Source: IOWA STATE UNIVERSITY submitted to
DEVELOPMENT AND EVALUATION OF IMPROVED STRATEGIES FOR GENOMIC SELECTION VIA SIMULATIONS AND EMPIRICAL TESTING
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
TERMINATED
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1011702
Grant No.
2017-67007-26175
Project No.
IOW05494
Proposal No.
2016-07989
Multistate No.
(N/A)
Program Code
A5171
Project Start Date
Feb 15, 2017
Project End Date
Feb 14, 2021
Grant Year
2017
Project Director
Schnable, P. S.
Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
Agronomy
Non Technical Summary
The overall goal of the proposed project is to increase the efficiency of crop breeding programs by developing and deploying improved genomic selection strategies that rely on improvements in the selection and mating steps.As a consequence of growing populations, changing diets, and the challenges of climate change, agricultural systems must produce more with less. More means greater demand for agricultural products such as food, feed, energy and fiber. Less means reduced agricultural inputs (at least on a per output basis) such as water, fertilizer, pesticides and a reduced environmental footprint, all on less land. A key tool for making agriculture systems more productive, sustainable and resilient is genetic improvement via breeding.Selection based on favorable (phenotypic selection) traits was central to the domestication of crops and has been used successfully in plant breeding for thousands of years. Recurrent selection is a special case of phenotypic selection that refers to a type of population improvement which includes the following key steps: phenotyping (evaluation), selection, and mating.Phenotyping involves measuring trait values of a population, such as grain and biomass yield, flowering time and color, etc. This step is often time-consuming/labor intensive and many phenotypes cannot be accurately scored without replicated tests.Selection involves the identification of those individuals within the population that exhibit the most favorable trait values.Mating occurs following selection and selected lines are typically randomly or arbitrarily mated to create a new population for phenotyping and selection. Little research has been conducted on the effects of other mating strategies.The phenotyping step can be costly, time consuming, logistically challenging, and/or destructive because it often involves a large number of individuals and many factors that can affect the measurements. Genomic selection (GS), which relies on efficient, high-throughput genotyping technologies (to determine the genetic composition of plants), enables breeders to predict the phenotype of plants based on their genotypes using advanced statistical models and a population of plants of known phenotypes used to "train" the prediction model. This new approach has transformed breeding, because it eliminates or reduces the need for phenotyping.Even moderately accurate genomic prediction can result in substantial cost saving and improve the rate of genetic gain in breeding programs. Previous literature has focused on three areas: the first is to improve the accuracy of phenotype prediction, upon which selection is based; the second is to define other quantitative indicators in lieu of the predicted phenotype to reflect the fitness of the lines from the genetics perspective and their likelihood to produce superior progeny; the third is the selection of an optimal training population that maximizes the accuracy of the prediction model when only a limited number of plants can be phenotyped. Although genomic prediction has transformed breeding, the selection and mating steps have not received equal attention and typically remain the same as in traditional phenotypic selection. Optimizing these two steps is a focus of the proposed project.To design optimal selection and mating strategies and to understand their interactions with other aspect of breeding programs we will make use of simulations within a framework of operations research, which deals with the application of advanced analytical methods to help make better decisions. Significantly, we will also develop user-friendly tools to allow breeders to optimize these parameters for their own breeding scenarios. The new selection and mating strategies that we propose will be designed using advanced mathematical programming and optimization techniques, fully tailorable to reflect user preferences with respect to the trade-offs among cost, time, and probability of success.
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20115991080100%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1599 - Grain crops, general/other;

Field Of Science
1080 - Genetics;
Goals / Objectives
This project will use the framework of operations research to optimize selection and mating parameters for specific types of breeding programs. A sophisticated simulation platform will be developed and used to test hundreds of GS experiments, each with different parameters and breeding goals. In addition, populations will be developed that could be used for empirical validation experiments as part of another project sometime in the future. Finally, a user-friendly interface that will allow breeders to explore and optimize these parameters relative to their own breeding scenarios. The proposed research will contribute to enhancing the efficiency of breeding programs by introducing engineering processes. In addition, this project will train three graduate students at the intersection of operations research and plant breeding, helping to develop "next generation plant breeders".Project Goal 1: Design of Improved Genomic Selection Strategies:We will apply operations research methodology to design and optimize four distinct GS strategies to address various research needs.Project Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies:An exploratory simulation platform will be developed in Matlab to evaluate the GS strategies developed in Specific Aim 1.Project Goal 3: Evaluate Novel GS Strategies using the Simulation Platform.Project Goal 4: We will create populations that could be used for empirical validation experiments as part of another project sometime in the future.Project Goal 5: Develop a user-friendly interface for our GS simulation platform:A graphical user interface will be designed and implemented to allow breeders to explore alternative GS strategies using the simulation platform developed in Project Goal2.
Project Methods
The proposed project will focus on optimizing selection and mating steps of GS because our preliminary simulation studies have shown that the success of GS is sensitive to the parameters of these steps. In Project Goal 1 we will design and develop GS strategies appropriate for various situations faced by breeders (e.g., short or long-term goals). In Project Goal 2 we will develop a simulation platform that will allow us to evaluate and iteratively optimize in Project Goal 3 the GS strategies developed in Project Goal. In Project Goal 4 we will develop maize populations that will be suitable for empirical testing of simulation platform developed in Project Goal 2 for future studies. Finally, in Project Goal 5 we will develop a user-friendly interface that will allow end-users to use our simulation platform to explore various GS strategies and parameters.This project will train three graduate students at the intersection of operations research and plant breeding, helping to generate "next generation plant breeders". Project progress and availability of the products will be presented in scientific conferences and disseminated via academia seminars and publications. Project Goals 2 and 3 will be used to evaluate the GS strategies built in Project Goal 1 and the data will be presented in annual/final reports and scientific publications.

Progress 02/15/17 to 02/14/21

Outputs
Target Audience:The maize genetics community and breeders. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Wang and Hu (with other colleagues) co-organized and presented in two summer workshops on Operations Research in Plant Breeding. The workshop was offered in 2017 at ISU and in 2019 at Cornell University. Wang designed and organized class competitions on genomic selection to engineering students in one undergraduate and two graduate level courses: IE 312 (Optimization) -- 2017 IE 534 (Linear Optimization) -- 2017 IE 634 (Linear Optimization) -- 2017 and 2019 Some of the journal and conference publications resulted from these competitions. How have the results been disseminated to communities of interest?The results were disseminated to the communities via conference presentations and publications, please refer to the product section. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Overall impact statement: We have designed and developed several improved GS algorithms and proposed an integrated framework. We designed several computer simulation programs to evaluate these GS algorithms. A web-based simulation tool is available for educational purpose. We also developed early flowering populations that can be used for validation experiment in future projects. These tools can facilitate the rapid design, development, evaluation, and modeling of new crop cultivars under genetic and environmental uncertainty. Due to population growth and changing consumer preferences, maintenance of global food security requires more than maintaining current rates of genetic gain, particularly in the context of the diminishing returns of R&D investments in breeding major crops. The data-driven approaches we describe and their integration into decision-support tools have the potential to revolutionize twenty-first century plant breeding by enabling breeders to rapidly and cost-effectively design and develop appropriate cultivars for these future environments. Project Goal 1: Design of Improved Genomic Selection Strategies:We will apply operations research methodology to design and optimize four distinct GS strategies to address various research needs. We have published several new GS algorithms: · The optimal population value (OPV) approach that selects breeding parents with complementary favorable alleles was developed. · Three GS algorithms proposed by engineering students were found to be comparable with state-of-the-art algorithms in the literature. · Look ahead selection (LAS) that selects breeding parents based on the performance of their progeny in a future generation rather than the current generation was developed. · The complementarity-based selection algorithm to identify the most beneficial and complementary breeding parents was developed. · An extension of the look ahead selection algorithm suitable for use with multiple traits was developed. · The look ahead trace back algorithm was proposed to address imperfect prediction of allele effects. · An opaque simulator was developed for GS, which is more realistic than most simulators used in the literature. We also proposed and published a vision for a framework that integrates crop growth models, high-throughput genotyping, high-throughput phenotyping, and environmental sensing and modeling with new advances in statistical modeling, machine learning, and operations research in flexible and efficient decision-support tools. These tools will facilitate the rapid design, development, evaluation, and modeling of new crop cultivars under genetic and environmental uncertainty, such as future climates. Project Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies:An exploratory simulation platform will be developed in Matlab to evaluate the GS strategies developed in Specific Aim 1. We have designed several computer programs for GS simulation, which were used for the evaluation of GS algorithms. Forexample, one simulation toolcan producea diagram which shows the distributions of genomic estimated breeding values (GEBV) over 10 generations. It will also showthe loss of upper and lower potentials, respectively, of GEBV as a result of selection. Project Goal 3: Evaluate Novel GS Strategies using the Simulation Platform. All new GS algorithms were rigorously evaluated using the simulation programs. Project Goal 4: Create populations that could be used for empirical validation experiments as part of another project sometime in the future. Flowering time is an important agronomic trait for maize, as it allows the plant to adapt to a certain local environment to optimize the growing period. A selection project focusing on flowering time could help us understand not only the genetic architecture but also the causal relationship of flowering time with other traits. We created an early flowering population from 327 maize inbred lines from a diversity panel. In the summer 2020, we planted 1,400 seeds from populations from the initial cycle year and the final cycle year in a single plot and collected trait data. The collected traits included days to anthesis (DTA), days to silking (DTA), leaf appearance rate (LAR), total leaf number (TLN), plant height (PHT), number of tillers, brace root numbers, and number of nodes with brace roots. The secondary trait anthesis silking interval (ASI) was calculated by subtracting DTS from DTA. We collected tissue samples for each individual around stage V3-V4, extracted DNA, and conducted tGBS and SNP calling. We obtained 970,514 SNPs and 743,834 SNPs in the two populations, respectively. The significant response to selection (R) for DTA is -3.2 days. The indirect responses include reduced DTS (-3.6 days), TLN (-1.8 leaves), brace-root number (-2.5 roots), nodes number with brace-root (-0.26 nodes), and increased ASI (0.56 days). We identified 462 candidate genes for which there is evidence of selection, among which are previously identified flowering time and related genes. Within each of the two populations, we conducted GWAS and identified 7 SNPs significantly associated with DTA, 11 SNPs for DTS, 14 SNPs for ASI, 46 SNPs for TLN, 35 SNPs for brace-root number, and 35 SNPs for number of nodes with brace roots. Considering the nearest genes to these candidate SNPs, 138 candidate genes were identified and 4 of these overlapped with the candidate selection response genes. These populations could be used for empirical validation experiments in future research projects. Project Goal 5: Develop a user-friendly interface for our GS simulation platform:A graphical user interface will be designed and implemented to allow breeders to explore alternative GS strategies using the simulation platform developed in Project Goal2. We have designed several computer programs for GS simulation, which were used for evaluate the GS algorithms developed during the project. We also made a web-based tool for illustration and educational purposes, which allows users to select breeding parents and simulate the results. The tool is available at https://lzwang2017.github.io/MATI/.

