Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs

Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.

Download Full-text

The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs

Genetics Selection Evolution ◽

10.1186/s12711-020-00576-0 ◽

2020 ◽

Vol 52 (1) ◽

Cited By ~ 1

Author(s):

Amir Aliakbari ◽

Emilie Delpuech ◽

Yann Labrune ◽

Juliette Riquet ◽

Hélène Gilbert

Keyword(s):

Feed Intake ◽

Genomic Prediction ◽

Feed Efficiency ◽

Prediction Accuracy ◽

Target Line ◽

The Other ◽

Training Set ◽

The Impact ◽

Genomic Predictions ◽

Training Sets

Abstract Background Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Results Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Conclusions Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

Download Full-text

Perspectives on Applications of Hierarchical Gene-To-Phenotype (G2P) Maps to Capture Non-stationary Effects of Alleles in Genomic Prediction

Frontiers in Plant Science ◽

10.3389/fpls.2021.663565 ◽

2021 ◽

Vol 12 ◽

Author(s):

Owen M. Powell ◽

Kai P. Voss-Fels ◽

David R. Jordan ◽

Graeme Hammer ◽

Mark Cooper

Keyword(s):

Plant Breeding ◽

Genomic Prediction ◽

Complex Traits ◽

Prediction Accuracy ◽

Predictive Ability ◽

Complex Trait ◽

Substitution Effects ◽

Term Prediction ◽

Gxe Interactions

Genomic prediction of complex traits across environments, breeding cycles, and populations remains a challenge for plant breeding. A potential explanation for this is that underlying non-additive genetic (GxG) and genotype-by-environment (GxE) interactions generate allele substitution effects that are non-stationary across different contexts. Such non-stationary effects of alleles are either ignored or assumed to be implicitly captured by most gene-to-phenotype (G2P) maps used in genomic prediction. The implicit capture of non-stationary effects of alleles requires the G2P map to be re-estimated across different contexts. We discuss the development and application of hierarchical G2P maps that explicitly capture non-stationary effects of alleles and have successfully increased short-term prediction accuracy in plant breeding. These hierarchical G2P maps achieve increases in prediction accuracy by allowing intermediate processes such as other traits and environmental factors and their interactions to contribute to complex trait variation. However, long-term prediction remains a challenge. The plant breeding community should undertake complementary simulation and empirical experiments to interrogate various hierarchical G2P maps that connect GxG and GxE interactions simultaneously. The existing genetic correlation framework can be used to assess the magnitude of non-stationary effects of alleles and the predictive ability of these hierarchical G2P maps in long-term, multi-context genomic predictions of complex traits in plant breeding.

Download Full-text

Optimizing genomic prediction of host resistance to koi herpesvirus disease in carp

10.1101/609784 ◽

2019 ◽

Author(s):

Christos Palaiokostas ◽

Tomas Vesely ◽

Martin Kocour ◽

Martin Prchal ◽

Dagmar Pokorova ◽

...

Keyword(s):

Genomic Selection ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Genetic Relationships ◽

Animal Health ◽

Economic Losses ◽

The European Union ◽

Koi Herpesvirus ◽

Wide Range ◽

Close Relatives

AbstractGenomic selection (GS) is increasingly applied in breeding programmes of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organisation for Animal Health and the European Union, causing major economic losses to carp production. Genomic selection has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. Genomic selection (GS) was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using genomic selection was between 8 and 18 % higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives.

Download Full-text

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Heredity ◽

10.1038/s41437-021-00474-1 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Yoseph Beyene ◽

Manje Gowda ◽

Jose Crossa ◽

Paulino Pérez-Rodríguez ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Prediction Models ◽

Selection Index ◽

Maize Yield ◽

Additive Models ◽

Training Data ◽

Training Set ◽

Gaussian Kernels ◽

Non Parametric

AbstractGenomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.

Download Full-text

The effects of training population design on genomic prediction accuracy in wheat

10.1101/443267 ◽

2018 ◽

Cited By ~ 1

Author(s):

Stefan McKinnon Edwards ◽

Jaap B. Buntjer ◽

Robert Jackson ◽

Alison R. Bentley ◽

Jacob Lage ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

Genetic Material ◽

Breeding Value ◽

Training Set ◽

Or Efficiency ◽

Genomic Breeding Value ◽

Close Relatives ◽

Training Sets

AbstractGenomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeder’s equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.To quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 – 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

Download Full-text

Genotyping-by-Sequencing to Unlock Genetic Diversity and Population Structure in White Yam (Dioscorea rotundata Poir.)

