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      Maximizing the potential of multi-parental crop populations

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          Abstract

          Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations.

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          An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

          Vincent Segura, Bjarni J. Vilhjalmsson, Alexander Platt … (2012)
          Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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            Diversity arrays: a solid state technology for sequence information independent genotyping.

            D Jaccoud, K. Peng, D Feinstein … (2001)
            Here we present the successful application of the microarray technology platform to the analysis of DNA polymorphisms. Using the rice genome as a model, we demonstrate the potential of a high-throughput genome analysis method called Diversity Array Technology, DArT'. In the format presented here the technology is assaying for the presence (or amount) of a specific DNA fragment in a representation derived from the total genomic DNA of an organism or a population of organisms. Two different approaches are presented: the first involves contrasting two representations on a single array while the second involves contrasting a representation with a reference DNA fragment common to all elements of the array. The Diversity Panels created using this method allow genetic fingerprinting of any organism or group of organisms belonging to the gene pool from which the panel was developed. Diversity Arrays enable rapid and economical application of a highly parallel, solid-state genotyping technology to any genome or complex genomic mixtures.
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              Genome-wide genetic association of complex traits in heterogeneous stock mice.

              William Valdar, Leah Solberg, Dominique Gauguier … (2006)
              Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.
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                Author and article information

                Contributors
                Alison R. Bentley
                Journal
                Journal ID (nlm-ta): Appl Transl Genom
                Journal ID (iso-abbrev): Appl Transl Genom
                Title: Applied & Translational Genomics
                Publisher: Elsevier
                ISSN (Electronic): 2212-0661
                Publication date PMC-release: 26 October 2016
                Publication date Collection: December 2016
                Publication date (Electronic): 26 October 2016
                Volume: 11
                Pages: 9-17
                Affiliations
                [a ]The John Bingham Laboratory, NIAB, Huntingdon Road, Cambridge CB3 0LE, United Kingdom
                [b ]Department of Plant Sciences, The University of Cambridge, Downing Street, Cambridge CB2 3EA, United Kingdom
                [c ]The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Research Centre, Midlothian EH25 9RG, United Kingdom
                Author notes
                [* ]Corresponding author. alison.bentley@ 123456niab.com
                Article
                Publisher ID: S2212-0661(16)30032-1
                DOI: 10.1016/j.atg.2016.10.002
                PMC ID: 5167364
                PubMed ID: 28018845
                SO-VID: 0836b3eb-881b-4bfc-a4b9-98773104647a
                Copyright © © 2016 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 15 July 2016
                Date revision received : 22 October 2016
                Date accepted : 24 October 2016
                Categories
                Subject: Special section on Crop genomics and food security guest edited by Nigel G. Halford

                Keywords: mapping,trait dissection,wheat,magic,nam
                Data availability:
                Keywords: mapping, trait dissection, wheat, magic, nam

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