scholarly journals Genomic selection analyses reveal tradeoff between chestnut blight tolerance and genome inheritance from American chestnut (Castanea dentata) in (C. dentataxC. mollissima) xC. dentatabackcross populations

2019 ◽  
Author(s):  
Jared W. Westbrook ◽  
Qian Zhang ◽  
Mihir K. Mandal ◽  
Eric V. Jenkins ◽  
Laura E. Barth ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Major Gene ◽  
Predictive Ability ◽  
Cryphonectria Parasitica ◽  
Major Effect ◽  
Castanea Dentata ◽  
American Chestnut ◽  
The Past ◽  
Type Form

AbstractAmerican chestnut was once a foundation species of eastern North American forests, but was rendered functionally extinct in the early 20th century by an exotic fungal blight (Cryphonectria parasitica). Over the past 30 years, The American Chestnut Foundation (TACF) has pursued backcross breeding to generate hybrids that combine the timber-type form of American chestnut with the blight tolerance of Chinese chestnut. The backcross strategy has been implemented based on the hypothesis that blight tolerance is conferred by few major effect alleles. We tested this hypothesis by developing genomic prediction models for five presence/absence blight phenotypes of 1,230 BC3F2selection candidates and average canker severity of their BC3F3progeny. We also genotyped pure Chinese and American chestnut reference panels to estimate the proportion of BC3F2genomes inherited from parent species. We found that genomic prediction from a method that assumes an infinitesimal model of inheritance (HBLUP) has a similar predictive ability to a method that tends to perform well for traits controlled by major genes (Bayes C). Furthermore, the proportion of BC3F2trees’ genomes inherited from American chestnut was negatively correlated with the blight tolerance of BC3F2trees and their progeny. On average, selected BC3F2trees inherited 83% of their genome from American chestnut and have blight-tolerance that is intermediate between F1hybrids and American chestnut. Results suggest polygenic rather than major gene inheritance for blight tolerance. The blight-tolerance of restoration populations will be enhanced by advancing additional sources of blight-tolerance through fewer backcross generations and by potentially by breeding with transgenic blight-tolerant trees.

Ex situ conservation of American chestnut (Castanea dentata (Marsh.) Borkh.) and butternut (Juglans cinerea L.), a review

2000 ◽  
Vol 76 (5) ◽  
pp. 765-774 ◽  
Author(s):  
Ken McIlwrick ◽  
S. Wetzel ◽  
T. Beardmore ◽  
K. Forbes
Keyword(s):  
Genetic Information ◽  
Ex Situ Conservation ◽  
In Situ Conservation ◽  
Cryphonectria Parasitica ◽  
Castanea Dentata ◽  
American Chestnut ◽  
Ex Situ ◽  
Juglans Cinerea ◽  
Research Areas

Two tree species native to North America, American chestnut (Castanea dentata (Marsh.) Borkh.) and butternut (Juglans cinerea L.), which have experienced rapid declines in their populations due to similar stressors (disease and changes in land use), are used as examples of how these species would benefit from ex situ conservation efforts. Current and past ex situ and in situ conservation efforts for these species are discussed and the focus of this review is on two key research areas: 1) what needs to be preserved (genetic information) and 2) how to preserve these trees or germplasm. Key words: butternut, American chestnut, Cryphonectria parasitica, Sirococcus clavigignenti-juglandacearum, ex situ conservation

scholarly journals Resistance toPhytophthora cinnamomiin American Chestnut (Castanea dentata) Backcross Populations that Descended from Two Chinese Chestnut (Castanea mollissima) Sources of Resistance

Plant Disease ◽  
2019 ◽  
Vol 103 (7) ◽  
pp. 1631-1641 ◽  
Author(s):  
Jared W. Westbrook ◽  
Joseph B. James ◽  
Paul H. Sisco ◽  
John Frampton ◽  
Sunny Lucas ◽  
...  
Keyword(s):  
Phytophthora Cinnamomi ◽  
Seedling Survival ◽  
Cryphonectria Parasitica ◽  
Castanea Dentata ◽  
American Chestnut ◽  
Chestnut Blight ◽  
Sources Of Resistance ◽  
Backcross Population ◽  
Chinese Chestnut ◽  
Root Health

Restoration of American chestnut (Castanea dentata) depends on combining resistance to both the chestnut blight fungus (Cryphonectria parasitica) and Phytophthora cinnamomi, which causes Phytophthora root rot, in a diverse population of C. dentata. Over a 14-year period (2004 to 2017), survival and root health of American chestnut backcross seedlings after inoculation with P. cinnamomi were compared among 28 BC3, 66 BC4, and 389 BC3F3families that descended from two BC1trees (Clapper and Graves) with different Chinese chestnut grandparents. The 5% most resistant Graves BC3F3families survived P. cinnamomi infection at rates of 75 to 100% but had mean root health scores that were intermediate between resistant Chinese chestnut and susceptible American chestnut families. Within Graves BC3F3families, seedling survival was greater than survival of Graves BC3and BC4families and was not genetically correlated with chestnut blight canker severity. Only low to intermediate resistance to P. cinnamomi was detected among backcross descendants from the Clapper tree. Results suggest that major-effect resistance alleles were inherited by descendants from the Graves tree, that intercrossing backcross trees enhances progeny resistance to P. cinnamomi, and that alleles for resistance to P. cinnamomi and C. parasitica are not linked. To combine resistance to both C. parasitica and P. cinnamomi, a diverse Graves backcross population will be screened for resistance to P. cinnamomi, survivors bred with trees selected for resistance to C. parasitica, and progeny selected for resistance to both pathogens will be intercrossed.

scholarly journals First Report of Ectomycorrhizal Fungus, Laccaria ochropurpurea, Associated with Castanea dentata (American Chestnut) Roots in a Mixed-Species Plantation

2019 ◽  
Vol 20 (3) ◽  
pp. 140-141
Author(s):  
Morgan V. Ritzi ◽  
Stephen D. Russell ◽  
M. Catherine Aime ◽  
Gordon G. McNickle
Keyword(s):  
Cryphonectria Parasitica ◽  
Ectomycorrhizal Fungus ◽  
Castanea Dentata ◽  
American Chestnut ◽  
Fungal Colonization ◽  
Root Tips ◽  
Mixed Species ◽  
Plant Host ◽  
Mutualistic Association ◽  
Direct Confirmation

American chestnut (Castanea dentata) is critically endangered by chestnut blight caused by Cryphonectria parasitica. Beneficial interactions with mutualistic ectomycorrhizae sometimes confer resistance to pathogens; however, little is known about the mycorrhizal partners of American chestnut. Basidiocarps of Laccaria ochropurpurea were observed in a 10-year-old American chestnut plantation. The identity of the species was confirmed utilizing the nuclear ribosomal internal transcribed spacer. In spring 2018, root fragments were excised from beneath three American chestnut trees in three separate plots where basidiocarps were observed. Root tips with evidence of mycorrhizal fungal colonization were pooled, extracted, and sequenced to confirm both the plant host and mycorrhizal associates. To our knowledge, this is the first direct confirmation of American chestnut roots associated with L. ochropurpurea. We suggest further studies to investigate whether this association is common, whether it confers any disease resistance, and if this mutualistic association could be employed in restoration efforts of the American chestnut.

scholarly journals CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction

2020 ◽  
Author(s):  
Rafael Massahiro Yassue ◽  
José Felipe Gonzaga Sabadin ◽  
Giovanni Galli ◽  
Filipe Couto Alves ◽  
Roberto Fritsche-Neto
Keyword(s):  
Genomic Prediction ◽  
Cross Validation ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Predictive Ability ◽  
Proof Of Concept ◽  
Squared Error ◽  
High Effect ◽  
The Mean ◽  
Fold Cross Validation

AbstractUsually, the comparison among genomic prediction models is based on validation schemes as Repeated Random Subsampling (RRS) or K-fold cross-validation. Nevertheless, the design of training and validation sets has a high effect on the way and subjectiveness that we compare models. Those procedures cited above have an overlap across replicates that might cause an overestimated estimate and lack of residuals independence due to resampling issues and might cause less accurate results. Furthermore, posthoc tests, such as ANOVA, are not recommended due to assumption unfulfilled regarding residuals independence. Thus, we propose a new way to sample observations to build training and validation sets based on cross-validation alpha-based design (CV-α). The CV-α was meant to create several scenarios of validation (replicates x folds), regardless of the number of treatments. Using CV-α, the number of genotypes in the same fold across replicates was much lower than K-fold, indicating higher residual independence. Therefore, based on the CV-α results, as proof of concept, via ANOVA, we could compare the proposed methodology to RRS and K-fold, applying four genomic prediction models with a simulated and real dataset. Concerning the predictive ability and bias, all validation methods showed similar performance. However, regarding the mean squared error and coefficient of variation, the CV-α method presented the best performance under the evaluated scenarios. Moreover, as it has no additional cost nor complexity, it is more reliable and allows the use of non-subjective methods to compare models and factors. Therefore, CV-α can be considered a more precise validation methodology for model selection.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Md. Abdullah Al Bari ◽  
Ping Zheng ◽  
Indalecio Viera ◽  
Hannah Worral ◽  
Stephen Szwiec ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Seed Yield ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Germplasm Collection ◽  
Predictive Ability ◽  
Snp Markers ◽  
Breeding Values ◽  
Germplasm Collections

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.

scholarly journals Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.)

