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

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.

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Metabolomic Spectra For Phenotypic Prediction of Malting Quality In Spring Barley

10.21203/rs.3.rs-1113863/v1 ◽

2021 ◽

Author(s):

Xiangyu Guo ◽

Ahmed Jahoor ◽

Just Jensen ◽

Pernille Sarup

Keyword(s):

Prediction Accuracy ◽

Cross Validation ◽

Spring Barley ◽

Predictive Ability ◽

Malting Quality ◽

Least Squares Regression ◽

Training Population ◽

Prediction Ability ◽

Rate Of Increase ◽

Best Linear Unbiased

Abstract The objectives were to investigate prediction of malting quality (MQ) phenotypes in different locations using information from metabolomic spectra, and compare the prediction ability using different models and different sizes of training population (TP). A total of 2,667 plots of 564 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each individual plot. Metabolomic features (MFs) used were 24,018 NMR intensities measured on each wort sample. Models involved in the statistical analyses were a metabolomic best linear unbiased prediction (MBLUP) model and a partial least squares regression (PLSR) model. Predictive ability within location and across locations were compared using cross-validation methods. The proportion of variance in MQ traits that could be explained by effects of MFs was above 0.9 for all traits. The prediction accuracy increased with increasing TP size but when the TP size reached 1,000, the rate of increase was negligible. The number of components considered in the PLSR models can affect the performance of PLSR models and 20 components were optimal. The accuracy of individual plots and line means using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation ranged from 0.517 to 0.817.In conclusion, it is possible to carry out metabolomic prediction of MQ traits using MFs, the prediction accuracy is high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

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Integration of Dominance and Marker×Environment Interactions into Maize Genomic Prediction Models

10.1101/362608 ◽

2018 ◽

Author(s):

Luis Felipe Ventorim Ferrão ◽

Caillet Dornelles Marinho ◽

Patricio R. Munoz ◽

Marcio F. R. Resende

Keyword(s):

Genomic Selection ◽

Prediction Models ◽

Predictive Ability ◽

Additive Models ◽

Hybrid Breeding ◽

Maize Breeding ◽

Prediction Ability ◽

Heterotic Groups ◽

Breeding Programs ◽

Grain Moisture

AbstractHybrid breeding programs are driven by the potential to explore the heterosis phenomenon in traits with non-additive inheritance. Traditionally, progress has been achieved by crossing lines from different heterotic groups and measuring phenotypic performance of hybrids in multiple environment trials. With the reduction in genotyping prices, genomic selection has become a reality for phenotype prediction and a promising tool to predict hybrid performances. However, its prediction ability is directly associated with models that represent the trait and breeding scheme under investigation. Herein, we assess modelling approaches where dominance effects and multi-environment statistical are considered for genomic selection in maize hybrid. To this end, we evaluated the predictive ability of grain yield and grain moisture collected over three production cycles in different locations. Hybrid genotypes were inferredin silicobased on their parental inbred lines using single-nucleotide polymorphism markers obtained via a 500k SNP chip. We considered the importance to decomposes additive and dominance marker effects into components that are constant across environments and deviations that are group-specific. Prediction within and across environments were tested. The incorporation of dominance effect increased the predictive ability for grain production by up to 30% in some scenarios. Contrastingly, additive models yielded better results for grain moisture. For multi-environment modelling, the inclusion of interaction effects increased the predictive ability overall. More generally, we demonstrate that including dominance and genotype by environment interactions resulted in gains in accuracy and hence could be considered for genomic selection implementation in maize breeding programs.

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

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab235 ◽

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.

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Training Population Optimization for Prediction of Cassava Brown Streak Disease Resistance in West African Clones

G3 Genes|Genome|Genetics ◽

10.1534/g3.118.200710 ◽

2018 ◽

pp. g3.200710.2018 ◽

Cited By ~ 4

Author(s):

Alfred Ozimati ◽

Robert Kawuki ◽

Williams Esuma ◽

Ismail Siraj Kayondo ◽

Marnin Wolfe ◽

...

Keyword(s):

Disease Resistance ◽

West African ◽

Training Population ◽

Cassava Brown Streak Disease ◽

Brown Streak

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Genomic selection for resistance to Francisellosis in commercial Nile tilapia population: Genetic and genomic parameters, correlation with growth rate and predictive ability

Aquaculture ◽

10.1016/j.aquaculture.2021.736515 ◽

2021 ◽

Vol 537 ◽

pp. 736515 ◽

Cited By ~ 1

Author(s):

Rajesh Joshi ◽

Diones Bender Almeida ◽

Arthur Roberto da Costa ◽

Anders Skaarud ◽

Ulisses de Pádua Pereira ◽

...