Publications

  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Amini, F., Franco, F.R., Hu, G., and Wang, L., The look ahead trace back optimizer for genomic selection under transpar-ent and opaque simulators. Sci Rep 11, 4124 (2021). https://doi.org/10.1038/s41598-021-83567-5
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Moeinizade, S., Han, Y., Pham, H., Hu, G., and Wang, L., A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression. Sci Rep 11, 3918 (2021). https://doi.org/10.1038/s41598-021-83634-x
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Moeinizade, S. Kusmec, A., Hu,G., Wang, L., and Schnable, P., Multi-trait Genomic Selection Methods for Crop Improvement. Genetics 215, 931-945 (2020) 10.1534/genetics.120.303305, 2020.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: Kusmec, A., Zheng, Z ., Archontoulis, S.V., Ganapathysubramanian, B., Hu, G., Wang, L., Yu, J., Schnable, P.S., Interdisciplinary strategies to enable data-driven plant breeding in a changing climate. One Earth, 4, 372-383 (2021). doi:10.1016/j.oneear.2021.02.005
  • Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Fatemeh Amini, Felipe Restrepo Franco, Guiping Hu, and Lizhi Wang, Trace-back Approach In Genomic Selection, INFORMS Annual Meeting, 2020.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Saba Moeinizade, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick Schnable, "A Simulation-based Optimization Approach For Improving Response In Multi-trait Genomic Selection", INFORMS Annual Meeting, 2020.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Guiping Hu, Saba Moeinizade, and Lizhi Wang,"Reinforcement Learning Based Resource Allocation for Genomic Selection", INFORMS Annual Meeting, 2020.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Saba Moeinizade, Han Ye, Hieu Trung Pham, Guiping Hu, and Lizhi Wang, "Optimizing Parental Selection In Trait Introgression Process'', INFORMS Annual Meeting, 2020.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2020 Citation: Lizhi Wang, Descriptive, predictive, and prescriptive models for plant genomics, Invited Symposium, ASA-CSSA- SSSA Annual Meeting, 2020.


Progress 02/15/19 to 02/14/20

Outputs
Target Audience:The plant genetics community and breeders. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?The results were disseminatedto the communities via conference presentations and publications, please refer to the product section. What do you plan to do during the next reporting period to accomplish the goals?• Attend and present our work at INFORMS 2020 • We are working on another GS strategy based on active learning, which we plan to submit for journal publication during the next reporting period. • We are working on a genomic selection simulation program for educational purposes. We will compare different options, including online version (using Java Script) or off line version (using Matlab or R), and choose the most appropriate format.

