scholarly journals Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

2022 ◽  
Vol 12 ◽  
Author(s):  
Philomin Juliana ◽  
Xinyao He ◽  
Felix Marza ◽  
Rabiul Islam ◽  
Babul Anwar ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Fixed Effects ◽  
Prediction Models ◽  
Blast Resistance ◽  
Phenotypic Selection ◽  
Sub Saharan Africa ◽  
Fixed Effects Model ◽  
Diversity Panel ◽  
Wheat Blast

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

scholarly journals A review of deep learning applications for genomic selection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Paulino Pérez-Rodríguez ◽  
José Alberto Barrón-López ◽  
Johannes W. R. Martini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Mixed Model ◽  
Prediction Models ◽  
Genetic Effect ◽  
Training Data ◽  
Additive Genetic Effect ◽  
Main Body

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

scholarly journals Haplotype genomic prediction of phenotypic values based on chromosome distance and gene boundaries using low-coverage sequencing in Duroc pigs

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Cheng Bian ◽  
Dzianis Prakapenka ◽  
Cheng Tan ◽  
Ruifei Yang ◽  
Di Zhu ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Average Daily Gain ◽  
Live Weight ◽  
Feed Conversion ◽  
Muscle Area ◽  
Haplotype Blocks ◽  
Low Coverage

Abstract Background Genomic selection using single nucleotide polymorphism (SNP) markers has been widely used for genetic improvement of livestock, but most current methods of genomic selection are based on SNP models. In this study, we investigated the prediction accuracies of haplotype models based on fixed chromosome distances and gene boundaries compared to those of SNP models for genomic prediction of phenotypic values. We also examined the reasons for the successes and failures of haplotype genomic prediction. Methods We analyzed a swine population of 3195 Duroc boars with records on eight traits: body judging score (BJS), teat number (TN), age (AGW), loin muscle area (LMA), loin muscle depth (LMD) and back fat thickness (BF) at 100 kg live weight, and average daily gain (ADG) and feed conversion rate (FCR) from 30 to100 kg live weight. Ten-fold validation was used to evaluate the prediction accuracy of each SNP model and each multi-allelic haplotype model based on 488,124 autosomal SNPs from low-coverage sequencing. Haplotype blocks were defined using fixed chromosome distances or gene boundaries. Results Compared to the best SNP model, the accuracy of predicting phenotypic values using a haplotype model was greater by 7.4% for BJS, 7.1% for AGW, 6.6% for ADG, 4.9% for FCR, 2.7% for LMA, 1.9% for LMD, 1.4% for BF, and 0.3% for TN. The use of gene-based haplotype blocks resulted in the best prediction accuracy for LMA, LMD, and TN. Compared to estimates of SNP additive heritability, estimates of haplotype epistasis heritability were strongly correlated with the increase in prediction accuracy by haplotype models. The increase in prediction accuracy was largest for BJS, AGW, ADG, and FCR, which also had the largest estimates of haplotype epistasis heritability, 24.4% for BJS, 14.3% for AGW, 14.5% for ADG, and 17.7% for FCR. SNP and haplotype heritability profiles across the genome identified several genes with large genetic contributions to phenotypes: NUDT3 for LMA, LMD and BF, VRTN for TN, COL5A2 for BJS, BSND for ADG, and CARTPT for FCR. Conclusions Haplotype prediction models improved the accuracy for genomic prediction of phenotypes in Duroc pigs. For some traits, the best prediction accuracy was obtained with haplotypes defined using gene regions, which provides evidence that functional genomic information can improve the accuracy of haplotype genomic prediction for certain traits.

scholarly journals Genomic Prediction of Tropical Maize Resistance to Fall Armyworm and Weevils: Genomic Selection Should Focus on Effective Training Set Determination

2020 ◽  
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Training Set ◽  
Maize Weevil ◽  
Maize Resistance ◽  
Sub Saharan

Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and relationship with the breeding set (BS) are optimized beforehand. In this study, PAs for the resistance to fall armyworm (FAW) and maize weevil (MW) in a diverse tropical maize panel composed of 341 double haploid and inbred lines were estimated. Both phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) were predicted using 17 parametric, semi-parametric, and nonparametric algorithms with a 10-fold and 5 repetitions cross-validation strategy. n. For both MW and FAW resistance datasets with an RBTS of 37%, PAs achieved with BLUPs were at least as twice as higher than those realized with BLUEs. The PAs achieved with BLUPs for MW resistance traits: grain weight loss (GWL), adult progeny emergence (AP), and number of affected kernels (AK) varied from 0.66 to 0.82. The PAs were also high for FAW resistance RBTS datasets, varying from 0.694 to 0.714 (for RBTS of 37%) to 0.843 to 0.844 (for RBTS of 85%). The PAs for FAW resistance with PBTS were generally high varying from 0.83 to 0.86, except for one dataset that had PAs ranging from 0.11 to 0.75. GP models showed generally similar predictive abilities for each trait while the TS designation was determinant. There was a highly positive correlation (R=0.92***) between TS size and PAs for the RBTS approach while, for the PBTS, these parameters were highly negatively correlated (R=-0.44***), indicating the importance of the degree of kinship between the TS and the BS with the smallest TS (31%) achieving the highest PAs (0.86). This study paves the way towards the use of GS for maize resistance to insect pests in sub-Saharan Africa.

scholarly journals Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

Agronomy ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 1591
Author(s):  
Sebastian Michel ◽  
Franziska Löschenberger ◽  
Ellen Sparry ◽  
Christian Ametz ◽  
Hermann Bürstmayr
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Wheat Breeding ◽  
Single Step ◽  
Breeding Program ◽  
Wheat Breeding Program ◽  
Record Keeping ◽  
Line Breeding ◽  
Cost Efficient

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

scholarly journals Genomic predictive ability for foliar nutritive traits in perennial ryegrass

2019 ◽  
Author(s):  
Sai Krishna Arojju ◽  
Mingshu Cao ◽  
M. Z. Zulfi Jahufer ◽  
Brent A Barrett ◽  
Marty J Faville
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Nutritive Value ◽  
Prediction Models ◽  
Genotypic Variation ◽  
Genetic Correlations ◽  
Predictive Ability ◽  
Water Soluble ◽  
Training Set ◽  
Sib Families

AbstractForage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of genotypic, environmental and genotype-by-environment (G × E) variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P<0.05) genotypic variation was detected for all nutritive traits and genomic heritability (h2g) was moderate to high (0.20 to 0.74). G × E interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesC genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two environments. High predictive ability was observed for the mineral traits sulphur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from 1 million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined. For traits with lower predictive ability, multi-trait genomic prediction approaches that exploit the strong genetic correlations observed amongst some nutritive traits may be useful. This appears to be particularly important for WSC, considered one of the primary constituent of nutritive value for forages.

Do Institutions Promote Firm Performance? Evidence from Sub-Saharan Africa

2019 ◽  
Vol 11 (3) ◽  
pp. 111-137
Author(s):  
Adamu Jibir ◽  
Musa Abdu ◽  
Farida Bello ◽  
Iliya Garba
Keyword(s):  
Fixed Effects ◽  
Labour Productivity ◽  
Sub Saharan Africa ◽  
Data Sets ◽  
Fixed Effects Model ◽  
Multiple Sources ◽  
Institutional Reforms ◽  
Regulatory Quality ◽  
Sub Saharan ◽  
Control Of Corruption

