scholarly journals Genomic Prediction for Fusiform Rust Disease Incidence in a Large Cloned Population of Pinus taeda

2021 ◽  
Author(s):  
Mohammad Nasir Shalizi ◽  
W Patrick Cumbie ◽  
Fikret Isik
Keyword(s):  
Genomic Selection ◽  
Pinus Taeda ◽  
Random Sampling ◽  
Cross Validation ◽  
Disease Incidence ◽  
Rust Disease ◽  
Fusiform Rust ◽  
Selection For ◽  
Model Training ◽  
Estimated Breeding Values

Abstract In this study, 723 Pinus taeda L. (loblolly pine) clonal varieties genotyped with 16920 SNP markers were used to evaluate genomic selection for fusiform rust disease caused by the fungus Cronartium quercuum f. sp. fusiforme. The 723 clonal varieties were from five full-sib families. They were a subset of a larger population (1831 clonal varieties), field-tested across 26 locations in the southeast US. Ridge regression, Bayes B and Bayes Cπ models were implemented to study marker-trait associations and estimate predictive ability for selection. A cross-validation scenario based on random sampling of 80% of the clonal varieties for model building had higher (0.71- 0.76) prediction accuracies of genomic estimated breeding values compared with family and within-family cross-validation scenarios. Random sampling within families for model training to predict genomic estimated breeding values of the remaining progenies within each family produced accuracies between 0.38 to 0.66. Using four families out of five for model training was not successful. The results showed the importance of genetic relatedness between the training and validation sets. Bayesian whole genome regression models detected three QTL with large effects on the disease outcome, explaining 54% of the genetic variation in the trait. The significance of QTL was validated with GWAS while accounting for the population structure and polygenic effect. The odds of disease incidence for heterozygous AB genotypes were 10.7 and 12.1 times greater than the homozygous AA genotypes for SNP11965 and SNP6347 loci, respectively. Genomic selection for fusiform rust disease incidence could be effective in P. taeda breeding. Markers with large effects could be fit as fixed covariates to increase the prediction accuracies, provided that their effects are validated further.

Performance of Livingston Parish Loblolly Pine in the Georgia Piedmont

1986 ◽  
Vol 10 (2) ◽  
pp. 84-87
Author(s):  
H. R. Powers
Keyword(s):  
Pinus Taeda ◽  
Loblolly Pine ◽  
Coastal Plain ◽  
Source Material ◽  
Atlantic Coastal Plain ◽  
Rust Disease ◽  
Fusiform Rust ◽  
Pinus Taeda L ◽  
Georgia Piedmont ◽  
Atlantic Coastal

Abstract Seedlings of Livingston Parish (Louisiana) loblolly pine (Pinus taeda L.) have been widely used across the Gulf and south Atlantic Coastal Plain to reduce the damage caused by the fusiform rust disease. Since this seed-source material provided the first rust-resistant seedlings available to forestland managers, it was used wherever rust damage was heavy, in some cases into the Piedmont north of the recommended area of planting. This paper evaluates the performance of ten-year-old Livingston Parish trees in such an area. The rust resistance of the Livingston Parish trees was outstanding, with 83% being free of disease as compared with only 14% of the commercial controls. There was no difference in growth between the two groups of trees, and ice breakage was not significantly greater in the Livingston Parish trees. South. J. Appl. For. 10:84-87, May 1986.

scholarly journals Potential of genomic selection for improvement of resistance to Ostreid Herpes virus in Pacific oyster (Crassostrea gigas)

2019 ◽  
Author(s):  
Alejandro P. Gutierrez ◽  
Jane Symonds ◽  
Nick King ◽  
Konstanze Steiner ◽  
Tim P. Bean ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Pacific Oyster ◽  
Snp Array ◽  
Cost Effective ◽  
Snp Markers ◽  
Density Reduction ◽  
Pacific Oysters ◽  
Summer Mortality ◽  
Selection For ◽  
Estimated Breeding Values