2020 ◽  
Vol 10 (3) ◽  
pp. 1113-1124 ◽  
Author(s):  
Madhav Bhatta ◽  
Lucia Gutierrez ◽  
Lorena Cammarota ◽  
Fernanda Cardozo ◽  
Silvia Germán ◽  
...  
Keyword(s):  
Prediction Model ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Prediction Models ◽  
Agronomic Traits ◽  
Predictive Ability ◽  
Malting Quality ◽  
Quality Traits ◽  
Multiple Traits ◽  
Correlated Traits

Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits (eight agronomic and malting quality traits) into the prediction models with two cross-validation schemes (CV1, predicting new lines with genotypic information only and CV2, predicting partially phenotyped lines using both genotypic and phenotypic information from correlated traits) in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles.

Genetic Diversity of Castanea pulia var. ozarkensis

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 526e-527
Author(s):  
Fenny Dane ◽  
Shona Harrison ◽  
Hongwen Huang
Keyword(s):  
Genetic Diversity ◽  
Cryphonectria Parasitica ◽  
American Chestnut ◽  
Chestnut Blight ◽  
Ozark Highlands ◽  
The Past ◽  
Geographic Populations ◽  
Natural Range ◽  
Rapd Polymorphism ◽  
Taxonomic Studies

The genus Castanea includes several species, some of which, like the American chestnut (C. dentata) and chinkapin (C. pumila), are susceptible to chestnut blight, caused by the Asian fungus Cryphonectria parasitica. Blight spread throughout the natural range of the American chestnut, destroying several billion trees within the past 50 years. Although the plight of the American chestnut is well-known, the chinkapin has been neglected. Taxonomic studies indicated two varieties, the Ozark chinkapin, var. ozarkensis, limited to the Ozark Highlands of Arkansas, Missouri, and Oklahoma, and the Allegheny chinkapin, var. pumila, found from New Jersey to Florida and Texas. The genetic diversity within and between 11 geographic populations of the Ozark chinkapin was evaluated to provide baseline genetic information pertinent to the conservation and restoration of this species. Nuts or dormant buds of chinkapin trees were evaluated for isozyme and RAPD polymorphism. The genetic variability of the Ozark chinkapin populations was relatively high when compared to the American chestnut, and most of the diversity resides within the populations. Conservation considerations for restoration of the Ozark chinkapin will be discussed.

scholarly journals Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model

2019 ◽  
Vol 15 ◽  
pp. 117693431984002 ◽  
Author(s):  
Reka Howard ◽  
Diego Jarquin
Keyword(s):  
Molecular Marker ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Predictive Ability ◽  
Image Data ◽  
Nested Association Mapping ◽  
Canopy Coverage ◽  
Prediction Techniques ◽  
And Training ◽  
Marker Information

Prediction techniques are important in plant breeding as they provide a tool for selection that is more efficient and economical than traditional phenotypic and pedigree based selection. The conventional genomic prediction models include molecular marker information to predict the phenotype. With the development of new phenomics techniques we have the opportunity to collect image data on the plants, and extend the traditional genomic prediction models where we incorporate diverse set of information collected on the plants. In our research, we developed a hybrid matrix model that incorporates molecular marker and canopy coverage information as a weighted linear combination to predict grain yield for the soybean nested association mapping (SoyNAM) panel. To obtain the testing and training sets, we clustered the individuals based on their marker and canopy information using 2 different clustering techniques, and we compared 5 different cross-validation schemes. The results showed that the predictive ability of the models was the highest when both the canopy and marker information was included, and it was the lowest when only the canopy information was included.

scholarly journals Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

2021 ◽  
Vol 11 ◽  
Author(s):  
Diego Jarquin ◽  
Natalia de Leon ◽  
Cinta Romay ◽  
Martin Bohn ◽  
Edward S. Buckler ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Combining Ability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Weather Data ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Environment Interaction ◽  
Environmental Covariates ◽  
Genotype By Environment

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

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