Agronomy ◽

10.3390/agronomy10091437 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1437

Author(s):

Ranjana Bhattacharjee ◽

Paterne Agre ◽

Guillaume Bauchet ◽

David De Koeyer ◽

Antonio Lopez-Montes ◽

...

Keyword(s):

Genetic Diversity ◽

Population Structure ◽

Phylogenetic Tree ◽

Genetic Relationships ◽

Genotyping By Sequencing ◽

Optimum Number ◽

Admixture Analysis ◽

Clustering Methods ◽

Breeding Lines ◽

White Yam

White yam (Dioscorearotundata Poir.) is one of the most important tuber crops in West Africa, where it is indigenous and represents the largest repository of biodiversity through several years of domestication, production, consumption, and trade. In this study, the genotyping-by-sequencing (GBS) approach was used to sequence 814 genotypes consisting of genebank landraces, breeding lines, and market varieties to understand the level of genetic diversity and pattern of the population structure among them. The genetic diversity among different genotypes was assessed using three complementary clustering methods, the model-based admixture, discriminant analysis of principal components (DAPC), and phylogenetic tree. ADMIXTURE analysis revealed an optimum number of four groups that matched with the number of clusters obtained through phylogenetic tree. Clustering results obtained from ADMIXTURE analysis were further validated using DAPC-based clustering. Analysis of molecular variance (AMOVA) revealed high genetic diversity (96%) within each genetic group. A network analysis was further carried out to depict the genetic relationships among the three genetic groups (breeding lines, genebank landraces, and market varieties) used in the study. This study showed that the use of advanced sequencing techniques such as GBS coupled with statistical analysis is a robust method for assessing genetic diversity and population structure in a complex crop such as white yam.

Download Full-text

A comparison between genotyping-by-sequencing and array-based scoring of SNPs for genomic prediction accuracy in winter wheat

Plant Science ◽

10.1016/j.plantsci.2018.02.019 ◽

2018 ◽

Vol 270 ◽

pp. 123-130 ◽

Cited By ~ 29

Author(s):

Ibrahim S. Elbasyoni ◽

A.J. Lorenz ◽

M. Guttieri ◽

K. Frels ◽

P.S. Baenziger ◽

...

Keyword(s):

Winter Wheat ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Genotyping By Sequencing

Download Full-text

The Genetic Diversity and Population Structure of Different Geographical Populations of Bottle Gourd (Lagenaria siceraria) Accessions Based on Genotyping-by-Sequencing

Agronomy ◽

10.3390/agronomy11081677 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1677

Author(s):

Rodrigo Contreras-Soto ◽

Ariel Salvatierra ◽

Carlos Maldonado ◽

Jacob Mashilo

Keyword(s):

Genetic Diversity ◽

Population Structure ◽

Genetic Relationships ◽

Genotyping By Sequencing ◽

Bottle Gourd ◽

Conservation Strategies ◽

Lagenaria Siceraria ◽

Genomic Resources ◽

Ssr Loci ◽

Pharmaceutical Industries

Lagenaria siceraria (Molina) Standl is an important horticultural and medicinal crop grown worldwide in the food and pharmaceutical industries. The crop exhibits extensive phenotypic and genetic variation useful for cultivar development targeting economic traits; however, limited genomic resources are available for effective germplasm characterization into breeding and conservation strategies. This study determined the genetic relationships and population structure in a collection of different accessions of bottle gourd derived from Chile, Asia, and South Africa by using single-nucleotide polymorphism (SNP) markers and mining of simple sequence repeat (SSR) loci derived from genotyping-by-sequencing (GBS) data. The GBS resulted in 12,766 SNPs classified as moderate to highly informative, with a mean polymorphic information content of 0.29. The mean gene diversity of 0.16 indicated a low genetic differentiation of the accessions. Analysis of molecular variance revealed less differentiation between (36%) as compared to within (48%) bottle gourd accessions, suggesting that a random mating system dominates inbreeding. Population structure revealed two genetically differentiated groups comprising South African accessions and an admixed group with accessions of Asian and Chilean origin. The results of SSR loci mining from GBS data should be developed and validated before being used in diverse bottle gourd accessions. The SNPs markers developed in the present study are useful genomic resources in bottle gourd breeding programs for assessing the extent of genetic diversity for effective parental selection and breeding.

