Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

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Improving Selection Efficiency of Crop Breeding with a Genomic Prediction Aided Partial Phenotyping Strategy

10.21203/rs.3.rs-365517/v1 ◽

2021 ◽

Author(s):

Sang He ◽

Yong Jiang ◽

Rebecca Thistlethwaite ◽

Matthew Hayden ◽

Richard Trethowan ◽

...

Keyword(s):

Genomic Prediction ◽

Selection Response ◽

Response To Selection ◽

Selection Efficiency ◽

Crop Breeding ◽

Main Interest ◽

Breeding Programs ◽

Selection Accuracy

Abstract Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a partial phenotyping strategy that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the partial genomic phenotyping strategy in a wheat dataset. Whether the partial phenotyping strategy resulted in more selection response depended on the correlations of phenotypes between environments. The partial phenotyping strategy consistently showed statistically significant higher simulated responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. Our results indicate that genomics-based partial phenotyping can improve selection response at middle stages of crop breeding programs.

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Review: Optimizing genomic selection for crossbred performance by model improvement and data collection

Journal of Animal Science ◽

10.1093/jas/skab205 ◽

2021 ◽

Author(s):

Pascal Duenk ◽

Piter Bijma ◽

Yvonne C J Wientjes ◽

Mario P L Calus

Keyword(s):

Genomic Prediction ◽

Real Data ◽

Reference Population ◽

Additive Models ◽

Future Research ◽

Response To Selection ◽

Dominance Model ◽

Breeding Programs ◽

Specific Effects ◽

Breeding Goal

Abstract Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred (CB) performance for purebred (PB) selection candidates. In this review, we compared genomic prediction strategies that differed in (1) the genomic prediction model used, or (2) the data used in the reference population. We found 27 unique studies, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data. Differences in accuracy and response to selection between strategies depended on i) the value of the purebred crossbred genetic correlation (rpc), ii) the genetic distance between the parental lines, iii) the size of PB and CB reference populations, and iv) the relatedness of these reference populations to the selection candidates. In studies where a PB reference population was used, the use of a dominance model yielded accuracies that were equal to or higher than those of additive models. When rpc was lower than ~0.8, and was caused mainly by GxE, it was beneficial to create a reference population of PB animals that are tested in a CB environment. In general, the benefit of collecting CB information increased with decreasing rpc. For a given rpc, the benefit of collecting CB information increased with increasing size of the reference populations. Collecting CB information was not beneficial when rpc was higher than ~0.9, especially when the reference populations were small. Collecting only phenotypes of CB animals may slightly improve accuracy and response to selection, but requires that the pedigree is known. It is therefore advisable to genotype these CB animals as well. Finally, considering the breed-origin of alleles allows for modelling breed-specific effects in the CB, but this did not always lead to higher accuracies. Our review shows that the differences in accuracy and response to selection between strategies depend on several factors. One of the most important factors is rpc, and we therefore recommend to obtain accurate estimates of rpc of all breeding goal traits. Furthermore, knowledge about the importance of components of rpc (i.e., dominance, epistasis, and GxE) can help breeders to decide which model to use, and whether to collect data on animals in a CB environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.

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ESTIMASI NILAI HERITABILITAS BOBOT IKAN MAS VARIETAS PUNTEN DALAM PROGRAM SELEKSI INDIVIDU

Jurnal Riset Akuakultur ◽

10.15578/jra.11.3.2016.217-223 ◽

2016 ◽

Vol 11 (3) ◽

pp. 217

Author(s):

Estu Nugroho ◽

Budi Setyono ◽

Mochammad Su’eb ◽

Tri Heru Prihadi

Keyword(s):