AbstractIn genomic selection (GS), genome-wide SNP markers are used to generate genomic estimated breeding values (gEBVs) for selection candidates. The application of GS in shellfish looks promising and has the potential to help in dealing with one of the main issues currently affecting Pacific oyster production worldwide, which is the “summer mortality syndrome”. This causes periodic mass mortality in farms worldwide and has mainly been attributed to a specific variant of the Ostreid herpesvirus (OsHV-1-μvar). In the current study, we evaluated the potential of genomic selection for host resistance OsHV in Pacific oysters, and compared it to pedigree-based approaches. An OsHV-1 disease challenge was performed using an immersion-based virus exposure treatment for oysters for seven days. 768 samples were genotyped using the medium density SNP array for oysters. GWAS was performed for the survival trait using a GBLUP approach in BLUPF90 software. Heritability ranged from 0.25±0.05 to 0.37±0.05 (mean±s.e) based on pedigree and genomic information, respectively. Genomic prediction was more accurate than pedigree prediction, and SNP density reduction had little impact on prediction accuracy until marker densities dropped below ∼500 SNPs. This demonstrates the potential for GS in Pacific oyster breeding programs and importantly, demonstrates that a low number of SNPs might suffice to obtain accurate gEBVs, thus potentially making the implementation of GS more cost effective.

scholarly journals LASSO with cross-validation for genomic selection

2009 ◽  
Vol 91 (6) ◽  
pp. 427-436 ◽  
Author(s):  
M. GRAZIANO USAI ◽  
MIKE E. GODDARD ◽  
BEN J. HAYES
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Simulated Data ◽  
Reference Population ◽  
Good Alternative ◽  
Data Sets ◽  
Nucleotide Polymorphisms ◽  
Data Set ◽  
Estimated Breeding Values ◽  
Selection Operator

SummaryWe used a least absolute shrinkage and selection operator (LASSO) approach to estimate marker effects for genomic selection. The least angle regression (LARS) algorithm and cross-validation were used to define the best subset of markers to include in the model. The LASSO–LARS approach was tested on two data sets: a simulated data set with 5865 individuals and 6000 Single Nucleotide Polymorphisms (SNPs); and a mouse data set with 1885 individuals genotyped for 10 656 SNPs and phenotyped for a number of quantitative traits. In the simulated data, three approaches were used to split the reference population into training and validation subsets for cross-validation: random splitting across the whole population; random sampling of validation set from the last generation only, either within or across families. The highest accuracy was obtained by random splitting across the whole population. The accuracy of genomic estimated breeding values (GEBVs) in the candidate population obtained by LASSO–LARS was 0·89 with 156 explanatory SNPs. This value was higher than those obtained by Best Linear Unbiased Prediction (BLUP) and a Bayesian method (BayesA), which were 0·75 and 0·84, respectively. In the mouse data, 1600 individuals were randomly allocated to the reference population. The GEBVs for the remaining 285 individuals estimated by LASSO–LARS were more accurate than those obtained by BLUP and BayesA for weight at six weeks and slightly lower for growth rate and body length. It was concluded that LASSO–LARS approach is a good alternative method to estimate marker effects for genomic selection, particularly when the cost of genotyping can be reduced by using a limited subset of markers.

scholarly journals Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava

2021 ◽  
Author(s):  
Williams Esuma ◽  
Alfred Ozimati ◽  
Peter Kulakow ◽  
Michael A Gore ◽  
Marnin D Wolfe ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Predictive Ability ◽  
West African ◽  
Provitamin A ◽  
Total Carotenoids ◽  
Prediction Ability ◽  
Cassava Brown Streak Disease ◽  
Selection For ◽  
Brown Streak

Abstract Global efforts are underway to develop cassava with enhanced levels of provitamin A carotenoids to sustainably meet increasing demands for food and nutrition where the crop is a major staple. Herein, we tested the effectiveness of genomic selection for rapid improvement of cassava for total carotenoids content and associated traits. We evaluated 632 clones from Uganda’s provitamin A cassava breeding pipeline and 648 West African introductions. At harvest, each clone was assessed for level of total carotenoids, dry matter content and resistance to cassava brown streak disease. All clones were genotyped with diversity array technology and imputed to a set of 23,431 single nucleotide polymorphic markers. We assessed predictive ability of four genomic prediction methods in scenarios of cross-validation, across population prediction and inclusion of quantitative trait loci markers. Cross-validations produced the highest mean prediction ability for total carotenoids content (0.52) and the lowest for cassava brown streak disease resistance (0.20), with G-BLUP outperforming other models tested. Across population predictions showed low ability of Ugandan population to predict the performance of West African clones, with the highest predictive ability recorded for total carotenoids content (0.34) and the lowest for cassava brown streak disease resistance (0.12) using G-BLUP. By incorporating chromosome 1 markers associated with carotenoids content as independent kernel in the G-BLUP model of a cross-validation scenario, prediction ability slightly improved from 0.52 to 0.58. These results reinforce ongoing efforts aimed at integrating genomic selection into cassava breeding and demonstrate the utility of this tool for rapid genetic improvement.

