Dataset Information

Leveraging genomic prediction to scan germplasm collection for crop improvement.

ABSTRACT: 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.

SUBMITTER: de Azevedo Peixoto L

PROVIDER: S-EPMC5466325 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Leveraging genomic prediction to scan germplasm collection for crop improvement.

de Azevedo Peixoto Leonardo L Moellers Tara C TC Zhang Jiaoping J Lorenz Aaron J AJ Bhering Leonardo L LL Beavis William D WD Singh Asheesh K AK

PloS one 20170609 6

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 tr ...[more]

PMID: 28598989

Similar Datasets

Project description: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.

Project description:Mentha is a strongly scented herb of the Lamiaceae (formerly Labiatae) and includes about 30 species and hybrid species that are distributed or introduced throughout the globe. These fragrant plants have been selected throughout millennia for use by humans as herbs, spices, and pharmaceutical needs. The distilling of essential oils from mint began in Japan and England but has become a significant industrial product for the US, China, India, and other countries. The US Department of Agriculture (USDA), Agricultural Research Service, National Clonal Germplasm Repository (NCGR) maintains a mint genebank in Corvallis, Oregon. This facility preserves and distributes about 450 clones representing 34 taxa, hybrid species, advanced breeder selections, and F1 hybrids. Mint crop wild relatives are included in this unique resource. The majority of mint accessions and hybrids in this collection were initially donated in the 1970s by the A.M. Todd Company, located in Kalamazoo, Michigan. Other representatives of diverse mint taxa and crop wild relatives have since been obtained from collaborators in Australia, New Zealand, Europe, and Vietnam. These mints have been evaluated for cytology, oil components, verticillium wilt resistance, and key morphological characters. Pressed voucher specimens have been prepared for morphological identity verification. An initial set of microsatellite markers has been developed to determine clonal identity and assess genetic diversity. Plant breeders at private and public institutions are using molecular analysis to determine identity and diversity of the USDA mint collection. Evaluation and characterization includes essential oil content, disease resistance, male sterility, and other traits for potential breeding use. These accessions can be a source for parental genes for enhancement efforts to produce hybrids, or for breeding new cultivars for agricultural production. Propagules of Mentha are available for distribution to international researchers as stem cuttings, rhizome cuttings, or seed, which can be requested through the GRIN-Global database of the US National Plant Germplasm System, subject to international treaty and quarantine regulations.

Project description:BackgroundDuring domestication and subsequent improvement plants were subjected to intensive positive selection for desirable traits. Identification of selection targets is important with respect to the future targeted broadening of diversity in breeding programmes. Rye (Secale cereale L.) is a cereal that is closely related to wheat, and it is an important crop in Central, Eastern and Northern Europe. The aim of the study was (i) to identify diverse groups of rye accessions based on high-density, genome-wide analysis of genetic diversity within a set of 478 rye accessions, covering a full spectrum of diversity within the genus, from wild accessions to inbred lines used in hybrid breeding, and (ii) to identify selective sweeps in the established groups of cultivated rye germplasm and putative candidate genes targeted by selection.ResultsPopulation structure and genetic diversity analyses based on high-quality SNP (DArTseq) markers revealed the presence of three complexes in the Secale genus: S. sylvestre, S. strictum and S. cereale/vavilovii, a relatively narrow diversity of S. sylvestre, very high diversity of S. strictum, and signatures of strong positive selection in S. vavilovii. Within cultivated ryes we detected the presence of genetic clusters and the influence of improvement status on the clustering. Rye landraces represent a reservoir of variation for breeding, and especially a distinct group of landraces from Turkey should be of special interest as a source of untapped variation. Selective sweep detection in cultivated accessions identified 133 outlier positions within 13 sweep regions and 170 putative candidate genes related, among others, to response to various environmental stimuli (such as pathogens, drought, cold), plant fertility and reproduction (pollen sperm cell differentiation, pollen maturation, pollen tube growth), and plant growth and biomass production.ConclusionsOur study provides valuable information for efficient management of rye germplasm collections, which can help to ensure proper safeguarding of their genetic potential and provides numerous novel candidate genes targeted by selection in cultivated rye for further functional characterisation and allelic diversity studies.

Dataset Information

Leveraging genomic prediction to scan germplasm collection for crop improvement.

Publications

Leveraging genomic prediction to scan germplasm collection for crop improvement.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets