Multi-trait Genomic Selection Methods for Crop Improvement

Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.

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Accuracy of Genomic Selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.)

Genetics ◽

10.1534/genetics.111.137026 ◽

2012 ◽

Vol 190 (4) ◽

pp. 1503-1510 ◽

Cited By ~ 236

Author(s):

M. F. R. Resende ◽

P. Muñoz ◽

M. D. V. Resende ◽

D. J. Garrick ◽

R. L. Fernando ◽

...

Keyword(s):

Genomic Selection ◽

Pinus Taeda ◽

Loblolly Pine ◽

Selection Methods ◽

Data Set ◽

Standard Data ◽

Pinus Taeda L

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Genomic selection methods for crop improvement: Current status and prospects

The Crop Journal ◽

10.1016/j.cj.2018.03.001 ◽

2018 ◽

Vol 6 (4) ◽

pp. 330-340 ◽

Cited By ~ 50

Author(s):

Xin Wang ◽

Yang Xu ◽

Zhongli Hu ◽

Chenwu Xu

Keyword(s):

Genomic Selection ◽

Crop Improvement ◽

Current Status ◽

Selection Methods

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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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Properties and methods of estimation for a bivariate exponentiated Fréchet distribution

Mathematica Slovaca ◽

10.1515/ms-2017-0426 ◽

2020 ◽

Vol 70 (5) ◽

pp. 1211-1230

Author(s):

Abdus Saboor ◽

Hassan S. Bakouch ◽

Fernando A. Moala ◽

Sheraz Hussain

Keyword(s):

Maximum Likelihood ◽

Probability Density Function ◽

Probability Density ◽

Density Function ◽

Superior Performance ◽

Estimation Methods ◽

Conditional Probability Density ◽

Data Set ◽

Fréchet Distribution ◽

Frechet Distribution

AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimation of the parameters is investigated by the maximum likelihood with the observed information matrix. In addition to the maximum likelihood estimation method, we consider the Bayesian inference and least square estimation and compare these three methodologies for the BvEF. A simulation study is carried out to compare the performance of the estimators by the presented estimation methods. The proposed bivariate distribution with other related bivariate distributions are fitted to a real-life paired data set. It is shown that, the BvEF distribution has a superior performance among the compared distributions using several tests of goodness–of–fit.

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Fundamental resource trade-offs for encoded distributed optimization

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa026 ◽

2020 ◽

Author(s):

A Salman Avestimehr ◽

Seyed Mohammadreza Mousavi Kalan ◽

Mahdi Soltanolkotabi

Keyword(s):

Computational Time ◽

Massive Data ◽

Data Sets ◽

Massive Data Sets ◽

Computational Framework ◽

Data Set ◽

Trade Offs ◽

Major Bottleneck ◽

Computing Environments ◽

Analyze Data

Abstract Dealing with the shear size and complexity of today’s massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. A recent computational framework, called encoded optimization, creates redundancy in the data to mitigate the effect of stragglers. In this paper, we develop novel mathematical understanding for this framework demonstrating its effectiveness in much broader settings than was previously understood. We also analyze the convergence behavior of iterative encoded optimization algorithms, allowing us to characterize fundamental trade-offs between convergence rate, size of data set, accuracy, computational load (or data redundancy) and straggler toleration in this framework.

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CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

Genome Medicine ◽

10.1186/s13073-021-00835-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Philipp Rentzsch ◽

Max Schubach ◽

Jay Shendure ◽

Martin Kircher

Keyword(s):

Prediction Models ◽

Splice Variants ◽

Superior Performance ◽

Data Set ◽

Pathogenic Variants ◽

Genome Wide ◽

Donor And Acceptor ◽

Human Proteins ◽

Variant Effect ◽

Variant Effect Prediction

Abstract Background Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. Methods It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. Results We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. Conclusions While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

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Demographic life history traits of reproductive natterjack toads (Bufo calamita) vary between northern and southern latitudes

Amphibia-Reptilia ◽

10.1163/156853806778189918 ◽

2006 ◽

Vol 27 (3) ◽

pp. 365-375 ◽

Cited By ~ 31

Author(s):

Delfi Sanuy ◽

Christoph Leskovar ◽

Neus Oromi ◽

Ulrich Sinsch

Keyword(s):

Life History ◽

Life History Traits ◽

Large Female ◽

Breeding Period ◽

Female Size ◽

Data Set ◽

Bufo Calamita ◽

Trade Offs ◽

Age Variation ◽

The Cost

AbstractDemographic life history traits were investigated in three Bufo calamita populations in Germany (Rhineland-Palatinate: Urmitz, 50°N; 1998-2000) and Spain (Catalonia: Balaguer, Mas de Melons, 41°N; 2004). We used skeletochronology to estimate the age as number of lines of arrested growth in breeding adults collected during the spring breeding period (all localities) and during the summer breeding period (only Urmitz). A data set including the variables sex, age and size of 185 males and of 87 females was analyzed with respect to seven life history traits (age and size at maturity of the youngest first breeders, age variation in first breeders, longevity, potential reproductive lifespan, median lifespan, age-size relationship). Spring and summer cohorts at the German locality differed with respect to longevity and potential reproductive lifespan by one year in favour of the early breeders. The potential consequences on fitness and stability of cohorts are discussed. Latitudinal variation of life history traits was mainly limited to female natterjacks in which along a south-north gradient longevity and potential reproductive lifespan increased while size decreased. These results and a review of published information on natterjack demography suggest that lifetime number of offspring seem to be optimized by locally different trade-offs: large female size at the cost of longevity in southern populations and increased longevity at the cost of size in northern ones.

