An Integrative Genetics Approach to Identify Candidate Genes Regulating BMD: Combining Linkage, Gene Expression, and Association

Authors

  • Charles R Farber,

    Corresponding author
    1. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
    • Address reprint requests to: Charles R Farber, PhD, Department of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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  • Atila van Nas,

    1. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Anatole Ghazalpour,

    1. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Jason E Aten,

    1. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Sudheer Doss,

    1. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Brandon Sos,

    1. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Eric E Schadt,

    1. Rosetta Inpharmatics/Merck, Seattle, Washington, USA
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    • Dr Schadt is an employee of Rosetta Inpharmatics, a wholly owned subsidiary of Merck and Co. All other authors state that they have no conflicts of interest.

  • Leslie Ingram-Drake,

    1. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Richard C Davis,

    1. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Steve Horvath,

    1. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
    2. Department of Biostatistics, School of Public Health, University of California, Los Angeles, California, USA
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  • Desmond J Smith,

    1. Molecular and Medical Pharmacology, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Thomas A Drake,

    1. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
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  • Aldons J Lusis

    1. Department of Medicine, David Geffen School of Medicine at University of California, Los Angeles, California, USA
    2. Department of Human Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
    3. Deptartment of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine at University of California, Los Angeles, California, USA
    4. Molecular Biology Institute, UCLA, Los Angeles, California, USA
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Abstract

Numerous quantitative trait loci (QTLs) affecting bone traits have been identified in the mouse; however, few of the underlying genes have been discovered. To improve the process of transitioning from QTL to gene, we describe an integrative genetics approach, which combines linkage analysis, expression QTL (eQTL) mapping, causality modeling, and genetic association in outbred mice. In C57BL/6J × C3H/HeJ (BXH) F2 mice, nine QTLs regulating femoral BMD were identified. To select candidate genes from within each QTL region, microarray gene expression profiles from individual F2 mice were used to identify 148 genes whose expression was correlated with BMD and regulated by local eQTLs. Many of the genes that were the most highly correlated with BMD have been previously shown to modulate bone mass or skeletal development. Candidates were further prioritized by determining whether their expression was predicted to underlie variation in BMD. Using network edge orienting (NEO), a causality modeling algorithm, 18 of the 148 candidates were predicted to be causally related to differences in BMD. To fine-map QTLs, markers in outbred MF1 mice were tested for association with BMD. Three chromosome 11 SNPs were identified that were associated with BMD within the Bmd11 QTL. Finally, our approach provides strong support for Wnt9a, Rasd1, or both underlying Bmd11. Integration of multiple genetic and genomic data sets can substantially improve the efficiency of QTL fine-mapping and candidate gene identification.

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