scholarly journals Prediction Accuracies of Genomic Selection for Nine Commercially Important Traits in the Portuguese Oyster (Crassostrea angulata) Using DArT-Seq Technology

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 210
Author(s):  
Sang V. Vu ◽  
Cedric Gondro ◽  
Ngoc T. H. Nguyen ◽  
Arthur R. Gilmour ◽  
Rick Tearle ◽  
...  
Keyword(s):  
Moisture Content ◽  
Genomic Selection ◽  
Shell Length ◽  
Prediction Accuracy ◽  
Genomic Information ◽  
Morphometric Traits ◽  
Shell Weight ◽  
Shell Width ◽  
Crassostrea Angulata ◽  
Selection For

Genomic selection has been widely used in terrestrial animals but has had limited application in aquaculture due to relatively high genotyping costs. Genomic information has an important role in improving the prediction accuracy of breeding values, especially for traits that are difficult or expensive to measure. The purposes of this study were to (i) further evaluate the use of genomic information to improve prediction accuracies of breeding values from, (ii) compare different prediction methods (BayesA, BayesCπ and GBLUP) on prediction accuracies in our field data, and (iii) investigate the effects of different SNP marker densities on prediction accuracies of traits in the Portuguese oyster (Crassostrea angulata). The traits studied are all of economic importance and included morphometric traits (shell length, shell width, shell depth, shell weight), edibility traits (tenderness, taste, moisture content), and disease traits (Polydora sp. and Marteilioides chungmuensis). A total of 18,849 single nucleotide polymorphisms were obtained from genotyping by sequencing and used to estimate genetic parameters (heritability and genetic correlation) and the prediction accuracy of genomic selection for these traits. Multi-locus mixed model analysis indicated high estimates of heritability for edibility traits; 0.44 for moisture content, 0.59 for taste, and 0.72 for tenderness. The morphometric traits, shell length, shell width, shell depth and shell weight had estimated genomic heritabilities ranging from 0.28 to 0.55. The genomic heritabilities were relatively low for the disease related traits: Polydora sp. prevalence (0.11) and M. chungmuensis (0.10). Genomic correlations between whole weight and other morphometric traits were from moderate to high and positive (0.58–0.90). However, unfavourably positive genomic correlations were observed between whole weight and the disease traits (0.35–0.37). The genomic best linear unbiased prediction method (GBLUP) showed slightly higher accuracy for the traits studied (0.240–0.794) compared with both BayesA and BayesCπ methods but these differences were not significant. In addition, there is a large potential for using low-density SNP markers for genomic selection in this population at a number of 3000 SNPs. Therefore, there is the prospect to improve morphometric, edibility and disease related traits using genomic information in this species.

scholarly journals Genomic selection in French dairy cattle

2012 ◽  
Vol 52 (3) ◽  
pp. 115 ◽  
Author(s):  
D. Boichard ◽  
F. Guillaume ◽  
A. Baur ◽  
P. Croiseau ◽  
M. N. Rossignol ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genomic Selection ◽  
Reference Population ◽  
Specific Reference ◽  
Nucleotide Polymorphisms ◽  
Genomic Evaluation ◽  
Linear Unbiased Prediction ◽  
Best Linear Unbiased ◽  
Estimated Breeding Values ◽  
Restricted Use

Genomic selection is implemented in French Holstein, Montbéliarde, and Normande breeds (70%, 16% and 12% of French dairy cows). A characteristic of the model for genomic evaluation is the use of haplotypes instead of single-nucleotide polymorphisms (SNPs), so as to maximise linkage disequilibrium between markers and quantitative trait loci (QTLs). For each trait, a QTL-BLUP model (i.e. a best linear unbiased prediction model including QTL random effects) includes 300–700 trait-dependent chromosomal regions selected either by linkage disequilibrium and linkage analysis or by elastic net. This model requires an important effort to phase genotypes, detect QTLs, select SNPs, but was found to be the most efficient one among all tested ones. QTLs are defined within breed and many of them were found to be breed specific. Reference populations include 1800 and 1400 bulls in Montbéliarde and Normande breeds. In Holstein, the very large reference population of 18 300 bulls originates from the EuroGenomics consortium. Since 2008, ~65 000 animals have been genotyped for selection by Labogena with the 50k chip. Bulls genomic estimated breeding values (GEBVs) were made official in June 2009. In 2010, the market share of the young bulls reached 30% and is expected to increase rapidly. Advertising actions have been undertaken to recommend a time-restricted use of young bulls with a limited number of doses. In January 2011, genomic selection was opened to all farmers for females. Current developments focus on the extension of the method to a multi-breed context, to use all reference populations simultaneously in genomic evaluation.

