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Cis-acting expression quantitative trait loci in mice

    • 1 Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
    • 2 Department of Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
    • 3 Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
    • 4 Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California 90095, USA
    • 5 Molecular Biology Institute, UCLA, Los Angeles, California 90095, USA
    • 6 Rosetta Inpharmatics, A wholly owned subsidiary of Merck & Co., Inc., Kirkland, Washington 98034, USA
Published April 18, 2005. Vol 15 Issue 5, pp. 681-691. https://doi.org/10.1101/gr.3216905
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cover of Genome Research Vol 36 Issue 4
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Abstract

We previously reported the analysis of genome-wide expression profiles and various diabetes-related traits in a segregating cross between inbred mouse strains C57BL/6J (B6) and DBA/2J (DBA). By considering transcript levels as quantitative traits, we identified several thousand expression quantitative trait loci (eQTL) with LOD score >4.3. We now experimentally address the problem of multiple comparisons by estimating the fraction of false-positive eQTL that are under cis-acting regulation. For this, we have utilized a classic cis–trans test with (B6 × DBA)F1 mice to determine the relative levels of transcripts from the B6 and DBA alleles. The results suggest that at least 64% of cis-acting eQTL with LOD >4.3 are true positives, while the remaining 36% could not be confirmed as truly cis-acting. Moreover, we find that >96% of apparent cis-acting eQTL occur in regions that do not share SNP haplotypes. Cis-acting eQTL serve as an important new resource for the identification of positional candidates in QTL studies in mice. Also, we use the analysis of the correlation structures between genotypes, gene expression traits, and phenotypic traits to further characterize genes expressed in liver that are under cis-acting control, and highlight the advantages and disadvantages of integrating genetics and gene expression data in segregating populations.

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