Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers

Santos, José Fortunato Ferreira; Pires, Luíz Paulo Miranda; Vasconcellos, Renato Coelho de Castro; Pereira, Gabriela Santos; Pinho, Renzo Garcia Von; Balestre, Márcio

doi:10.1186/s12863-016-0392-3

Cited by 49 publications

(30 citation statements)

References 38 publications

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“…In maize, prediction accuracy of GS among the full-sibs was more accurate than unrelated crosses (Riedelsheimer et al, 2013). Among the GS models, rrBLUP and BSSV were found equally efficient in identifying the Stenocarpella maydis resistant maize inbred lines using DArTseq markers (Pedroso et al, 2016). …”

Section: Introductionmentioning

confidence: 99%

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

et al. 2017

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Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

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Section: Introductionmentioning

confidence: 99%

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

et al. 2017

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“…The technique can detect both SNPs and DArTs using cost-effective and efficient strategies (RAMAN et al, 2014). Due to its rapid, high throughput and cost-effective characters, DArT-seq has been widely used for genetic diversity studies, linkage mapping, QTL identification in biparental mapping population and genome wide association studies (GWAS) in wheat (LI et al, 2015;BALOCH et al, 2017;KAUR et al, 2017) and many other crops over the previous four years (COURTOIS et al, 2013;SANTOS et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Population structure of Chinese southwest wheat germplasms resistant to stripe rust and powdery mildew using the DArT-seq technique

et al. 2018

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Understanding genetic variability in existing wheat accessions is critical for collection, conservation and use of wheat germplasms. In this study, 138 Chinese southwest wheat accessions were investigated by genotyping using two resistance gene makers (Pm21 and Yr26) and DArT-seq technique. Finally, about 50% cultivars (lines) amplified the specific allele for the Yr26 gene (Gwm11) and 40.6% for the Pm21 gene (SCAR1265). By DArT-seq analysis, 30,485 markers (6486 SNPs and 23999 DArTs) were obtained with mean polymorphic information content (PIC) value 0.33 and 0.28 for DArT and SNP marker, respectively. The mean Dice genetic similarity coefficient (GS) was 0.72. Two consistent groups of wheat varieties were identified using principal coordinate analysis (PCoA) at the level of both the chromosome 6AS and the whole-genome, respectively. Group I was composed of non-6VS/6AL translocation lines of different origins, while Group II was composed of 6VS/6AL translocation (T6VS/6AL) lines, most of which carried the Yr26 and Pm21 genes and originated from Guizhou. Besides, a model-based population structure analysis revealed extensive admixture and further divided these wheat accessions into six subgroups (SG1, SG2, SG3, SG4, SG5 and SG6), based on their origin, pedigree or disease resistance. This information is useful for wheat breeding in southwestern China and association mapping for disease resistance using these wheat germplasms in future.

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“…Mixed linear models, hierarchical Bayesian models with informative priors, kernel methods, and neural nets are modeling approaches used for GP, but minimal differences in predictive performance are typically seen across these approaches (de los Campos et al 2013;Heslot et al 2015;dos Santos et al 2016a). This outcome may be explained by the high density of covariates (SNP markers) compared to the population size used for training the models.…”

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confidence: 99%

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

Santos

Fernandes²,

McCoy

et al. 2020

G3 Genes|Genomes|Genetics

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The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4–52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

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Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers

Cited by 49 publications

References 38 publications

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Population structure of Chinese southwest wheat germplasms resistant to stripe rust and powdery mildew using the DArT-seq technique

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

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