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      Quantitative Trait Locus Mapping Methods for Diversity Outbred Mice

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          Abstract

          Genetic mapping studies in the mouse and other model organisms are used to search for genes underlying complex phenotypes. Traditional genetic mapping studies that employ single-generation crosses have poor mapping resolution and limit discovery to loci that are polymorphic between the two parental strains. Multiparent outbreeding populations address these shortcomings by increasing the density of recombination events and introducing allelic variants from multiple founder strains. However, multiparent crosses present new analytical challenges and require specialized software to take full advantage of these benefits. Each animal in an outbreeding population is genetically unique and must be genotyped using a high-density marker set; regression models for mapping must accommodate multiple founder alleles, and complex breeding designs give rise to polygenic covariance among related animals that must be accounted for in mapping analysis. The Diversity Outbred (DO) mice combine the genetic diversity of eight founder strains in a multigenerational breeding design that has been maintained for >16 generations. The large population size and randomized mating ensure the long-term genetic stability of this population. We present a complete analytical pipeline for genetic mapping in DO mice, including algorithms for probabilistic reconstruction of founder haplotypes from genotyping array intensity data, and mapping methods that accommodate multiple founder haplotypes and account for relatedness among animals. Power analysis suggests that studies with as few as 200 DO mice can detect loci with large effects, but loci that account for <5% of trait variance may require a sample size of up to 1000 animals. The methods described here are implemented in the freely available R package DOQTL.

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          A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

          The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
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            Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

            The advent of complete genetic linkage maps consisting of codominant DNA markers [typically restriction fragment length polymorphisms (RFLPs)] has stimulated interest in the systematic genetic dissection of discrete Mendelian factors underlying quantitative traits in experimental organisms. We describe here a set of analytical methods that modify and extend the classical theory for mapping such quantitative trait loci (QTLs). These include: (i) a method of identifying promising crosses for QTL mapping by exploiting a classical formula of SEWALL WRIGHT; (ii) a method (interval mapping) for exploiting the full power of RFLP linkage maps by adapting the approach of LOD score analysis used in human genetics, to obtain accurate estimates of the genetic location and phenotypic effect of QTLs; and (iii) a method (selective genotyping) that allows a substantial reduction in the number of progeny that need to be scored with the DNA markers. In addition to the exposition of the methods, explicit graphs are provided that allow experimental geneticists to estimate, in any particular case, the number of progeny required to map QTLs underlying a quantitative trait.
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              Chapter 11: Genome-Wide Association Studies

              Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                1 September 2014
                September 2014
                : 4
                : 9
                : 1623-1633
                Affiliations
                [* ]The Jackson Laboratory, Bar Harbor, Maine 04609
                []Medical College of Virginia of Virginia Commonwealth University, Richmond, Virginia 23298
                []Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
                [§ ]Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
                [†† ]Department of Human Genetics, University of Chicago, Chicago, Illinois 60637
                [‡‡ ]Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706
                [** ]Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 0200, Australia
                Author notes
                [1 ]Corresponding author: 600 Main St., Bar Harbor, ME 04609. E-mail: gary.churchill@ 123456jax.org
                Article
                GGG_013748
                10.1534/g3.114.013748
                4169154
                25237114
                eff31026-9ae3-48fc-871f-2b3e88fd306c
                Copyright © 2014 Gatti et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Unported License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 February 2014
                : 28 July 2014
                Page count
                Pages: 11
                Categories
                Multiparental Populations
                Custom metadata
                v1

                Genetics
                diversity outbred,haplotype reconstruction,quantitative trait locus mapping,multiparent advanced generation inter-cross (magic),multiparental populations,mpp

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