scholarly journals PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

2021 ◽  
Vol 12 ◽  
Author(s):  
Johnathon M. Shook ◽  
Daniela Lourenco ◽  
Asheesh K. Singh
Keyword(s):  
Genomic Prediction ◽  
Rapid Identification ◽  
Identity By Descent ◽  
Crop Species ◽  
Breeding Programs ◽  
Parental Allele ◽  
Ancestral Haplotypes ◽  
Available Information ◽  
Chromosome Segments ◽  
Diversity Estimates

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present “Parental Allele Tracing, Recombination Identification, and Optimal predicTion” (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity-by-descent relationships, PATRIOT showed an improvement in GP accuracy by 16.6% relative to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

scholarly journals PATRIOT: A pipeline for tracing identical-by-descent chromosome segments to improve genomic prediction in self-pollinating crop species

2020 ◽  
Author(s):  
Johnathon M. Shook ◽  
Daniela Lourenco ◽  
Asheesh K. Singh
Keyword(s):  
Genomic Prediction ◽  
Rapid Identification ◽  
Crop Species ◽  
Breeding Programs ◽  
Parental Allele ◽  
Ancestral Haplotypes ◽  
Available Information ◽  
Chromosome Segments ◽  
Diversity Estimates ◽  
Genomic Relatedness

ABSTRACTThe lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present ‘Parental Allele Tracing, Recombination Identification, and Optimal predicTion’ (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity by descent, PATRIOT showed an improvement in GP accuracy by 16.6% compared to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

scholarly journals How Population Structure Impacts Genomic Selection Accuracy in Cross-Validation: Implications for Practical Breeding

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian R. Werner ◽  
R. Chris Gaynor ◽  
Gregor Gorjanc ◽  
John M. Hickey ◽  
Tobias Kox ◽  
...  
Keyword(s):  
Family Structure ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Careful Analysis ◽  
Critical Approach ◽  
Crop Species ◽  
Breeding Programs ◽  
Mendelian Sampling

Over the last two decades, the application of genomic selection has been extensively studied in various crop species, and it has become a common practice to report prediction accuracies using cross validation. However, genomic prediction accuracies obtained from random cross validation can be strongly inflated due to population or family structure, a characteristic shared by many breeding populations. An understanding of the effect of population and family structure on prediction accuracy is essential for the successful application of genomic selection in plant breeding programs. The objective of this study was to make this effect and its implications for practical breeding programs comprehensible for breeders and scientists with a limited background in quantitative genetics and genomic selection theory. We, therefore, compared genomic prediction accuracies obtained from different random cross validation approaches and within-family prediction in three different prediction scenarios. We used a highly structured population of 940 Brassica napus hybrids coming from 46 testcross families and two subpopulations. Our demonstrations show how genomic prediction accuracies obtained from among-family predictions in random cross validation and within-family predictions capture different measures of prediction accuracy. While among-family prediction accuracy measures prediction accuracy of both the parent average component and the Mendelian sampling term, within-family prediction only measures how accurately the Mendelian sampling term can be predicted. With this paper we aim to foster a critical approach to different measures of genomic prediction accuracy and a careful analysis of values observed in genomic selection experiments and reported in literature.

scholarly journals Combining metabolomic and transcriptomic approaches to assess and improve crop quality traits

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Delphine M. Pott ◽  
Sara Durán-Soria ◽  
Sonia Osorio ◽  
José G. Vallarino
Keyword(s):  
Quality Trait ◽  
Quality Traits ◽  
Crop Species ◽  
Breeding Programs ◽  
Crop Quality ◽  
Crop Varieties ◽  
New Genes ◽  
Technical Advances ◽  
Health Promoting ◽  
Selection Of

AbstractPlant quality trait improvement has become a global necessity due to the world overpopulation. In particular, producing crop species with enhanced nutrients and health-promoting compounds is one of the main aims of current breeding programs. However, breeders traditionally focused on characteristics such as yield or pest resistance, while breeding for crop quality, which largely depends on the presence and accumulation of highly valuable metabolites in the plant edible parts, was left out due to the complexity of plant metabolome and the impossibility to properly phenotype it. Recent technical advances in high throughput metabolomic, transcriptomic and genomic platforms have provided efficient approaches to identify new genes and pathways responsible for the extremely diverse plant metabolome. In addition, they allow to establish correlation between genotype and metabolite composition, and to clarify the genetic architecture of complex biochemical pathways, such as the accumulation of secondary metabolites in plants, many of them being highly valuable for the human diet. In this review, we focus on how the combination of metabolomic, transcriptomic and genomic approaches is a useful tool for the selection of crop varieties with improved nutritional value and quality traits.