Impacts
What was accomplished under these goals? Overall impact statement: We made improvement to our Matlab-based GS simulation platform and now users can evaluate different GS strategies on the platform. We shared this result with the community through our publications and presentations at major conferences. Project Goal 1: Design of Improved Genomic Selection Strategies:We will apply operations research methodology to design and optimize four distinct GS strategies to address various research needs. Nothing to report. Project Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies:An exploratory simulation platform will be developed in Matlab to evaluate the GS strategies developed in Specific Aim 1. Nothing to report. Project Goal 3: Evaluate Novel GS Strategies using the Simulation Platform. Nothing to report. Project Goal 4: We will create populations that could be used for empirical validation experiments as part of another project sometime in the future. Nothing to report. Project Goal 5: Develop a user-friendly interface for our GS simulation platform:A graphical user interface will be designed and implemented to allow breeders to explore alternative GS strategies using the simulation platform developed in Project Goal2. • The Look-ahead selection method has been published in G3: Genes Genomes Genetics. • A manuscript on Multi-trait genomic selection has been submitted to Genetics. This study is an extension from the Look-ahead selection to multi-trait scenarios. • A paper on Complementarity-Based Selection Strategy for Genomic Selection has been published in Crop Science. • Five conference presentations have been made in 2019 at INFORMS annual meeting, PAG Asia meeting. • We continue to improve a Matlab-based GS simulation platform, which allows users to test and evaluate different GS strategies. This platform was used in the above mentioned journal and conference publications to compare the new GS strategies with previous ones.

Publications

  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Saba Moeinizade, Guiping Hu, Lizhi Wang, and Patrick Schnable, Optimizing Selection and Mating in Genomic Selection with a Look-ahead Approach: An Operations Research Framework, G3: Genes|Genomes|Genetics
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Moeinizade, Saba, Megan Wellner, Guiping Hu, and Lizhi Wang. "Complementarity?based selection strategy for genomic selection." Crop Science 60, no. 1 (2020): 149-156
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Saba Moeinizade, Lizhi Wang, Guiping Hu, and Patrick Schnable, Multi-trait Selection Strategy for Genomic Selection, PAG Aisa, 2019
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Saba Moeinizade, Megan Wellner, Guiping Hu, and Lizhi Wang, Complementarity-Based Selection Strategy for Genomic Selection, PAG Aisa, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Saba Moeinizade, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick Schnable Multi-trait Genomic Selection Methods For Crop Improvement, INFORMS Annual Meeting, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Fatemeh Amini and Guiping Hu, Prediction of the Performance of Maize Hybrids Via Machine Learning Methods Using DNA Markers and Seedling Transcriptome Profiles of Parents and Offspring, INFORMS Annual Meeting, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Saba Moeinizade, Guiping Hu, and Lizhi Wang Operations Research Methods For Enhancing Efficiency In Plant Breeding, INFORMS Annual Meeting, 2019.


Progress 02/15/18 to 02/14/19

Outputs
Target Audience:The maize genetics community and breeders. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest?Wang and Hu attended and presented GS related research in the Institute for Operations Research and the Management Sciences (INFORMS) 2018 annual meeting. Luning Bi and Guiping Hu, "A Genetic Algorithm Based Deep Learning Approach to Understand Genotype-Environment Interaction (G x E)," INFORMS Data Mining workshop, 2018. Saba Moeinizade, Guiping Hu, Lizhi Wang, and Patrick Schnable, "Look-ahead Selection Strategy for Genomic Selection: Optimizing selection and Pairing Strategies with a Time-dependent Approach," INFORMS Annual Meeting, 2018. Megan Wellner, Saba Moeinizade, Guiping Hu, and Lizhi Wang, "Gender-based Selection Strategy for Genomic Selection," INFORMS Annual Meeting, 2018. What do you plan to do during the next reporting period to accomplish the goals? Attend and present our work at PAG Asia 2019 Attend and present our work at INFORMS 2019 We are working on another GS strategy, which we plan to submit for journal publication during the next reporting period.

