Genomic Prediction of Rust Resistance in Tetraploid Wheat under Field and Controlled Environment Conditions

Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle.

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Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽

10.3390/plants9060719 ◽

2020 ◽

Vol 9 (6) ◽

pp. 719

Author(s):

Mulusew Fikere ◽

Denise M. Barbulescu ◽

M. Michelle Malmberg ◽

Pankaj Maharjan ◽

Phillip A. Salisbury ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Seed Quality ◽

Agronomic Traits ◽

Genetic Correlations ◽

Quality Traits ◽

Blackleg Disease ◽

Genetic Progress ◽

Seed Quality Traits

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

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Mapping of Quantitative Trait Loci for Leaf Rust Resistance in the Wheat Population Ning7840 × Clark

Plant Disease ◽

10.1094/pdis-12-16-1743-re ◽

2017 ◽

Vol 101 (12) ◽

pp. 1974-1979 ◽

Cited By ~ 9

Author(s):

Chunlian Li ◽

Zhonghua Wang ◽

Chunxin Li ◽

Robert Bowden ◽

Guihua Bai ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Leaf Rust ◽

Quantitative Trait ◽

Rust Resistance ◽

Puccinia Triticina ◽

Leaf Rust Resistance ◽

Adult Plant ◽

Nucleotide Polymorphisms ◽

Trait Loci ◽

Rust Severity

Leaf rust, caused by Puccinia triticina, is an important fungal disease of wheat (Triticum aestivum L.) and causes significant yield losses worldwide. To determine quantitative trait loci (QTLs) responsible for leaf rust resistance, a recombinant inbred line (RIL) population developed from a cross of Ning7840 × Clark was evaluated for leaf rust severity, and was genotyped for single nucleotide polymorphisms (SNPs) using 9K Illumina chips, and with simple sequence repeat (SSR) markers. Two major QTLs on chromosome arms 7DS and 3BS, and two minor QTLs on chromosomes 5AS and 6AS showed a significant effect on leaf rust severity. The 7DS QTL from Ning7840 and the 3BS QTL from Clark explained, respectively, about 35% and 18% of the phenotypic variation for leaf rust resistance. The QTL on 7DS was confirmed to be Lr34. The QTL on 3BS, QLr.hwwg-3B.1, was associated with adult plant resistance and was provisionally identified as Lr74. QLr.hwwg-5AS and QLr.hwwg-6AS from Ning7840 and Clark, respectively, may correspond to previously described QTLs. Lr34, QLr.hwwg-3BS.1, and QLr.hwwg-6AS had an additive effect on leaf rust severity. RILs with all three favorable alleles showed the highest resistance to leaf rust and the RILs with none of them showed the lowest resistance.

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Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

Frontiers in Plant Science ◽

10.3389/fpls.2021.690059 ◽

2021 ◽

Vol 12 ◽

Author(s):

Juan Ma ◽

Yanyong Cao

Keyword(s):

Grain Yield ◽

Genomic Prediction ◽

Fixed Effects ◽

Prediction Accuracy ◽

Reproducing Kernel ◽

Genome Wide Association Study ◽

Kernel Weight ◽

Primary Objective ◽

High Yield ◽

Nucleotide Polymorphisms

High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.

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Genomic prediction for malting quality traits in practical barley breeding programs

10.1101/2020.07.30.228007 ◽

2020 ◽

Cited By ~ 1

Author(s):

Pernille Sarup ◽

Vahid Edriss ◽

Nanna Hellum Kristensen ◽

Jens Due Jensen ◽

Jihad Orabi ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Cross Validation ◽