scholarly journals Wheat, Barley, and Oat Breeding for Health Benefit Components in Grain

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 86
Author(s):  
Igor G. Loskutov ◽  
Elena K. Khlestkina
Keyword(s):  
Bioactive Compounds ◽  
Plant Genetic Resources ◽  
Health Benefit ◽  
Cultivated Plants ◽  
Crop Species ◽  
Breeding Programs ◽  
Cereal Grain ◽  
Staple Foods ◽  
Functional Components ◽  
Oat Breeding

Cereal grains provide half of the calories consumed by humans. In addition, they contain important compounds beneficial for health. During the last years, a broad spectrum of new cereal grain-derived products for dietary purposes emerged on the global food market. Special breeding programs aimed at cultivars utilizable for these new products have been launched for both the main sources of staple foods (such as rice, wheat, and maize) and other cereal crops (oat, barley, sorghum, millet, etc.). The breeding paradigm has been switched from traditional grain quality indicators (for example, high breadmaking quality and protein content for common wheat or content of protein, lysine, and starch for barley and oat) to more specialized ones (high content of bioactive compounds, vitamins, dietary fibers, and oils, etc.). To enrich cereal grain with functional components while growing plants in contrast to the post-harvesting improvement of staple foods with natural and synthetic additives, the new breeding programs need a source of genes for the improvement of the content of health benefit components in grain. The current review aims to consider current trends and achievements in wheat, barley, and oat breeding for health-benefiting components. The sources of these valuable genes are plant genetic resources deposited in genebanks: landraces, rare crop species, or even wild relatives of cultivated plants. Traditional plant breeding approaches supplemented with marker-assisted selection and genetic editing, as well as high-throughput chemotyping techniques, are exploited to speed up the breeding for the desired genotуpes. Biochemical and genetic bases for the enrichment of the grain of modern cereal crop cultivars with micronutrients, oils, phenolics, and other compounds are discussed, and certain cases of contributions to special health-improving diets are summarized. Correlations between the content of certain bioactive compounds and the resistance to diseases or tolerance to certain abiotic stressors suggest that breeding programs aimed at raising the levels of health-benefiting components in cereal grain might at the same time match the task of developing cultivars adapted to unfavorable environmental conditions.

scholarly journals PIF4-controlled auxin pathway contributes to hybrid vigor in Arabidopsis thaliana

2017 ◽  
Vol 114 (17) ◽  
pp. E3555-E3562 ◽  
Author(s):  
Li Wang ◽  
Li Min Wu ◽  
Ian K. Greaves ◽  
Anyu Zhu ◽  
Elizabeth S. Dennis ◽  
...  
Keyword(s):  
Recurrent Selection ◽  
Leaf Growth ◽  
Hybrid Vigor ◽  
Auxin Biosynthesis ◽  
F1 Hybrids ◽  
Auxin Signaling ◽  
Crop Species ◽  
Altered Expression ◽  
F2 Generation ◽  
Chromosome Segments

F1 hybrids in Arabidopsis and crop species are uniform and high yielding. The F2 generation loses much of the yield advantage and the plants have heterogeneous phenotypes. We generated pure breeding hybrid mimic lines by recurrent selection and also selected a pure breeding small phenotype line. The hybrid mimics are almost completely homozygous with chromosome segments from each parent. Four particular chromosomal segments from C24 and 8 from Ler were present in all of the hybrid mimic lines, whereas in the F6 small phenotype line, the 12 segments were each derived from the alternative parent. Loci critical for promoting hybrid vigor may be contained in each of these 12 conserved segments. We have identified genes with similar altered expression in hybrid mimics and F1 plants but not in the small phenotype line. These genes may be critical for the generation of hybrid vigor. Analysis of transcriptomes indicated that increased expression of the transcription factor PHYTOCHROME-INTERACTING FACTOR (PIF4) may contribute to hybrid vigor by targeting the auxin biosynthesis gene YUCCA8 and the auxin signaling gene IAA29. A number of auxin responsive genes promoting leaf growth were up-regulated in the F1 hybrids and hybrid mimics, suggesting that increased auxin biosynthesis and signaling contribute to the hybrid phenotype. The hybrid mimic seeds had earlier germination as did the seeds of the F1 hybrids, indicating cosegregation of the genes for rosette size and the germination trait. Early germination may be an indicator of vigorous hybrids.

scholarly journals Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 669 ◽  
Author(s):  
Peter S. Kristensen ◽  
Just Jensen ◽  
Jeppe R. Andersen ◽  
Carlos Guzmán ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Breeding Programs ◽  
Genome Wide ◽  
Flour Yield