Download Full-text

Genome-wide association study and Genomic Prediction of spot blotch disease in wheat (Triticum aestivum L.) using genotyping by sequencing

10.21203/rs.2.22818/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Vipin Tomar ◽

Ravi P Singh ◽

Jesse Poland ◽

Daljit Singh ◽

Arun K Joshi ◽

...

Keyword(s):

Disease Resistance ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Genome Wide Association Study ◽

Genotyping By Sequencing ◽

Spot Blotch ◽

Genomic Region ◽

Genome Wide Association ◽

Genome Wide ◽

Genomic Regions

Abstract Background Spot blotch caused by Bipolaris sorokiniana is a major constraint in wheat production in tropics and subtropics. There is limited information available on GWAS and study on genomic prediction is completely lacking. To reveal the genetic markers associated with disease resistance, we performed a genome-wide association study (GWAS) for spot blotch disease in 141 spring wheat lines. Results Based on the testing under natural infection in three years at hot spots location in Pusa, India and Jamalpur, Bangladesh, the genotypes showed significant genetic variation for disease severity. Using Genotyping-by-Sequencing (GBS) based 18637 polymorphic SNP markers and phenotyping from diverse environments, we identified 23 genomic regions across the genome ( p < 0.001) on 14 chromosomes associated with disease scores. Consistent with the previous reports, a most stable genomic region on chromosome 2B, 5B and 7D were detected across the environments. The new genomic region on chromosome 3D was also identified. We performed functional annotation with wheat genome assembly annotation (IWGSC Ref Seq v1.0) and identified NBS-LRR and 35 other plant defense-related protein families across multiple chromosome regions. Using a five-fold cross-validation scheme, we observed moderate prediction accuracy for 3 of 4 environments indicated that our model was able to successfully capture the quantitative variation underlying the SB variation in our population. Conclusions The GWAS based on the phenotypic data from PUSA India and BARI Bangladesh resulted in a total of 23 genomic regions on 14 chromosomes. The new genomic region appeared on chromosome 3D associated with Zinc finger protein that play important role in plant disease resistance. The genomic prediction model for spot blotch disease resistance in wheat was tested and obtained moderate prediction accuracy.

Download Full-text

Genomic assessment of white clover and perennial ryegrass genetic resources

Journal of New Zealand Grasslands ◽

10.33584/jnzg.2020.82.437 ◽

2020 ◽

Vol 82 ◽

pp. 27-34

Author(s):

Marty J. Faville ◽

Andrew G. Griffiths ◽

Abdul Baten ◽

Mingshu Cao ◽

Rachael Ashby ◽

...

Keyword(s):

Genetic Diversity ◽

Genetic Variation ◽

New Zealand ◽

Iberian Peninsula ◽

Perennial Ryegrass ◽

White Clover ◽

Eastern Mediterranean ◽

Genetic Relationships ◽

Genotyping By Sequencing ◽

Phenotypic Characterisation

Forage resources conserved in genebanks, such as the Margot Forde Germplasm Centre (MFGC; PalmerstonNorth), are reservoirs of genetic diversity important for the development of cultivars adapted to abiotic stresses and environmental constraints. Genomic tools, including genotyping-by-sequencing (GBS), can support identification of manageable subsets (core collections) that are genetically representative of these large germplasm collections, for phenotypic characterisation. We used GBS to generate SNP (single nucleotide polymorphism) profiles for 172 white clover (WC) and 357 perennial ryegrass (PRG) MFGC-sourced accessions and estimated genetic relationships amongst accessions. In WC, accessions aligned along an east-west transect from Kazakhstan to Spain, identifying major diversity in Caucasus/Central Asia and Iberian Peninsula. A key feature was the reduced diversity present in New Zealand (NZL) accessions. Similarly, for PRG, most NZL accessions coalesced as one group, distinct from large clusters associated with the Iberian Peninsula, Italy and eastern Mediterranean/Caucasian region. These results emphasise the relatively narrow genetic diversity in NZL WC and PRG, and the broad extent of largely unexploited global diversity. Capturing global genetic variation incore collections will support pre-breeding programmes to mobilise novel genetic variation into New Zealand-adapted genetic backgrounds, enabling development of cultivars with non-traditional traits including enhancedclimate resilience and environmental performance.

Download Full-text