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A high-resolution association mapping panel for the dissection of complex traits in mice
Author(s) -
Brian J. Bennett,
Charles R. Farber,
Luz D. Orozco,
Hyun Min Kang,
Anatole Ghazalpour,
Nathan O. Siemers,
Michael Neubauer,
Isaac Neuhaus,
Roumyana Yordanova,
Bo Guan,
Amy Truong,
Wen-Pin Yang,
Aiqing He,
Paul S. Kayne,
Peter S. Gargalovic,
Todd G. Kirchgessner,
Calvin Pan,
Lawrence W. Castellani,
Emrah Kostem,
Nicholas A. Furlotte,
Thomas A. Drake,
Eleazar Eskin,
Aldons J. Lusis
Publication year - 2010
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.099234.109
Subject(s) - quantitative trait locus , biology , association mapping , family based qtl mapping , genetics , gene mapping , computational biology , population , phenotype , inbred strain , genetic linkage , linkage (software) , inclusive composite interval mapping , genetic association , gene , evolutionary biology , genotype , single nucleotide polymorphism , chromosome , demography , sociology
Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

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