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

scholarly journals Single-plant GWAS coupled with bulk segregant analysis allows rapid identification and corroboration of plant-height candidate SNPs

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Abiskar Gyawali ◽  
Vivek Shrestha ◽  
Katherine E. Guill ◽  
Sherry Flint-Garcia ◽  
Timothy M. Beissinger
Keyword(s):  
Plant Height ◽  
Bulk Segregant Analysis ◽  
Association Studies ◽  
Significant Snps ◽  
Rapid Identification ◽  
Candidate Snps ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Plant ◽  
Crop Species

Abstract Background Genome wide association studies (GWAS) are a powerful tool for identifying quantitative trait loci (QTL) and causal single nucleotide polymorphisms (SNPs)/genes associated with various important traits in crop species. Typically, GWAS in crops are performed using a panel of inbred lines, where multiple replicates of the same inbred are measured and the average phenotype is taken as the response variable. Here we describe and evaluate single plant GWAS (sp-GWAS) for performing a GWAS on individual plants, which does not require an association panel of inbreds. Instead sp-GWAS relies on the phenotypes and genotypes from individual plants sampled from a randomly mating population. Importantly, we demonstrate how sp-GWAS can be efficiently combined with a bulk segregant analysis (BSA) experiment to rapidly corroborate evidence for significant SNPs. Results In this study we used the Shoepeg maize landrace, collected as an open pollinating variety from a farm in Southern Missouri in the 1960’s, to evaluate whether sp-GWAS coupled with BSA can efficiently and powerfully used to detect significant association of SNPs for plant height (PH). Plant were grown in 8 locations across two years and in total 768 individuals were genotyped and phenotyped for sp-GWAS. A total of 306 k polymorphic markers in 768 individuals evaluated via association analysis detected 25 significant SNPs (P ≤ 0.00001) for PH. The results from our single-plant GWAS were further validated by bulk segregant analysis (BSA) for PH. BSA sequencing was performed on the same population by selecting tall and short plants as separate bulks. This approach identified 37 genomic regions for plant height. Of the 25 significant SNPs from GWAS, the three most significant SNPs co-localize with regions identified by BSA. Conclusion Overall, this study demonstrates that sp-GWAS coupled with BSA can be a useful tool for detecting significant SNPs and identifying candidate genes. This result is particularly useful for species/populations where association panels are not readily available.

scholarly journals Genomic prediction unifies animal and plant breeding programs to form platforms for biological discovery

2017 ◽  
Vol 49 (9) ◽  
pp. 1297-1303 ◽  
Author(s):  
John M Hickey ◽  
◽  
Tinashe Chiurugwi ◽  
Ian Mackay ◽  
Wayne Powell
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Breeding Programs ◽  
Biological Discovery

scholarly journals Use of F2 bulks in training sets for genomic prediction of combining ability and hybrid performance

2018 ◽  
Author(s):  
Frank Technow
Keyword(s):  
Genomic Prediction ◽  
Combining Ability ◽  
Hybrid Breeding ◽  
F1 Hybrids ◽  
Hybrid Performance ◽  
Crop Species ◽  
Heterotic Groups ◽  
Quantitative Genetic ◽  
Hand Pollination ◽  
Training Sets

ABSTRACTDeveloping training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species (e.g., wheat, rice, rapeseed, cotton) this requires elaborate hybridization systems to prevent self-pollination and presents a significant impediment to the implementation of hybrid breeding in general and genomic selection in particular. An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.

scholarly journals Generalizable approaches for genomic prediction of metabolites in plants

2021 ◽  
Author(s):  
Lauren J Brzozowski ◽  
Malachy T Campbell ◽  
Haixiao Hu ◽  
Melanie Caffe ◽  
Lucia Guterrez ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Group Model ◽  
Plant Metabolites ◽  
Plant Populations ◽  
Breeding Programs ◽  
Breeding Lines ◽  
Consistent Model ◽  
Selection For

Plant metabolites are important for plant breeders to improve nutrition and agronomic performance, yet integrating selection for metabolomic traits is limited by phenotyping expense and limited genetic characterization, especially of uncommon metabolites. As such, developing biologically-based and generalizable genomic selection methods for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for more than 600 metabolites measured by GC-MS and LC-MS in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite GWAS (mGWAS) and selected loci to use in multi-kernel models that encompassed metabolome-wide mGWAS results, or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel, consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We tested if similar metabolites had consistent model ranks and found that, while different metrics of similarity had different results, using annotation-free methods to group metabolites led to consistent within-group model rankings. Overall, testing biological rationales for developing kernels for genomic prediction across populations, contributes to developing frameworks for plant breeding for metabolite traits.

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