sheath blight resistance in rice. |