Spring Barley ◽

Malting Quality ◽

Breeding Cycle ◽

Quality Traits ◽

Training Population ◽

Barley Breeding ◽

Breeding Cycles

AbstractGenomic prediction can be advantageous in barley breeding for traits such as yield and malting quality to increase selection accuracy and minimize expensive phenotyping. In this paper, we investigate the possibilities of genomic selection for malting quality traits using a limited training population. The size of the training population is an important factor in determining the prediction accuracy of a trait. We investigated the potential for genomic prediction of malting quality within breeding cycles with leave one out (LOO) cross-validation, and across breeding cycles with leave set out (LSO) cross-validation. In addition, we investigated the effect of training population size on prediction accuracy by random two, four, and ten-fold cross-validation. The material used in this study was a population of 1329 spring barley lines from four breeding cycles. We found medium to high narrow sense heritabilities of the malting traits (0.31 to 0.65). Accuracies of predicting breeding values from LOO tests ranged from 0.6 to 0.9 making it worth the effort to use genomic prediction within breeding cycles. Accuracies from LSO tests ranged from 0.39 to 0.70 showing that genomic prediction across the breeding cycles were possible as well. Accuracy of prediction increased when the size of the training population increased. Therefore, prediction accuracy might be increased both within and across breeding cycle by increasing size of the training population

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Genetic Dissection of Resistance to Common Rust (Puccinia Sorghi) in Tropical Maize (Zea mays L.) by Combined Genetic Mapping and Genomic Prediction

10.21203/rs.3.rs-126178/v1 ◽

2020 ◽

Author(s):

Xuecai Zhang ◽

Jiaojiao Ren ◽

Zhimin Li ◽

Penghao Wu ◽

Alexander Loladze ◽

...

Keyword(s):

Genomic Prediction ◽

Linkage Mapping ◽

Rust Resistance ◽

Genome Wide Association Study ◽

Genetic Dissection ◽

Nucleotide Polymorphisms ◽

Maize Breeding ◽

Dh Population ◽

Association Panel ◽

Maize Varieties

Abstract Background: Common rust is one of the major foliar diseases of maize, leading to significant grain yield losses and poor grain quality. The most sustainable strategy for controlling common rust is to develop resistant maize varieties, which requires a further understanding of genetic dissection of common rust resistance. Results: In this study, an association panel and two bi-parental doubled haploid (DH) populations were used to perform genome-wide association study (GWAS), linkage mapping, and genomic prediction analyses. All the populations were phenotyped in multi-environment trials for common rust resistance and genotyped with genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). GWAS revealed six SNPs significantly associated with common rust resistance at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04, respectively. The SNP effect of each SNP ranged from 0.13 to 0.17. Linkage mapping identified six quantitative trait loci (QTL) in the first DH population (DH1) and two QTL in the second DH population (DH2), distributed on chromosomes 1, 2, 3, 4, 6, 7, and 9, respectively. The phenotypic variation explained (PVE) of each QTL ranged from 3.55% to 12.45%. A new major QTL was detected in DH1 on chromosome 7 in the region between 144,585,945 and 149,528,489 bp, it had the highest LOD score of 7.82 and the largest PVE value of 12.45%. The genomic regions located at bins 1.05, 1.10, and 4.08 were detected by both GWAS and linkage mapping. GRMZM2G114893 (bin 1.05) and GRMZM2G138949 (bin 4.08) were identified as the putative candidate genes conferring common rust resistance. The genomic prediction accuracies observed in the association panel and two bi-parental DH populations were 0.61, 0.51, and 0.10, respectively. Conclusions: These results provided new insight into the genetic architecture of common rust resistance in maize and a better understanding of the application of genomic prediction for common rust resistance in maize breeding.

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Genomic Prediction of Drought Tolerance during Seedling Stage in Maize using Low-Cost Markers

10.21203/rs.3.rs-1172460/v1 ◽

2021 ◽

Author(s):

Ao Zhang ◽

Shan Chen ◽

Zhenhai Cui ◽

Yubo Liu ◽

Yuan Guan ◽

...

