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

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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The effectiveness of genomic selection for milk production traits of Holstein dairy cattle

Asian-Australasian Journal of Animal Sciences ◽

10.5713/ajas.19.0546 ◽

2020 ◽

Vol 33 (3) ◽

pp. 382-389 ◽

Cited By ~ 1

Author(s):

Yun-Mi Lee ◽

Chang-Gwon Dang ◽

Mohammad Z. Alam ◽

You-Sam Kim ◽

Kwang-Hyeon Cho ◽

...

Keyword(s):

Genomic Selection ◽

Milk Production ◽

Genetic Gain ◽

Milk Fat ◽

Breeding Value ◽

Production Traits ◽

Milk Production Traits ◽

Data Set ◽

Selection For ◽

Estimated Breeding Value

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population.Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction.Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (LSB) and that for the production of cows (LSC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (LDB) and cows (LDC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records.Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

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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.

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

Agronomy ◽

10.3390/agronomy10030340 ◽

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.

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Genomic selection strategies for clonally propagated crops

10.1101/2020.06.15.152017 ◽

2020 ◽

Cited By ~ 1

Author(s):

Christian R. Werner ◽

R. Chris Gaynor ◽

Daniel J. Sargent ◽

Alessandra Lillo ◽

Gregor Gorjanc ◽

...

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Genomic Prediction ◽

Breeding Program ◽

Selection Methods ◽

Breeding Programs ◽

Breeding Values ◽

Parent Selection ◽

Estimated Breeding Values ◽

Selection Of

AbstractFor genomic selection in clonal breeding programs to be effective, crossing parents should be selected based on genomic predicted cross performance unless dominance is negligible. Genomic prediction of cross performance enables a balanced exploitation of the additive and dominance value simultaneously. Here, we compared different strategies for the implementation of genomic selection in clonal plant breeding programs. We used stochastic simulations to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included i) a breeding program that introduced genomic selection in the first clonal testing stage, and ii) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were i) selection of parents based on genomic estimated breeding values, and ii) selection of parents based on genomic predicted cross performance. Selection of parents based on genomic predicted cross performance produced faster genetic gain than selection of parents based on genomic estimated breeding values because it substantially reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using genomic prediction of cross performance always produced the most genetic gain unless dominance was negligible. We conclude that i) in clonal breeding programs with genomic selection, parents should be selected based on genomic predicted cross performance, and ii) a two-part breeding program with parent selection based on genomic predicted cross performance to rapidly drive population improvement has great potential to improve breeding clonally propagated crops.

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1526-P: Time-Dependent Effect of Kidney Disease on All-Cause and CVD Mortality in Adults with Type 2 Diabetes: A Secondary Analysis of the Look AHEAD Clinical Trial

Diabetes ◽

10.2337/db20-1526-p ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 1526-P

Author(s):

ANDREW C. WILLS ◽

WILLIAM C. KNOWLER

Keyword(s):

Type 2 Diabetes ◽

Clinical Trial ◽

Secondary Analysis ◽

Time Dependent ◽

Dependent Effect ◽

Look Ahead ◽

Time Dependent Effect ◽

The Look ◽

Cvd Mortality

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170-OR: Association of Timing of Physical Activity Bouts with Cardiorespiratory Fitness in Adults with Type 2 Diabetes in the Look AHEAD Study

Diabetes ◽

10.2337/db20-170-or ◽

2020 ◽

Vol 69 (Supplement 1) ◽

pp. 170-OR

Author(s):

JINGYI QIAN ◽

MICHAEL P. WALKUP ◽

SHYH-HUEI CHEN ◽

PETER H. BRUBAKER ◽

DALE BOND ◽

...

Keyword(s):

Physical Activity ◽

Type 2 Diabetes ◽

Cardiorespiratory Fitness ◽

Look Ahead ◽

The Look

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Faculty Opinions recommendation of Changes in body composition over 8 years in a randomized trial of a lifestyle intervention: the look AHEAD study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725370893.793505083 ◽

2015 ◽

Author(s):

Marleen van Baak

Keyword(s):

Body Composition ◽

Randomized Trial ◽

Lifestyle Intervention ◽

Look Ahead ◽

The Look

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Does Lifestyle Intervention Improve Health of Adults with Overweight/Obesity and Type 2 Diabetes: Findings from the Look AHEAD Randomized Trial

Obesity ◽

10.1002/oby.23158 ◽

2021 ◽

Author(s):

Rena R. Wing ◽

Keyword(s):

Type 2 Diabetes ◽

Randomized Trial ◽

Lifestyle Intervention ◽

Look Ahead ◽

The Look ◽

Improve Health

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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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