Leveraging genomic prediction to scan germplasm collection for crop improvement

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops

Predictive ability and standard deviation reported using RR-BLUP from white mold evaluation experiments in field and greenhouse (2014 and 2015), when one environment was used to train the model and validated on another environment.

<p>The predictive ability (estimated by 10 fold cross-validation) using the same experiment for training and validation population was used as control.</p

Relationship between predictive ability and the number of SNP markers using a training population of 352 genotypes among 465 diverse soybean accessions tested for white mold in 2014 and 2015 field and greenhouse experiments.

<p>Relationship between predictive ability and the number of SNP markers using a training population of 352 genotypes among 465 diverse soybean accessions tested for white mold in 2014 and 2015 field and greenhouse experiments.</p

Predictive ability for white mold reaction phenotyped for WM in field and greenhouse screening in 2014 and 2015 using RR-BLUP for differing training population sizes using 5 k SNP markers using 465 diverse soybean accessions.

<p>Predictive ability for white mold reaction phenotyped for WM in field and greenhouse screening in 2014 and 2015 using RR-BLUP for differing training population sizes using 5 k SNP markers using 465 diverse soybean accessions.</p

Scheme demonstrating the use of genomic selection models in training, validating, and testing sets.

<p>Top– 5% and 10% most resistant accessions for WM found in the present study (F2015); Bottom– 5% and 10% most susceptible accessions for WM found in the present study (F2015); USDA_Top– 5% most resistant accessions found in USDA soybean germplasm collection; Test–Resistant accessions previously reported by other researches. GWS = Genome wide selection, GEBV = Genomic estimated breeding value, WM = white mold.</p