scholarly journals Differential Expression of Pine and Cronartium quercuum f. sp. fusiforme Genes in Fusiform Rust Galls

2004 ◽  
Vol 70 (1) ◽  
pp. 441-451 ◽  
Author(s):  
Jaimie M. Warren ◽  
Sarah F. Covert
Keyword(s):  
Stress Responses ◽  
Axenic Culture ◽  
The United States ◽  
Northern Analysis ◽  
Pcr Analysis ◽  
Rust Disease ◽  
Fusiform Rust ◽  
Expression Levels ◽  
Cronartium Quercuum ◽  
Gene Expression Levels

ABSTRACT Cronartium quercuum f. sp. fusiforme is the causative agent of fusiform rust disease of southern pines in the United States. This disease is characterized by the formation of woody branch and stem galls. Differential display was used to identify pine genes whose expression is altered by C. quercuum f. sp. fusiforme infection and to identify C. quercuum f. sp. fusiforme genes that are expressed in fusiform rust galls. Six pine cDNAs that appeared to be differentially expressed in galled and healthy stems and 13 C. quercuum f. sp. fusiforme cDNAs expressed in galled tissues were identified. A probe that hybridizes specifically to C. quercuum f. sp. fusiforme 18S rRNA was used to estimate that 14% of the total RNA in fusiform rust galls was from C. quercuum f. sp. fusiforme. This finding was used to calibrate gene expression levels in galls when comparing them to expression levels in uninfected pines or in isolated C. quercuum f. sp. fusiforme cultures. According to Northern analysis and reverse transcriptase PCR analysis, all six of the pine clones were expressed at lower levels in galls than in healthy tissues. Seven of the nine C. quercuum f. sp. fusiforme clones that were assayed were expressed at higher levels in galls than in axenic culture. A number of the cDNAs encode proteins that are similar to those that play roles in plant development, plant defense, or fungal stress responses.

Genomic Selection for the Traits Expressed after Pollination in Allogamous Plants

Crop Science ◽  
2014 ◽  
Vol 54 (4) ◽  
pp. 1448-1457 ◽  
Author(s):  
Shiori Yabe ◽  
Ryo Ohsawa ◽  
Hiroyoshi Iwata
Keyword(s):  
Genomic Selection ◽  
Selection For

scholarly journals Families of loblolly pine that are the most stable for resistance to fusiform rust are the least predictable

2003 ◽  
Vol 33 (7) ◽  
pp. 1335-1339 ◽  
Author(s):  
S E McKeand ◽  
H V Amerson ◽  
B Li ◽  
T J Mullin
Keyword(s):  
Loblolly Pine ◽  
Coefficient Of Determination ◽  
Rust Disease ◽  
Infection Level ◽  
Fusiform Rust ◽  
Rust Infection ◽  
Average Coefficient ◽  
Pinus Taeda L ◽  
The Individual ◽  
Pathogen Populations

In an extensive series of trials with open-pollinated families of loblolly pine (Pinus taeda L.), resistance to fusiform rust disease (caused by Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme) at individual test sites was relatively unpredictable for the families deemed most resistant. The most resistant families were also the most stable for performance across test sites, with stability defined as the slope of the regression of family means for rust infection versus site means for rust infection. A family's R-50 value (its predicted rust infection level when the site mean infection is 50%) was correlated to its stability parameter or slope (r = 0.78). On average, any one family's level of infection (% galled) was reasonably predictable for any given infection level at a given site; the average coefficient of determination (r2) was 0.78 for the regression of family means for rust infection versus site means for rust infection. However, the six most stable families for resistance had the lowest r2 values (average r2 = 0.58). We speculated that the lower predictability for the most resistant families was due to interactions of specific resistance genes in these families and corresponding avirulence and (or) virulence levels in the pathogen populations that may differ among sites. Although the predictability of the individual resistant families was relatively low, if these families were bulked into a resistant seed lot, they performed in a more predictable manner with r2 = 0.74 for the regression of the bulk mean versus site means. Bulks of four to six highly resistant families appeared to be a good solution to obtain stable and predictable performance across a range of sites.

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