This article examines the roles of institutions in driving firms’ performance using merged Enterprise Panel Survey data sets collected and compiled by World Bank for 23 sub-Saharan Africa (SSA). The study used labour productivity as a proxy of firms’ performance due to its advantages over total factor productivity. To address the problems of multiple sources of heterogeneity and simultaneity biases, the study employed high-dimensional fixed-effects model together with its instrumental variable version to estimate the impacts of institutions on firms’ performance in SSA. The study established that institutions contribute significantly and positively to firms’ performance in SSA. The study infers that control of corruption, government effectiveness, regulatory quality and rule of law are pro-market institutions that effectively create favourable business and investment climates. Other findings of the study include capital–labour ratio, export, high school, average labour costs, research and development (R&D) and capacity utilisation, which all drive the firm’s performance in SSA. Thus, the findings imply that prevailing and future policies on business, trade and investment in SSA should also incorporate institutional reforms as one of the essential measures to rapidly and sustainably boost economic performance in the region.

scholarly journals Divergent Genomic Selection for Herbage Accumulation and Days-To-Heading in Perennial Ryegrass

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 340
Author(s):  
Marty Faville ◽  
Mingshu Cao ◽  
Jana Schmidt ◽  
Douglas Ryan ◽  
Siva Ganesh ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Perennial Ryegrass ◽  
Genetic Gain ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Selection Response ◽  
Training Set ◽  
Days To Heading ◽  
Selection For ◽  
Target Environment

Increasing the rate of genetic gain for dry matter (DM) yield in perennial ryegrass (Lolium perenne L.), which is a key source of nutrition for ruminants in temperate environments, is an important goal for breeders. Genomic selection (GS) is a strategy used to improve genetic gain by using molecular marker information to predict breeding values in selection candidates. An empirical assessment of GS for herbage accumulation (HA; proxy for DM yield) and days-to-heading (DTH) was completed by using existing genomic prediction models to conduct one cycle of divergent GS in four selection populations (Pop I G1 and G3; Pop III G1 and G3), for each trait. G1 populations were the offspring of the training set and G3 populations were two generations further on from that. The HA of the High GEBV selection group (SG) progenies, averaged across all four populations, was 28% higher (p < 0.05) than Low GEBV SGs when assessed in the target environment, while it did not differ significantly in a second environment. Divergence was greater in Pop I (43%–65%) than Pop III (10%–16%) and the selection response was higher in G1 than in G3. Divergent GS for DTH also produced significant (p < 0.05) differences between High and Low GEBV SGs in G1 populations (+6.3 to 9.1 days; 31%–61%) and smaller, non-significant (p > 0.05) responses in G3. This study shows that genomic prediction models, trained from a small, composite reference set, can be used to improve traits with contrasting genetic architectures in perennial ryegrass. The results highlight the importance of target environment selection for training models, as well as the influence of relatedness between the training set and selection populations.

The African Development Bank, Organized Hypocrisy, and Maternal Mortality

2019 ◽  
Vol 5 (1) ◽  
pp. 31-49 ◽  
Author(s):  
Jamie M. Sommer ◽  
John M. Shandra ◽  
Michael Restivo ◽  
Holly E. Reed
Keyword(s):  
Maternal Mortality ◽  
Fixed Effects ◽  
Health Sector ◽  
Bank Lending ◽  
Sub Saharan Africa ◽  
Fixed Effects Model ◽  
Development Bank ◽  
African Development ◽  
African Development Bank ◽  
Sub Saharan

We draw on the theory of organized hypocrisy and examine how different forms of lending by the African Development Bank affect maternal mortality in Sub-Saharan Africa. We do so by using a two-way fixed effects model for a sample of 33 Sub-Saharan African nations from 1990 to 2010. We find that the bank's structural adjustment lending in the health sector is associated with increased maternal mortality, and its reproductive health investment lending is associated with decreased maternal mortality, consistent with the organized hypocrisy approach. These findings remain stable and consistent even when controlling for World Bank lending and other relevant control variables. We conclude by discussing the implications of these findings for global health and development.

scholarly journals Genomic selection: the option for new robustness traits?