Keyword(s):

Growth Rate ◽

Genomic Selection ◽

Nile Tilapia ◽

Population Genetic ◽

Predictive Ability ◽

Selection For

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Genomic prediction for commercial traits using univariate and multivariate approaches in Nile tilapia (Oreochromis niloticus)

10.1101/725143 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rajesh Joshi ◽

Anders Skaarud ◽

Mayet de Vera ◽

Alejandro Tola Alvarez ◽

Jørgen Ødegård

Keyword(s):

Genetic Correlation ◽

Genomic Selection ◽

Genomic Prediction ◽

Nile Tilapia ◽

Prediction Accuracy ◽

Cross Validation ◽

Predictive Ability ◽

Breeding Program ◽

Significant Difference ◽

Multivariate Approaches

AbstractBackgroundOver the past three decades, Nile tilapia industry has grown into a significant aquaculture industry spread over 120 tropical and sub-tropical countries around the world accounting for 7.4% of global aquaculture production in 2015. Across species, genomic selection has been shown to increase predictive ability and genetic gain, also extending into aquaculture. Hence, the aim of this paper is to compare the predictive abilities of pedigree- and genomic-based models in univariate and multivariate approaches, with the aim to utilize genomic selection in a Nile tilapia breeding program. A total of 1444 fish were genotyped (48,960 SNP loci) and phenotyped for body weight at harvest (BW), fillet weight (FW) and fillet yield (FY). The pedigree-based analysis utilized a deep pedigree, including 14 generations. Estimated breeding values (EBVs and GEBVs) were obtained with traditional pedigree-based (PBLUP) and genomic (GBLUP) models, using both univariate and multivariate approaches. Prediction accuracy and bias were evaluated using 5 replicates of 10-fold cross-validation with three different cross-validation approaches. Further, impact of these models and approaches on the genetic evaluation was assessed based on the ranking of the selection candidates.ResultsGBLUP univariate models were found to increase the prediction accuracy and reduce bias of prediction compared to other PBLUP and multivariate approaches. Relative to pedigree-based models, prediction accuracy increased by ∼20% for FY, >75% for FW and >43% for BW. GBLUP models caused major re-ranking of the selection candidates, with no significant difference in the ranking due to univariate or multivariate GBLUP approaches. The heritabilities using multivariate GBLUP models for BW, FW and FY were 0.19 ± 0.04, 0.17 ± 0.04 and 0.23 ± 0.04 respectively. BW showed very high genetic correlation with FW (0.96 ± 0.01) and a slightly negative genetic correlation with FY (−0.11 ± 0.15).ConclusionPredictive ability of genomic prediction models is substantially higher than for classical pedigree-based models. Genomic selection is therefore beneficial to the Nile tilapia breeding program, and it is recommended in routine genetic evaluations of commercial traits in the Nile tilapia breeding nucleus.

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

Genes ◽

10.3390/genes12020210 ◽

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.

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Differential Tropism in Roots and Shoots of Resistant and Susceptible Cassava (Manihot esculenta Crantz) Infected by Cassava Brown Streak Viruses

Cells ◽

10.3390/cells10051221 ◽

2021 ◽

Vol 10 (5) ◽

pp. 1221

Author(s):

Samar Sheat ◽

Paolo Margaria ◽

Stephan Winter

Keyword(s):

Virus Replication ◽

Central Africa ◽

Streak Virus ◽

Virus Infections ◽

Long Distance ◽

Manihot Esculenta Crantz ◽

Cassava Brown Streak Virus ◽

Cassava Brown Streak Disease ◽

Long Distance Movement ◽

Brown Streak

Cassava brown streak disease (CBSD) is a destructive disease of cassava in Eastern and Central Africa. Because there was no source of resistance in African varieties to provide complete protection against the viruses causing the disease, we searched in South American germplasm and identified cassava lines that did not become infected with the cassava brown streak viruses. These findings motivated further investigations into the mechanism of virus resistance. We used RNAscope® in situ hybridization to localize cassava brown streak virus in cassava germplasm lines that were highly resistant (DSC 167, immune) or that restricted virus infections to stems and roots only (DSC 260). We show that the resistance in those lines is not a restriction of long-distance movement but due to preventing virus unloading from the phloem into parenchyma cells for replication, thus restricting the virus to the phloem cells only. When DSC 167 and DSC 260 were compared for virus invasion, only a low CBSV signal was found in phloem tissue of DSC 167, indicating that there is no replication in this host, while the presence of intense hybridization signals in the phloem of DSC 260 provided evidence for virus replication in companion cells. In neither of the two lines studied was there evidence of virus replication outside the phloem tissues. Thus, we conclude that in resistant cassava lines, CBSV is confined to the phloem tissues only, in which virus replication can still take place or is arrested.

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Field evaluation of selected cassava genotypes for cassava brown streak disease based on symptom expression and virus load

Virology Journal ◽

10.1186/s12985-014-0216-x ◽

2014 ◽

Vol 11 (1) ◽

Cited By ~ 46

Author(s):

Tadeo Kaweesi ◽

Robert Kawuki ◽

Vincent Kyaligonza ◽

Yona Baguma ◽

Geoffrey Tusiime ◽

...

Keyword(s):

Field Evaluation ◽

Symptom Expression ◽

Virus Load ◽

Cassava Brown Streak Disease ◽

Brown Streak

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QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index

Journal of the Serbian Chemical Society ◽

10.2298/jsc130611152j ◽

2014 ◽

Vol 79 (8) ◽

pp. 965-975 ◽

Cited By ~ 3

Author(s):

Long Jiao ◽

Xiaofei Wang ◽

LI. Hua ◽

Yunxia Wang

Keyword(s):

Partition Coefficient ◽

Cross Validation ◽

External Validation ◽

Quantitative Relationship ◽

Structure Property ◽

Prediction Ability ◽

Vector Index ◽

Artificial Neural Network Ann ◽

Leave One Out ◽

Edge Vector

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

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