Keyword(s):

Drought Tolerance ◽

Genomic Selection ◽

Genomic Prediction ◽

Northeast China ◽

Prediction Accuracy ◽

Phylogenetic Trees ◽

Predictive Accuracy ◽

Seedling Stage ◽

Breeding Cycle ◽

Maize Breeding

Abstract Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, such traits can be improved by genomic selection (GS), which has become a practical and effective tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought environments in northeast China. adequate genetic variants have been found for genomic selection, they are not stable enough between two years. Similarly, the heritability of the three traits is not stable enough, and the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) are higher than that in 2020 (0.65, 0.53, 0.33) and cross-two-year (0.32, 0.26, 0.33). The current research obtained two kinds of marker sets: the SilicoDArT markers were from DArT-seq, and SNPs were from the GBS and DArT-seq. In total, a number of 11,865 SilicoDArT, 7,837 DArT's SNPs, and 91,003 GBS SNPs were used for analysis after quality control. The results of phylogenetic trees showed that the population was rich in consanguinity. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the 2-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For SPH, the prediction accuracies using SilicoDArT were more than ones using DArT SNP, In some cases, alignment to the reference genome results in a loss to the prediction. The trait with lower heritability can improve the prediction accuracy using filtering of linkage disequilibrium. For the same trait, the prediction accuracy estimated with two types of DArT markers was consistently higher than those estimated with the GBS SNPs under the same genotyping cost. Our results show the prediction accuracy has been improved in some cases of controlling population structure and marker quality, even when the density of the marker is reduced. In the initial maize breeding cycle, Silicodart markers can obtain higher prediction accuracy with a lower cost. However, higher marker density platforms i.e. GBS may play a role in the following breeding cycle for the long term. The natural drought experimental station can reduce the difficulty of phenotypic identification in a water-scarce environment. The accumulation of more yearly data will help to stabilize the heritability and improve predictive accuracy in maize breeding. The experimental design and model for drought resistance also need to be further developed.

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Transfer of leaf and stripe rust resistance genes Lr19 and Yr15 in to a susceptible wheat cultivar HS295

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.79.3.12 ◽

2019 ◽

Vol 79 (3) ◽

Author(s):

Pratima Sharma ◽

Madhu Patial ◽

Dharam Pal ◽

S. C. Bhardwaj ◽

Subodh . Kumar ◽

...

Keyword(s):

Rust Resistance ◽

Ssr Marker ◽

Scar Marker ◽

Adult Plant Resistance ◽

Wheat Cultivar ◽

Stripe Rust Resistance ◽

Adult Plant ◽

Potential Donor ◽

Seedling Resistance ◽

Breeding Lines

The present study was conducted to transfer multiple rust resistance in a popular but rust susceptible wheat cultivar HS295. Selected derivatives WBM3632 and WBM3635 have been developed from a cross, HS295*2/FLW20//HS295*2/ FLW13 using bulk-pedigree method of breeding. Advance line WBM3697 selected from a breeding line WBM3532 was named as HS661. This line was evaluated for seedling resistance to a wide array of rust pathotypes and found to possess resistance to all the three rusts. HS661 was also tested under field conditions and showed adult plant resistance to leaf rust (AC1=0.6), stem rust (ACI=2.7) and strpe rust (AC1=3.8). Among 34 F3 lines, 28 were tested positive for SSR marker Xwmc221 indicating the presence of Lr19/Sr25. Out of 14 selected F4 lines from F3, nine were homozygous positive for Lr19/Sr25. The advanced breeding lines viz., WBM3632 (WBM3697) and WBM3635 were also positive for Lr19/Sr25 with SCAR marker SCS265512. SSR marker Xgwm1 producing 215 bp band in Avst-15, FLW13 and HS661 confirmed the presence of Yr15 . Agronomically, HS661 was comparable with recipient variety HS295 and superior to a standard check HS490 under late sown restricted irrigation production conditions of NHZ. HS661 may serve as a potential donor for creating new usable variability against all the three rusts.

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Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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