Body Weight ◽

Selection Response ◽

Base Population ◽

Breeding Programs ◽

Male And Female ◽

Body Weight Trait ◽

Population Selection ◽

Selection For ◽

Weight Trait ◽

Selection Of

Program pemuliaan ikan mas varietas Punten dilakukan dengan seleksi individu terhadap karakter bobot ikan. Pembentukan populasi dasar untuk kegiatan seleksi dilakukan dengan memijahkan secara massal induk ikan mas yang terdiri atas 20 induk betina dan 21 induk jantan yang dikoleksi dari daerah Punten, Kepanjen (delapan betina dan enam jantan), Kediri (tujuh betina dan 12 jantan), Sragen (27 betina dan 10 jantan), dan Blitar (15 betina dan 11 jantan). Larva umur 10 hari dipelihara selama empat bulan. Selanjutnya dilakukan penjarangan sebesar 50% dan benih dipelihara selama 14 bulan untuk dilakukan seleksi dengan panduan hasil sampling 250 ekor individu setiap populasi. Seleksi terhadap calon induk dilakukan saat umur 18 bulan pada populasi jantan dan betina secara terpisah dengan memilih berdasarkan 10% bobot ikan yang terbaik. Calon induk yang terseleksi kemudian dipelihara hingga matang gonad, kemudian dipilih sebanyak 150 pasang dan dipijahkan secara massal. Didapatkan respons positif dari hasil seleksi berdasarkan bobot ikan, yaitu 49,89 g atau 3,66% (populasi ikan jantan) dan 168,47 g atau 11,43% (populasi ikan betina). Nilai heritabilitas untuk bobot ikan adalah 0,238 (jantan) dan 0,505 (betina).Punten carp breeding programs were carried out by individual selection for body weight trait. The base population for selection activities were conducted by mass breeding of parent consisted of 20 female and 21 male collected from area Punten, eight female and six male (Kepanjen), seven female and 12 male (Kediri), 27 female and 10 male (Sragen), 15 female and 11 male (Blitar). Larvae 10 days old reared for four moths. Then after spacing out 50% of total harvest, the offspring reared for 14 months for selection activity based on the sampling of 250 individual each population. Selection of broodstock candidates performed since 18 months age on male and female populations separately by selecting based on 10% of fish with best body weight. Candidates selected broodstocks were then maintained until mature. In oder to produce the next generation 150 pairs were sets and held for mass spawning. The results revealed that selection response were positive, 49.89 g (3.66%) for male and 168.47 (11.43%) for female. Heritability for body weight is 0.238 (male) and 0.505 (female).

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The look ahead trace back optimizer for genomic selection under transparent and opaque simulators

Scientific Reports ◽

10.1038/s41598-021-83567-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Fatemeh Amini ◽

Felipe Restrepo Franco ◽

Guiping Hu ◽

Lizhi Wang

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

In Planta ◽

Data Set ◽

Look Ahead ◽

Trace Back ◽

New Variant ◽

The Look ◽

Partially Observable

AbstractRecent advances in genomic selection (GS) have demonstrated the importance of not only the accuracy of genomic prediction but also the intelligence of selection strategies. The look ahead selection algorithm, for example, has been found to significantly outperform the widely used truncation selection approach in terms of genetic gain, thanks to its strategy of selecting breeding parents that may not necessarily be elite themselves but have the best chance of producing elite progeny in the future. This paper presents the look ahead trace back algorithm as a new variant of the look ahead approach, which introduces several improvements to further accelerate genetic gain especially under imperfect genomic prediction. Perhaps an even more significant contribution of this paper is the design of opaque simulators for evaluating the performance of GS algorithms. These simulators are partially observable, explicitly capture both additive and non-additive genetic effects, and simulate uncertain recombination events more realistically. In contrast, most existing GS simulation settings are transparent, either explicitly or implicitly allowing the GS algorithm to exploit certain critical information that may not be possible in actual breeding programs. Comprehensive computational experiments were carried out using a maize data set to compare a variety of GS algorithms under four simulators with different levels of opacity. These results reveal how differently a same GS algorithm would interact with different simulators, suggesting the need for continued research in the design of more realistic simulators. As long as GS algorithms continue to be trained in silico rather than in planta, the best way to avoid disappointing discrepancy between their simulated and actual performances may be to make the simulator as akin to the complex and opaque nature as possible.