Impacts
What was accomplished under these goals? Project Goal 1: Design of Improved Genomic Selection Strategies:We will apply operations research methodology to design and optimize four distinct GS strategies to address various research needs. Nothing to report. Project Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies:An exploratory simulation platform will be developed in Matlab to evaluate the GS strategies developed in Specific Aim 1. Nothing to report. Project Goal 3: Evaluate Novel GS Strategies using the Simulation Platform. Nothing to report. Project Goal 4: We will create populations that could be used for empirical validation experiments as part of another project sometime in the future. Nothing to report. Project Goal 5: Develop a user-friendly interface for our GS simulation platform:A graphical user interface will be designed and implemented to allow breeders to explore alternative GS strategies using the simulation platform developed in Project Goal2. We designed a new GS strategy, called Look-ahead selection. We presented this GS strategy in a journal paper, which has been accepted for publication in G3 subject to minor revisions. We have developed a Matlab-based GS simulation platform, which allows users to test and evaluate different GS strategies. This platform was used in the above mentioned journal and conference publications to compare the new GS strategies with previous ones.

Publications

  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Lizhi Wang, Guodong Zhu, Will Johnson, and Mriga Kher, "Three new approaches to genomic selection", Plant Breeding, vol. 137(5), p. 673-681, 2018.
  • Type: Journal Articles Status: Under Review Year Published: 2019 Citation: Saba Moeinizade, Guiping Hu, Lizhi Wang, and Patrick Schnable, Optimizing Selection and Mating in Genomic Selection with a Look-ahead Approach: An Operations Research Framework, under review, G3


Progress 02/15/17 to 02/14/18

Outputs
Target Audience:The maize genetics community and breeders. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The graduate student working on this project is receving training in conducting research and collaborating with colleagues in a different research area. How have the results been disseminated to communities of interest?Journal publications and conference proceedings (see Products) Conference presentations The Institute for Operations Research and the Management Sciences meeting, 2017 Institute of Industrial and Systems Engineers annual meeting, 2017 Plant and Animal Genome, 2017 What do you plan to do during the next reporting period to accomplish the goals? Design new and more efficient algorithms. Develop more comprehensive simulation experiments to test algorithms. Disseminate results with more journal and conference publications and conference presentations.

Impacts
What was accomplished under these goals? IMPACT STATEMENT: As a consequence of growing populations, changing diets, and the challenges of climate change agricultural systems must produce more with less.Breeding is a key tool for making agriculture systems more productive, sustainable and resilient.Genomic selection (GS) has the potential to increase the rate of genetic gain in breeding programs and is therefore transforming breeding programs.GS includes three steps: genomic prediction, selection and mating. Most research on GS has focused on improving the first step, genomic prediction. Our preliminary simulation studies have, however, shown that the success of GS is sensitive to both the selection and mating steps. Hence, this project uses the framework of operations research to optimize selection and mating parameters for specific types of breeding programs. Our research is increasing knowledge of how to enhance the efficiency of breeding programs by introducing engineering processes.In addition, this project is training graduate students at the intersection of operations research and plant breeding, helping to develop "next generation plant breeders." Goal1: Design of Improved Genomic Selection Strategies. We designed three versions of genomic selection algorithms. The first one is the OPV (optimal population value) approach, which was published in Genetics in 2017. The second one is the gender-based algorithm, which was published in a conference proceeding. The first author of this conference paper is an undergraduate student, and she has been invited to present in the Institute of Industrial and Systems Engineers annual meeting as one of three finalists of an undergraduate research competition, which will take place in May 2018. The third algorithm is the look-ahead selection, which was published in a conference proceeding and will be extended as a journal paper. Goal 2: Develop an Exploratory Simulation Platform to Evaluate Novel GS Strategies. The look-ahead selection algorithm mentioned above also applies to this specific goal. Goal 3: Evaluate Novel GS Strategies using the Simulation Platform. Nothing to report this period. Goal 4: Create populations that could be used for empirical validation experiments. Nothing to report this period. Goal 5: Develop a user-friendly interface for our GS simulation platform. Nothing to report this period.

Publications

  • Type: Journal Articles Status: Published Year Published: 2017 Citation: Matt Goiffon, Aaron Kusmec, Lizhi Wang, Guiping Hu, and Patrick Schnable, "Improving Response in Genomic Selection with Optimal Population Value Selection: a Population-Based Selection Strategy," vol. 206 no. 3, 1675-1682, Genetics, 2017.
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2018 Citation: Megan Wellner, Saba Moeinizade, Yulu Chen, Guiping Hu, and Lizhi Wang, Gender-based Selection Strategy for Genomic Selection, Proceedings of IISE 2018.
  • Type: Conference Papers and Presentations Status: Submitted Year Published: 2018 Citation: Saba Moeinizade, Lizhi Wang, and Guiping Hu, Improving Response in Genomic Selection with a look-ahead approach, Proceedings of IISE 2018.
  • Type: Journal Articles Status: Under Review Year Published: 2018 Citation: Lizhi Wang, Guodong Zhu, Will Jonson, Mriga Kher, Three new algorithms for genomic selection,