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Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.

ABSTRACT: Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

SUBMITTER: Bari MAA

PROVIDER: S-EPMC8740293 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Sweetpotato (Ipomoea batatas) is the sixth most important food crop and plays a critical role in maintaining food security worldwide. Support for sweetpotato improvement research in breeding and genetics programs, and maintenance of sweetpotato germplasm collections is essential for preserving food security for future generations. Germplasm collections seek to preserve phenotypic and genotypic diversity through accession characterization. However, due to its genetic complexity, high heterogeneity, polyploid genome, phenotypic plasticity, and high flower production variability, sweetpotato genetic characterization is challenging. Here, we characterize the genetic diversity and population structure of 604 accessions from the sweetpotato germplasm collection maintained by the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), Plant Genetic Resources Conservation Unit (PGRCU) in Griffin, Georgia, United States. Using the genotyping-by-sequencing platform (GBSpoly) and bioinformatic pipelines (ngsComposer and GBSapp), a total of 102,870 polymorphic SNPs with hexaploid dosage calls were identified from the 604 accessions. Discriminant analysis of principal components (DAPC) and Bayesian clustering identified six unique genetic groupings across seven broad geographic regions. Genetic diversity analyses using the hexaploid data set revealed ample genetic diversity among the analyzed collection in concordance with previous analyses. Following population structure and diversity analyses, breeder germplasm subsets of 24, 48, 96, and 384 accessions were established using K-means clustering with manual selection to maintain phenotypic and genotypic diversity. The genetic characterization of the PGRCU sweetpotato germplasm collection and breeder germplasm subsets developed in this study provide the foundation for future association studies and serve as precursors toward phenotyping studies aimed at linking genotype with phenotype.

Project description:The World Collection of Sugarcane and Related Grasses, maintained at the USDA-ARS in Miami, FL, is one of the largest sugarcane germplasm repositories in the world. However, the genetic integrity of the Saccharum spp. germplasm in this collection has not been fully analyzed. In this study, we employed a single-dose SNP panel to genotype 901 sugarcane accessions, representing six Saccharum species and various hybrids. Our analysis uncovered a high rate of clone mislabeling in the collection. Specifically, we identified 86 groups of duplicates, characterized by identical SNP genotypes, which encompassed 211 accessions (23% of the total clones), while 135 groups, constituting 471 clones (52% of the total), exhibited near-identical genotypes. In addition, twenty-seven homonymous groups were detected, which shared the same clone name but differed in SNP genotypes. Hierarchical analysis of population structure partitioned the Saccharum germplasm into five clusters, corresponding to S. barberi, S. sinense, S. officinarum, S. spontaneum and S. robustum/S. edule. An assignment test, based on the five Saccharum species, enabled correcting 141 instances of mislabeled species memberships and inaccuracies. Moreover, we clarified the species membership and parentage of 298 clones that had ambiguous passport records (e.g., 'Saccharum spp', 'unknown', and 'hybrid'). Population structure and genetic diversity in these five species were further supported by Principal Coordinate Analysis and neighbor-joining clustering analysis. Analysis of Molecular Variance revealed that within-species genetic variations accounted for 85% of the total molecular variance, with the remaining 15% attributed to among-species genetic variations. The single-dose SNP markers developed in this study offer a robust tool for characterizing sugarcane germplasm worldwide. These findings have important implications for sugarcane genebank management, germplasm exchange, and crop genetic improvement.

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Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.

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