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      The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population

      research-article
      Collaborative Cross Consortium
      Genetics
      Genetics Society of America

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          Abstract

          The Collaborative Cross Consortium reports here on the development of a unique genetic resource population. The Collaborative Cross (CC) is a multiparental recombinant inbred panel derived from eight laboratory mouse inbred strains. Breeding of the CC lines was initiated at multiple international sites using mice from The Jackson Laboratory. Currently, this innovative project is breeding independent CC lines at the University of North Carolina (UNC), at Tel Aviv University (TAU), and at Geniad in Western Australia (GND). These institutions aim to make publicly available the completed CC lines and their genotypes and sequence information. We genotyped, and report here, results from 458 extant lines from UNC, TAU, and GND using a custom genotyping array with 7500 SNPs designed to be maximally informative in the CC and used a novel algorithm to infer inherited haplotypes directly from hybridization intensity patterns. We identified lines with breeding errors and cousin lines generated by splitting incipient lines into two or more cousin lines at early generations of inbreeding. We then characterized the genome architecture of 350 genetically independent CC lines. Results showed that founder haplotypes are inherited at the expected frequency, although we also consistently observed highly significant transmission ratio distortion at specific loci across all three populations. On chromosome 2, there is significant overrepresentation of WSB/EiJ alleles, and on chromosome X, there is a large deficit of CC lines with CAST/EiJ alleles. Linkage disequilibrium decays as expected and we saw no evidence of gametic disequilibrium in the CC population as a whole or in random subsets of the population. Gametic equilibrium in the CC population is in marked contrast to the gametic disequilibrium present in a large panel of classical inbred strains. Finally, we discuss access to the CC population and to the associated raw data describing the genetic structure of individual lines. Integration of rich phenotypic and genomic data over time and across a wide variety of fields will be vital to delivering on one of the key attributes of the CC, a common genetic reference platform for identifying causative variants and genetic networks determining traits in mammals.

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          Most cited references27

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          Systems Genetics of Complex Traits in Drosophila melanogaster

          SUMMARY Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically inter-correlated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression, and transcription factor binding sites. The high transcriptional connectivity allows us to infer genetic networks and the function of predicted genes based on annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provides insight into the molecular basis of pleiotropy between complex traits.
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            Sequence-based characterization of structural variation in the mouse genome.

            Structural variation is widespread in mammalian genomes and is an important cause of disease, but just how abundant and important structural variants (SVs) are in shaping phenotypic variation remains unclear. Without knowing how many SVs there are, and how they arise, it is difficult to discover what they do. Combining experimental with automated analyses, we identified 711,920 SVs at 281,243 sites in the genomes of thirteen classical and four wild-derived inbred mouse strains. The majority of SVs are less than 1 kilobase in size and 98% are deletions or insertions. The breakpoints of 160,000 SVs were mapped to base pair resolution, allowing us to infer that insertion of retrotransposons causes more than half of SVs. Yet, despite their prevalence, SVs are less likely than other sequence variants to cause gene expression or quantitative phenotypic variation. We identified 24 SVs that disrupt coding exons, acting as rare variants of large effect on gene function. One-third of the genes so affected have immunological functions.
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              A method for fine mapping quantitative trait loci in outbred animal stocks.

              High-resolution mapping of quantitative trait loci (QTL) in animals has proved to be difficult because the large effect sizes detected in crosses between inbred strains are often caused by numerous linked QTLs, each of small effect. In a study of fearfulness in mice, we have shown it is possible to fine map small-effect QTLs in a genetically heterogeneous stock (HS). This strategy is a powerful general method of fine mapping QTLs, provided QTLs detected in crosses between inbred strains that formed the HS can be reliably detected in the HS. We show here that single-marker association analysis identifies only two of five QTLs expected to be segregating in the HS and apparently limits the strategy's usefulness for fine mapping. We solve this problem with a multipoint analysis that assigns the probability that an allele descends from each progenitor in the HS. The analysis does not use pedigrees but instead requires information about the HS founder haplotypes. With this method we mapped all three previously undetected loci [chromosome (Chr.) 1 logP 4.9, Chr. 10 logP 6.0, Chr. 15 logP 4.0]. We show that the reason for the failure of single-marker association to detect QTLs is its inability to distinguish opposing phenotypic effects when they occur on the same marker allele. We have developed a robust method of fine mapping QTLs in genetically heterogeneous animals and suggest it is now cost effective to undertake genomewide high-resolution analysis of complex traits in parallel on the same set of mice.
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                Author and article information

                Journal
                Genetics
                Genetics
                genetics
                genetics
                genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                February 2012
                February 2012
                February 2012
                : 190
                : 2
                : 389-401
                Author notes
                [1]

