Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

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Abstract

Background

The limited genetic diversity of elite maize germplasms raises concerns about the potential to breed for new challenges. Initiatives have been formed over the years to identify and utilize useful diversity from landraces to overcome this issue. The aim of this study was to evaluate the proposed designs to initiate a pre-breeding program within the Seeds of Discovery (SeeD) initiative with emphasis on harnessing polygenic variation from landraces using genomic selection. We evaluated these designs with stochastic simulation to provide decision support about the effect of several design factors on the quality of resulting (pre-bridging) germplasm. The evaluated design factors were: i) the approach to initiate a pre-breeding program from the selected landraces, doubled haploids of the selected landraces, or testcrosses of the elite hybrid and selected landraces, ii) the genetic parameters of landraces and phenotypes, and iii) logistical factors related to the size and management of a pre-breeding program.

Results

The results suggest a pre-breeding program should be initiated directly from landraces. Initiating from testcrosses leads to a rapid reconstruction of the elite donor genome during further improvement of the pre-bridging germplasm. The analysis of accuracy of genomic predictions across the various design factors indicate the power of genomic selection for pre-breeding programs with large genetic diversity and constrained resources for data recording. The joint effect of design factors was summarized with decision trees with easy to follow guidelines to optimize pre-breeding efforts of SeeD and similar initiatives.

Conclusions

Results of this study provide guidelines for SeeD and similar initiatives on how to initiate pre-breeding programs that aim to harness polygenic variation from landraces.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2345-z) contains supplementary material, which is available to authorized users.

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Most cited references 54

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An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

Carolin Strobl, James Malley, Gerhard Tutz (2009)

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.

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Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Hans D Daetwyler, Beatriz Villanueva, John A. Woolliams (2008)

Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.

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Introduction to Quantitative Genetics

Douglas Scott Falconer (1989)

This is an introductory textbook with the emphasis on general principles rather than on practical applications. It covers a range of topics in genetics, including mutation, and this edition seeks to include the developments of the 20 years since the first edition and to provide more material on plants. Though the mathematics does not go beyond simple algebra (neither calculus nor matrix methods are used), the author does assume a knowledge of statistics, particularly of the analysis of variance and of correlation and regression. separately, at the end of the relevant chapter. Solutions are provided.

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Author and article information

Contributors

Gregor Gorjanc:

ORCID: http://orcid.org/0000-0001-8008-2787

gregor.gorjanc@bf.uni-lj.si , gregor.gorjanc@roslin.ed.ac.uk

Janez Jenko: janez.jenko@kis.si , janez.jenko@roslin.ed.ac.uk

Sarah J. Hearne: s.hearne@cgiar.org

John M. Hickey: john.hickey@roslin.ed.ac.uk

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 5 January 2016

Publication date PMC-release: 5 January 2016

Publication date Collection: 2016

Volume: 17

Electronic Location Identifier: 30

Affiliations

[ ]Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia

[ ]The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, Scotland UK

[ ]Agricultural Institute of Slovenia, 1000 Ljubljana, Slovenia

[ ]Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Apdo, 06600 México, D.F. México

Author information

Gregor Gorjanc http://orcid.org/0000-0001-8008-2787

Article

Publisher ID: 2345

DOI: 10.1186/s12864-015-2345-z

PMC ID: 4702314

PubMed ID: 26732811

SO-VID: 293256f5-d96f-4e25-8451-f161d225f33b

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 14 October 2015

Date accepted : 21 December 2015

Funding

Funded by: Seeds of Discovery Project

Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council (GB);

Award ID: ISP to The Roslin Institute

Award Recipient : John M. Hickey

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: maize,landrace,diversity,pre-breeding,genomic selection

Data availability:

ScienceOpen disciplines: Genetics

Keywords: maize, landrace, diversity, pre-breeding, genomic selection

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Initiating maize pre-breeding programs using genomic selection to harness polygenic variation from landrace populations

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Genomic Prediction

Most cited references 54

An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Introduction to Quantitative Genetics

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Cited by 46

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