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Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

Political Analysis ◽

10.1017/pan.2017.15 ◽

2017 ◽

Vol 25 (4) ◽

pp. 413-434 ◽

Cited By ~ 29

Author(s):

Justin Grimmer ◽

Solomon Messing ◽

Sean J. Westwood

Keyword(s):

Treatment Effects ◽

Ensemble Methods ◽

Ensemble Method ◽

Superior Performance ◽

Accurate Estimation ◽

Randomized Experiments ◽

Heterogeneous Treatment Effects ◽

Data Set ◽

Heterogeneous Effects ◽

The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

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Robust estimation of vertical symmetry axis models via joint migration inversion: Including multiples in anisotropic parameter estimation

Geophysics ◽

10.1190/geo2017-0856.1 ◽

2019 ◽

Vol 84 (1) ◽

pp. C57-C74 ◽

Cited By ~ 4

Author(s):

Abdulrahman A. Alshuhail ◽

Dirk J. Verschuur

Keyword(s):

Symmetry Axis ◽

Transverse Isotropy ◽

Half Cycle ◽

Anisotropic Parameter ◽

Data Set ◽

Trade Off ◽

Trade Offs ◽

Highly Nonlinear ◽

Wide Range ◽

Internal Multiples

Because the earth is predominately anisotropic, the anisotropy of the medium needs to be included in seismic imaging to avoid mispositioning of reflectors and unfocused images. Deriving accurate anisotropic velocities from the seismic reflection measurements is a highly nonlinear and ambiguous process. To mitigate the nonlinearity and trade-offs between parameters, we have included anisotropy in the so-called joint migration inversion (JMI) method, in which we limit ourselves to the case of transverse isotropy with a vertical symmetry axis. The JMI method is based on strictly separating the scattering effects in the data from the propagation effects. The scattering information is encoded in the reflectivity operators, whereas the phase information is encoded in the propagation operators. This strict separation enables the method to be more robust, in that it can appropriately handle a wide range of starting models, even when the differences in traveltimes are more than a half cycle away. The method also uses internal multiples in estimating reflectivities and anisotropic velocities. Including internal multiples in inversion not only reduces the crosstalk in the final image, but it can also reduce the trade-off between the anisotropic parameters because internal multiples usually have more of an imprint of the subsurface parameters compared with primaries. The inverse problem is parameterized in terms of a reflectivity, vertical velocity, horizontal velocity, and a fixed [Formula: see text] value. The method is demonstrated on several synthetic models and a marine data set from the North Sea. Our results indicate that using JMI for anisotropic inversion makes the inversion robust in terms of using highly erroneous initial models. Moreover, internal multiples can contain valuable information on the subsurface parameters, which can help to reduce the trade-off between anisotropic parameters in inversion.

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Evolutionary and ecological perspectives on the wheat phenotype

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2021.1259 ◽

2021 ◽

Vol 288 (1958) ◽

pp. 20211259

Author(s):

Victor O. Sadras

Keyword(s):

Precision Agriculture ◽

Current Model ◽

Crop Improvement ◽

Wheat Yield ◽

High Yield ◽

Striking Similarity ◽

Stabilizing Selection ◽

Grain Number ◽

Offspring Number ◽

Trade Offs

Technologies, from molecular genetics to precision agriculture, are outpacing theory, which is becoming a bottleneck for crop improvement. Here, we outline theoretical insights on the wheat phenotype from the perspective of three evolutionary and ecologically important relations—mother–offspring, plant–insect and plant–plant. The correlation between yield and grain number has been misinterpreted as cause-and-effect; an evolutionary perspective shows a striking similarity between crop and fishes. Both respond to environmental variation through offspring number; seed and egg size are conserved. The offspring of annual plants and semelparous fishes, lacking parental care, are subject to mother–offspring conflict and stabilizing selection. Labile reserve carbohydrates do not fit the current model of wheat yield; they can stabilize grain size, but involve trade-offs with root growth and grain number, and are at best neutral for yield. Shifting the focus from the carbon balance to an ecological role, we suggest that labile carbohydrates may disrupt aphid osmoregulation, and thus contribute to wheat agronomic adaptation. The tight association between high yield and low competitive ability justifies the view of crop yield as a population attribute whereby the behaviour of the plant becomes subordinated within that of the population, with implications for genotyping, phenotyping and plant breeding.

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