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Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽

10.1093/genetics/iyab030 ◽

2021 ◽

Author(s):

Marco Lopez-Cruz ◽

Gustavo de los Campos

Keyword(s):

Sample Size ◽

Dna Sequences ◽

Genomic Prediction ◽

Prediction Accuracy ◽

Regularization Parameter ◽

Selection Index ◽

Prediction Method ◽

Training Data ◽

Breeding Value ◽

Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽

10.3390/inventions6020042 ◽

2021 ◽

Vol 6 (2) ◽

pp. 42

Author(s):

Worasit Sangjan ◽

Arron H. Carter ◽

Michael O. Pumphrey ◽

Vadim Jitkov ◽

Sindhuja Sankaran

Keyword(s):

Real Time ◽

Imaging System ◽

Crop Growth ◽

Sensor System ◽

Plant Phenotyping ◽

Raspberry Pi ◽

The Internet ◽

Growth Stages ◽

Crop Breeding ◽

Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

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Variance component estimates and genetic trends in a broiler breeder dam line using animal model methods

Proceedings of the British Society of Animal Science ◽

10.1017/s0308229600028208 ◽

1995 ◽

Vol 1995 ◽

pp. 51-51

Author(s):

A.J. Morri ◽

G.E. Pollott

Keyword(s):

Breeding Value ◽

Data Set ◽

Breeding Programs ◽

Broiler Breeder ◽

Value Prediction ◽

Recent Developments ◽

Commercial Breeding ◽

Variance Component Estimates ◽

Poultry Data ◽

Genetic Trends

Recent developments in computing technology have seen the widespread implementation of animal models for parameter estimation and breeding value prediction in both experimental and commercial breeding programs. These techniques have several benefits over traditional least squares and index methods, including the ability to monitor genetic trends in die absence of an unselected control population. This paper reports heritabilities and genetic trends estimated from a large poultry data set for three broiler traits.

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Rates of inbreeding using DNA fingerprinting in aquaculture breeding programs at various broodstock fitness levels — a simulation study

Australian Journal of Experimental Agriculture ◽

10.1071/ea05042 ◽

2005 ◽

Vol 45 (8) ◽

pp. 893 ◽

Cited By ~ 4

Author(s):

M. Macbeth

Keyword(s):

Dna Fingerprinting ◽

Simulation Study ◽

Intraclass Correlation ◽

Genetic Selection ◽

Selection Response ◽

Phenotypic Variance ◽

Selection Intensity ◽

Breeding Programs ◽

Inbreeding Rate ◽

Number Of Individuals

A simulation study was used to examine the potential use of DNA fingerprinting (DNA tagging) as a tool to avoid excessive inbreeding by identifying suitable candidate breeders in genetic selection programs. ‘Broodstock fitness’ (the ability of broodstock to survive from harvest and reproduce) needs to be considered in designing breeding programs using DNA tagging. In this study, reduced broodstock fitness increased inbreeding exponentially. The level of inbreeding was also dependent on the intraclass correlation (t), selection intensity, number of individuals DNA tagged (NDNA), number of families maintained (Nf) and the number of candidate breeders retained per sex/family at harvest (C). With a broodstock fitness of 0.90, DNA tagging could theoretically achieve a selection intensity, in terms of the total phenotypic variance, of 2.90 standard deviations with 800 000 graded at harvest, while maintaining an inbreeding rate of 1.0% per generation (NDNA = 800, Nf = 30, C = 4, t = 0.3). In practice, the numbers required could be achieved by growing families in individual facilities (e.g. sea cages for barramundi or ponds for prawns). When mechanical grading is not possible, the selection pool may be limited to a level where physical tagging is feasible. In this case, there was no advantage in selection response using DNA tagging compared with physical tags. DNA tagging as a selection tool may be more feasible when broodstock fitness is above 0.6, and may fill a niche where industry infrastructure is not large enough to support separate rearing of families or where physical tagging is not economically viable or suitable. DNA tagging may also be useful as a means of recovering families in backup facilities where families have been pooled to reduce infrastructure costs. Due to the random nature of DNA sampling, not all families may be recovered and a reduction in selection pressure may facilitate family recovery.

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

Genes ◽

10.3390/genes10090669 ◽

2019 ◽

Vol 10 (9) ◽

pp. 669 ◽

Cited By ~ 2

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.

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