scholarly journals Genomic prediction using composite training sets is an effective method for exploiting germplasm conserved in rice gene banks

2022 ◽  
Author(s):  
Sang He ◽  
Hongyan Liu ◽  
Junhui Zhan ◽  
Yun Meng ◽  
Yamei Wang ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Rice Gene ◽  
Gene Banks ◽  
Training Sets

scholarly journals Enviromic-based Kernels Optimize Resource Allocation with Multi-trait Multi-environment Genomic Prediction for Tropical Maize

2021 ◽  
Author(s):  
Raysa Gevartosky ◽  
Humberto Fanelli Carvalho ◽  
Germano Costa-Neto ◽  
Osval A. Montesinos-Lopez ◽  
Jose Crossa ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Genomic Prediction ◽  
Predictive Ability ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Apply Genetic ◽  
Ear Height ◽  
Predictor Model ◽  
Training Sets

Genomic prediction (GP) success is directly dependent on establishing a training population, where incorporating envirotyping data and correlated traits may increase the GP accuracy. Therefore, we aimed to design optimized training sets for multi-trait for multi-environment trials (MTMET). For that, we evaluated the predictive ability of five GP models using the genomic best linear unbiased predictor model (GBLUP) with additive + dominance effects (M1) as the baseline and then adding genotype by environment interaction (G × E) (M2), enviromic data (W) (M3), W+G × E (M4), and finally W+G × W (M5), where G × W denotes the genotype by enviromic interaction. Moreover, we considered single-trait multi-environment trials (STMET) and MTMET for three traits: grain yield (GY), plant height (PH), and ear height (EH), with two datasets and two cross-validation schemes. Afterward, we built two kernels for genotype by environment by trait interaction (GET) and genotype by enviromic by trait interaction (GWT) to apply genetic algorithms to select genotype:environment:trait combinations that represent 98% of the variation of the whole dataset and composed the optimized training set (OTS). Using OTS based on enviromic data, it was possible to increase the response to selection per amount invested by 142%. Consequently, our results suggested that genetic algorithms of optimization associated with genomic and enviromic data efficiently design optimized training sets for genomic prediction and improve the genetic gains per dollar invested.

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

2021 ◽  
Vol 12 ◽  
Author(s):  
Shiliang Cao ◽  
Junqiao Song ◽  
Yibing Yuan ◽  
Ao Zhang ◽  
Jiaojiao Ren ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Genetic Relationships ◽  
Genotyping By Sequencing ◽  
Genotypic Diversity ◽  
Complex Trait ◽  
Training Set ◽  
Tar Spot ◽  
Training Sets

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.

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

2018 ◽  
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.

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

2020 ◽  
Vol 52 (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.

Genomic prediction contributing to a promising global strategy to turbocharge gene banks

Nature Plants ◽  
2016 ◽  
Vol 2 (10) ◽  
Author(s):  
Xiaoqing Yu ◽  
Xianran Li ◽  
Tingting Guo ◽  
Chengsong Zhu ◽  
Yuye Wu ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Global Strategy ◽  
Gene Banks

scholarly journals Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Plants ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Arfang Badji ◽  
Lewis Machida ◽  
Daniel Bomet Kwemoi ◽  
Frank Kumi ◽  
Dennis Okii ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Insect Pests ◽  
Fall Armyworm ◽  
Sub Saharan Africa ◽  
Tropical Maize ◽  
Maize Weevil ◽  
Individual Traits ◽  
Maize Resistance ◽  
Sub Saharan ◽  
Training Sets

Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAW resistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.

Sign in / Sign up

Export Citation Format

Share Document