Unlocking Diversity in Germplasm Collections via Genomic Selection: A Case Study Based on Quantitative Adult Plant Resistance to Stripe Rust in Spring Wheat

Abstract

Harnessing diversity from germplasm collections is more feasible today because of the development of lower-cost and higherthroughput genotyping methods. However, the cost of phenotyping is still generally high, so efficient methods of sampling and exploiting useful diversity are needed. Genomic selection (GS) has the potential to enhance the use of desirable genetic variation in germplasm collections through predicting the genomic estimated breeding values (GEBVs) for all traits that have been measured. Here, we evaluated the effects of various scenarios of population genetic properties and marker density on the accuracy of GEBVs in the context of applying GS for wheat (Triticum aestivum L.) germplasm use. Empirical data for adult plant resistance to stripe rust (Puccinia striiformis f. sp. tritici) collected on 1163 spring wheat accessions and genotypic data based on the wheat 9K single nucleotide polymorphism (SNP) iSelect assay were used for various genomic prediction tests. Unsurprisingly, the results of the cross-validation tests demonstrated that prediction accuracy increased with an increase in training population size and marker density. It was evident that using all the available markers (5619) was unnecessary for capturing the trait variation in the germplasm collection, with no further gain in prediction accuracy beyond 1 SNP per 3.2 cM (~1850 markers), which is close to the linkage disequilibrium decay rate in this population. Collectively, our results suggest that larger germplasm collections may be efficiently sampled via lower-density genotyping methods, whereas genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections.

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

“…We identified a cohort of PI accessions with high yield, further demonstrating the wealth of genetic diversity available to soybean breeders in the germplasm collection. These results are consistent with a broader body of research demonstrating the utility of germplasm collection for modern breeding efforts for biotic [48][49][50] and abiotic [51][52][53] resistance and performance traits [33,[54][55][56]. The presence of genetic variation for SY makes this panel of 292 accessions relevant for study objectives as it covers a broader range of performance and background.…”

Development of core-collections for Guizhou tea genetic resources and GWAS of leaf size using SNP developed by genotyping-by-sequencing

“…Among them, 0.208-3.32 Gb clean reads were generated after the quality filtering step in 415 accessions, and 30,282 high-quality SNPs were retained with strict filter conditions. More SNPs were identified in this study compared with previous studies, which meets the requirements of our objectives (Chen et al, 2017;Eltaher et al, 2018).…”

“…The SNPs were filtered according to the methods described by Hussain et al (2017), Chen et al (2017) andEltaher et al (2018) based on the following criteria: (1) variants must be bi-allelic SNPs, (2) ''QUAL <50.0 || QD <2.0 || FS >60.0 || MQ <40.0 || Mapping Quality Rank Sum <−12.5 || Read Pos Rank Sum <−8.0'' was used in Variant Filtration in GATK (V 3.7.0) to filter the SNPs, (3) SNPs with MAF <0.05 or >10% missing data were filtered out by VCFtools (V 0.1.15); (4) a window of 50 SNPs, a step size of ten SNPs, and an r 2 threshold of 0.2 were used to prune the SNPs using Plink (v1.9). As a result, a set of 415 accessions and 30,282 high-quality SNPs were retained and used for subsequent analyses (Table S2).…”

“…Previous studies on association mapping and core-collection development mainly focused on maize (Pace et al, 2015;Suwarno et al, 2015;Coan et al, 2018), sunflower (Heena et al, 2018, wheat (Muleta et al, 2017), pine (Bartholomé et al, 2017), sorghum (Bouchet et al, 2017), finger millet (Babu et al, 2018), melon (Hou et al, 2018), pea (Desgroux et al, 2017 and cotton (Ma et al, 2018a;Ma et al, 2018b;Ma et al, 2018c). By contrast, little has been done on tea germplasm with high density SNPs distributed on the whole genome.…”

Peer Review #2 of "Development of core-collections for Guizhou tea genetic resources and GWAS of leaf size using SNP developed by genotyping-by-sequencing (v0.1)"

“…Manuscript to be reviewed maize (Pace et al, 2015;Suwarno et al, 2015;Coan et al, 2018), sunflower (Heena et al, 2018, wheat (Muleta et al, 2017), pine (Bartholome et al, 2017), sorghum (Bouchet et al, 2017), finger millet (Babu et al, 2018), melon (Hou et al, 2018, pea (Desgroux et al, 2018) and cotton . By contrast, little has been done on tea germplasm with high density SNPs distributed on the whole genome.…”