                Tel Aviv University, Ramat Aviv, Tel Aviv 69978, Israel: Fuad A. Iraqi, Mustafa Mahajne, Yasser Salaymah, Hani Sandovski, Hanna Tayem, and Karin Vered; Geniad, Ltd., University of Western Australia, and Animal Resources Centre, Australia: Lois Balmer, Michael Hall, Glynn Manship, Grant Morahan, Ken Pettit, Jeremy Scholten, Kathryn Tweedie, Andrew Wallace, and Lakshini Weerasekera; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom: James Cleak, Caroline Durrant, Leo Goodstadt, Richard Mott and Binnaz Yalcin; University of North Carolina, Chapel Hill, NC 27599: David L. Aylor, Ralph S. Baric, Timothy A. Bell, Katharine M. Bendt, Jennifer Brennan, Jackie D. Brooks, Ryan J. Buus, James J. Crowley, John D. Calaway, Mark E. Calaway, Agnieszka Cholka, David B. Darr, John P. Didion, Amy Dorman, Eric T. Everett, Martin T. Ferris, Wendy Foulds Mathes, Chen-Ping Fu, Terry J. Gooch, Summer G. Goodson, Lisa E. Gralinski, Stephanie D. Hansen, Mark T. Heise, Jane Hoel, Kunjie Hua, Mayanga C. Kapita, Seunggeun Lee, Alan B. Lenarcic, Eric Yi Liu, Hedi Liu, Leonard McMillan, Terry R. Magnuson, Kenneth F. Manly, Darla R. Miller, Deborah A. O'Brien, Fanny Odet, Isa Kemal Pakatci, Wenqi Pan, Fernando Pardo-Manuel de Villena 2 , Charles M. Perou, Daniel Pomp, Corey R. Quackenbush, Nashiya N. Robinson, Norman E. Sharpless, Ginger D. Shaw, Jason S. Spence, Patrick F. Sullivan, Wei Sun, Lisa M. Tarantino, William Valdar, Jeremy Wang, Wei Wang, Catherine E. Welsh, Alan Whitmore, Tim Wiltshire, Fred A. Wright, Yuying Xie, Zaining Yun, Vasyl Zhabotynsky, Zhaojun Zhang, and Fei Zou; North Carolina State University, Raleigh, NC 27695: Christine Powell, Jill Steigerwalt, and David W. Threadgill; The Jackson Laboratory, Bar Harbor, ME 04607: Elissa J. Chesler, Gary A. Churchill, Daniel M. Gatti, Ron Korstanje, and Karen L. Svenson; National Institutes of Health, Bethesda, MD 20892: Francis S. Collins, Nigel Crawford, Kent Hunter, Samir N. P. Kelada, Bailey C. E. Peck, Karlyne Reilly, and Urraca Tavarez; Oregon Health and Science University, Portland, OR 97239: Daniel Bottomly, Robert Hitzeman, and Shannon K. McWeeney; University of Arizona, Tucson, AZ 85719: Jeffrey Frelinger, Harsha Krovi, and Jason Phillippi; University of Colorado Denver, Denver, CO: Richard A. Spritz; University of Washington, Seattle, WA 98195: Lauri Aicher, Michael Katze, and Elizabeth Rosenzweig; Faculty of Dental Medicine, Hadassah Medical Centers and The Hebrew University, Jerusalem, Israel: Ariel Shusterman, Aysar Nashef, Ervin I. Weiss, and Yael Houri-Haddad; Hebrew University, Jerusalem, Israel: Morris Soller; University of Tennessee Health Science Center, Memphis, TN 38163: Robert W. Williams; Helmholtz Centre for Infection Research & University of Veterinary Medicine Hannover, Braunschweig, Germany: Klaus Schughart; Duke University, Durham, NC 27710: Hyuna Yang; National Institute of Environmental Health Sciences, National Toxicology Program, Research Triangle Park, NC 27709: John E. French; University of Nebraska-Lincoln, Lincoln, NE 68583: Andrew K. Benson, Jaehyoung Kim, Ryan Legge, Soo Jen Low, Fangrui Ma, Ines Martinez, and Jens Walter; University of Wisconsin-Madison, Madison, WI 53706: Karl W. Broman; The Alberta Children's Hospital Research Institute, University of Calgary, 3330 Hospital Dr. NW, Calgary, Alberta T2N 4N1, Canada: Benedikt Hallgrimsson; University of California San Francisco, San Francisco, CA 94143: Ophir Klein; The Genome Institute at Washington University, St. Louis, MO 63108: George Weinstock and Wesley C. Warren; University of Colorado School of Medicine, Denver, CO 80206: Yvana V. Yang and David Schwartz.

                [2 ]Corresponding author: Department of Genetics, 5046 Genetics Medicine Bldg., University of North Carolina, Campus Box 7264, Chapel Hill, NC, 27599. E-mail: fernando@ 123456med.unc.edu
                Article
                132639
                10.1534/genetics.111.132639
                3276630
                22345608
                aa0f792f-9409-45a5-abf1-3f20fb013209
                Copyright © 2012 by the Genetics Society of America

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

                History
                : 11 July 2011
                : 3 October 2011
                Categories
                Mouse Genetic Resources
                Custom metadata
                v1

                Genetics
                Genetics

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