R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations

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Abstract

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely-used R/qtl software package to include multiparental populations, better handles modern high-dimensional data....

Abstract

R/qtl2 is an interactive software environment for mapping quantitative trait loci (QTL) in experimental populations. The R/qtl2 software expands the scope of the widely used R/qtl software package to include multiparent populations derived from more than two founder strains, such as the Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations. R/qtl2 is designed to handle modern high-density genotyping data and high-dimensional molecular phenotypes, including gene expression and proteomics. R/qtl2 includes the ability to perform genome scans using a linear mixed model to account for population structure, and also includes features to impute SNPs based on founder strain genomes and to carry out association mapping. The R/qtl2 software provides all of the basic features needed for QTL mapping, including graphical displays and summary reports, and it can be extended through the creation of add-on packages. R/qtl2, which is free and open source software written in the R and C++ programming languages, comes with a test framework.

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Most cited references 35

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

C S Haley, S Knott (1992)

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.

Eric S. Lander, D Botstein, E Lander … (1989)

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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High-resolution genetic mapping using the Mouse Diversity outbred population.

Karen Svenson, Daniel M. Gatti, William Valdar … (2012)

The JAX Diversity Outbred population is a new mouse resource derived from partially inbred Collaborative Cross strains and maintained by randomized outcrossing. As such, it segregates the same allelic variants as the Collaborative Cross but embeds these in a distinct population architecture in which each animal has a high degree of heterozygosity and carries a unique combination of alleles. Phenotypic diversity is striking and often divergent from phenotypes seen in the founder strains of the Collaborative Cross. Allele frequencies and recombination density in early generations of Diversity Outbred mice are consistent with expectations based on simulations of the mating design. We describe analytical methods for genetic mapping using this resource and demonstrate the power and high mapping resolution achieved with this population by mapping a serum cholesterol trait to a 2-Mb region on chromosome 3 containing only 11 genes. Analysis of the estimated allele effects in conjunction with complete genome sequence data of the founder strains reduced the pool of candidate polymorphisms to seven SNPs, five of which are located in an intergenic region upstream of the Foxo1 gene.

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Author and article information

Journal

Journal ID (nlm-ta): Genetics

Journal ID (iso-abbrev): Genetics

Journal ID (hwp): genetics

Journal ID (pmc): genetics

Journal ID (publisher-id): genetics

Title: Genetics

Publisher: Genetics Society of America

ISSN (Print): 0016-6731

ISSN (Electronic): 1943-2631

Publication date (Print): February 2019

Publication date (Electronic): 5 February 2019

Publication date PMC-release: 5 February 2019

Volume: 211

Issue: 2

Pages: 495-502

Affiliations

[* ]Departments of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin 53706

[‡‡ ]Horticulture Department, University of Wisconsin–Madison, Wisconsin 53706

[§§ ]Statistics, University of Wisconsin–Madison, Wisconsin 53706

[† ]The Jackson Laboratory, Bar Harbor, Maine 04609

[‡ ]23andMe, Mountain View, California 94043

[§ ]Departments of Genetics, Genomics, and Informatics, University of Tennessee Health Sciences Center, Memphis, Tennessee 38163

[†† ]Preventive Medicine, University of Tennessee Health Sciences Center, Memphis, Tennessee 38163

[** ]Center for Molecular Medicine, University Medical Center Utrecht, 3584CT, The Netherlands

Author notes

[1 ]Corresponding author: Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, 2126 Genetics-Biotechnology Center, 425 Henry Mall, Madison, WI 53706. E-mail: broman@ 123456wisc.edu

Author information

Karl W. Broman http://orcid.org/0000-0002-4914-6671

Daniel M. Gatti http://orcid.org/0000-0003-0667-9926

Petr Simecek http://orcid.org/0000-0002-2922-7183

Nicholas A. Furlotte http://orcid.org/0000-0002-9096-6276

Pjotr Prins http://orcid.org/0000-0002-8021-9162

Śaunak Sen http://orcid.org/0000-0003-4519-6361

Brian S. Yandell http://orcid.org/0000-0002-8774-9377

Gary A. Churchill http://orcid.org/0000-0001-9190-9284

Article

Publisher ID: 301595

DOI: 10.1534/genetics.118.301595

PMC ID: 6366910

PubMed ID: 30591514

SO-VID: 2ee6cfd5-a7a6-4301-b70e-77ed7262953f

License:

Available freely online through the author-supported open access option.

This is an open-access article 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 the original work is properly cited.

History

Date received : 11 September 2018

Date accepted : 21 December 2018

Page count

Figures: 2, Tables: 0, Equations: 0, References: 63, Pages: 8

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ScienceOpen disciplines: Genetics

Keywords: software,qtl,multiparent populations,magic,diversity outbred mice,heterogeneous stock,collaborative cross,multiparent advanced generation inter-cross (magic),mpp

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ScienceOpen disciplines: Genetics

Keywords: software, qtl, multiparent populations, magic, diversity outbred mice, heterogeneous stock, collaborative cross, multiparent advanced generation inter-cross (magic), mpp

R/qtl2: Software for Mapping Quantitative Trait Loci with High-Dimensional Data and Multiparent Populations

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Most cited references 35

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

Mapping mendelian factors underlying quantitative traits using RFLP linkage maps.

High-resolution genetic mapping using the Mouse Diversity outbred population.

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