2013 ◽  
Vol 4 (3) ◽  
pp. 618-625 ◽  
Author(s):  
M. P. L. Calus ◽  
D. P. Berry ◽  
G. Banos ◽  
Y. de Haas ◽  
R. F. Veerkamp
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Genetic Selection ◽  
Reference Population ◽  
Progeny Testing ◽  
Production Traits ◽  
Large Populations ◽  
Dairy Cattle Breeding ◽  
Selection For

Genomic selection is rapidly becoming the state-of-the-art genetic selection methodology in dairy cattle breeding schemes around the world. The objective of this paper was to explore possibilities to apply genomic selection for traits related to dairy cow robustness. Deterministic simulations indicate that replacing progeny testing with genomic selection may favour genetic response for production traits at the expense of robustness traits, owing to a disproportional change in accuracies obtained across trait groups. Nevertheless, several options are available to improve the accuracy of genomic selection for robustness traits. Moreover, genomic selection opens up the opportunity to begin selection for new traits using specialised reference populations of limited size where phenotyping of large populations of animals is currently prohibitive. Reference populations for such traits may be nucleus-type herds, research herds or pooled data from (international) research experiments or research herds. The RobustMilk project has set an example for the latter approach, by collating international data for progesterone-based traits, feed intake and energy balance-related traits. Reference population design, both in terms of relatedness of the animals and variability in phenotypic performance, is important to optimise the accuracy of genomic selection. Use of indicator traits, combined with multi-trait genomic prediction models, can further contribute to improved accuracy of genomic prediction for robustness traits. Experience to date indicates that for newly recorded robustness traits that are negatively correlated with the main breeding goal, cow reference populations of ⩾10 000 are required when genotyping is based on medium- or high-density single-nucleotide polymorphism arrays. Further genotyping advances (e.g. sequencing) combined with post-genomics technologies will enhance the opportunities for (genomic) selection to improve cow robustness.

scholarly journals Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for dealing with G × E

2019 ◽  
Author(s):  
L.M. Souza ◽  
F.R. Francisco ◽  
P.S. Gonçalves ◽  
E.J. Scaloppi Junior ◽  
V. Le Guen ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Rubber Tree ◽  
Tree Breeding ◽  
Genotypic Effect ◽  
Breeding Programs ◽  
Genetic Breeding ◽  
Effect Model

AbstractSeveral genomic prediction models incorporating genotype × environment (G×E) interactions have recently been developed and used in genomic selection (GS) in plant breeding programs. G×E interactions decrease selection accuracy and limit genetic gains in plant breeding. Two genomic data sets were used to compare the prediction ability of multi-environment G×E genomic models and two kernel methods (a linear kernel (genomic best linear unbiased predictor, GBLUP) (GB) and a nonlinear kernel (Gaussian kernel, GK)) and prediction accuracy (PA) of five genomic prediction models: (1) one without environmental data (BSG); (2) a single-environment, main genotypic effect model (SM); (3) a multi-environment, main genotypic effect model (MM); (4) a multi-environment, single variance GxE deviation model (MDs); and (5) a multi-environment, environment-specific variance GxE deviation model (MDe). We evaluated the utility of GS with 435 rubber tree individuals in two sites and genotyped the individuals with genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were estimated for diameter (DAP) and height (AP) at different ages, with a heritability ranging from 0.59 to 0.75 for both traits. Applying the model (BSG, SM, MM, MDs, and MDe) and kernel method (GBLUP and GK) combinations to rubber tree data showed that models with the nonlinear GK and linear GBLUP kernel had similar PAs. Multi-environment models were superior to single-environment genomic models regardless the kernel (GBLUP or GK), suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. In the best scenario (well-watered (WW / GK), an increase of 6.7 and 8.7 fold of genetic gain can be obtained for AP and DAP, respectively, with multi-environment GS (MM, MDe and MDS) than by conventional genetic breeding model (CBM). Furthermore, GS resulted in a more balanced selection response in DAP and AP and if used in conjunction with traditional genetic breeding programs will contribute to a reduction in selection time. With the rapid advances in and declining costs of genotyping methods, balanced against the overall costs of managing large progeny trials and potential increased gains per unit time, we are hopeful that GS can be implemented in rubber tree breeding programs.

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