Genome-wide association of multiple complex traits in outbred mice by ultra-low-coverage sequencing

Journal name:
Nature Genetics
Volume:
48,
Pages:
912–918
Year published:
DOI:
doi:10.1038/ng.3595
Received
Accepted
Published online

Abstract

Two bottlenecks impeding the genetic analysis of complex traits in rodents are access to mapping populations able to deliver gene-level mapping resolution and the need for population-specific genotyping arrays and haplotype reference panels. Here we combine low-coverage (0.15×) sequencing with a new method to impute the ancestral haplotype space in 1,887 commercially available outbred mice. We mapped 156 unique quantitative trait loci for 92 phenotypes at a 5% false discovery rate. Gene-level mapping resolution was achieved at about one-fifth of the loci, implicating Unc13c and Pgc1a at loci for the quality of sleep, Adarb2 for home cage activity, Rtkn2 for intensity of reaction to startle, Bmp2 for wound healing, Il15 and Id2 for several T cell measures and Prkca for bone mineral content. These findings have implications for diverse areas of mammalian biology and demonstrate how genome-wide association studies can be extended via low-coverage sequencing to species with highly recombinant outbred populations.

At a glance

Figures

  1. Sequence diversity of the CFW population.
    Figure 1: Sequence diversity of the CFW population.

    (a) Distribution of heterozygosity in 100-kb windows across the genome. (b) Histogram of genome-wide heterozygosity. (c) Example of new and total SNP density for a region of chromosome 19. Results are representative of those seen across the genome. (d) MAF density for populations of wild Indian mice (n = 10; 44.9 million whole-genome sequencing SNPs), CFW mice (n = 2,073; 5.7 million imputed SNPs) and HS mice (n = 1,904; 11,000 SNPs from a genotyping array). Known CFW variation refers to variants also segregating among 14 sequenced classical inbred strains. (e) The extent of LD in CFW and HS mice. Values are mean LD r2 values for all pairs of SNPs binned by distance.

  2. Mapping resolution and effect size of QTLs.
    Figure 2: Mapping resolution and effect size of QTLs.

    (a,b) Frequency distributions of the size (a) and number of genes present (b) for the 95% confidence interval (CI) of 255 QTLs. (c) The sum of the variance explained by QTLs plotted against heritability for 92 measures where heritability could be estimated and at least one QTL was detected. The color of the dots corresponds to the type of measure: behavior, physiological (body weight, respiratory, electrocardiography) or tissue (any measure obtained after dissection).

  3. Summary porcupine plot for 92 phenotypes.
    Figure 3: Summary porcupine plot for 92 phenotypes.

    Genome-wide representation of all unique QTLs (n = 156, FDR < 5%) identified in this study. Light and dark gray dots show associations for the 92 measures where at least one QTL was detected at tagging SNP positions (n = 359,559). The most significant SNP at each QTL is marked by a colored dot, with the color corresponding to the type of measure. The y axis shows the −log10 P values for association of the imputed allele dosages with tested measures and is truncated at −log10 P = 32. The positions of the two strongest QTLs with −log10 P values of 133 (chromosome 4) and 76 (chromosome 17) are marked by triangles.

  4. Single-gene-resolution mapping at four loci using the entire set of SNPs (7.1 million).
    Figure 4: Single-gene-resolution mapping at four loci using the entire set of SNPs (7.1 million).

    (a) Weight of soleus muscle on chromosome 6 (n = 1,832). (b) Measure of the number of long sleep episodes on chromosome 9 (n = 1,577). (c) Ratio of CD3+CD4+ to CD3+CD8+ cells on chromosome 8 (n = 1,324). (d) Intensity of reaction to startle on chromosome 10 (n = 1,740). The strongest associated SNP for each region is marked with a purple diamond, and the other SNPs that passed post-imputation quality control (IMPUTE2-style INFO scores >0.4 and Hardy–Weinberg equilibrium P > 1 × 10−6) are colored according to LD r2 with the strongest SNP. The gray dots represent SNPs that failed post-imputation quality control and therefore were not used for the analysis.

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Primary accessions

European Nucleotide Archive

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Author information

Affiliations

  1. Wellcome Trust Centre for Human Genetics, Oxford, UK.

    • Jérôme Nicod,
    • Robert W Davies,
    • Na Cai,
    • Leo Goodstadt,
    • Nasrin Bopp,
    • Amarjit Bhomra,
    • Polinka Hernandez-Pliego,
    • Richard Mott &
    • Jonathan Flint
  2. Mary Lyon Centre, MRC Harwell, Harwell Science and Innovation Campus, Harwell, UK.

    • Carl Hassett,
    • Tertius Hough,
    • Russell Joynson,
    • Hayley Phelps,
    • Barbara Nell,
    • Clare Rowe,
    • Joe Wood,
    • Alison Walling,
    • Mark Harrison,
    • Martin Fray,
    • Tom Weaver &
    • Sara Wells
  3. Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, Oxford, UK.

    • Cormac Cosgrove &
    • Paul Klenerman
  4. Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Hong Kong, China.

    • Benjamin K Yee
  5. School of Medicine, Medical Sciences and Nutrition, College of Life Sciences and Medicine, University of Aberdeen, Aberdeen, UK.

    • Vikte Lionikaite,
    • Jennifer S Gregory,
    • Richard M Aspden &
    • Arimantas Lionikas
  6. Wellcome Trust Sanger Institute, Hinxton, UK.

    • Rebecca E McIntyre &
    • David J Adams
  7. Heart Center, Department of Clinical and Experimental Cardiology, Academic Medical Center, Amsterdam, the Netherlands.

    • Carol Ann Remme,
    • Elisabeth M Lodder,
    • Yigal M Pinto &
    • Connie R Bezzina
  8. Department of Biochemistry, AP-HP, Hôpital Lariboisière, INSERM U942, Paris, France.

    • Jacques Callebert &
    • Jean-Marie Launay
  9. Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.

    • Nick P Talbot &
    • Peter A Robbins
  10. Department of Biobehavioral Health, Pennsylvania State University, University Park, Pennsylvania, USA.

    • David A Blizard
  11. Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.

    • Paul Franken
  12. Mammalian Genetics Unit, MRC Harwell, Harwell Science and Innovation Campus, Harwell, UK.

    • Steve D M Brown &
    • Paul K Potter
  13. UCL Genetics Institute, University College London, London, UK.

    • Richard Mott
  14. Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, USA.

    • Jonathan Flint

Contributions

J.N. and J.F. designed the study and experiments. J.N., B.K.Y. and N.C. processed data. C.C., R.E.M., N.B., A.B., P.H.-P., C.H., R.J., H.P., B.N., C.R. and T.H. phenotyped the mice and generated data. J.W. and A.W. developed bespoke Laboratory Information Management System (LIMS) and bioinformatics solutions for data collection. M.H. and M.F. managed importation and isolation procedures of mice into the Mary Lyon Centre. S.W., T.W. and S.D.M.B. provided infrastructure and staff, and established the phenotyping within the MLC. P.K.P. and J.N. managed the project. V.L., J.S.G. and R.M.A. quantified bone size and mineral content. D.A.B. and A.L. acquired skeletal muscle phenotypes. C.A.R., E.M.L., Y.M.P. and C.R.B. supervised cardiac data acquisition and analyzed the cardiac data. J.C. and J.-M.L. quantified serotonin. J.N., C.C., R.E.M., P.F., B.K.Y., D.J.A., P.K., N.P.T., P.A.R. and A.L. analyzed the phenotypic data. N.C. and L.G. processed the sequencing data. R.W.D. and L.G. performed genotype imputation. R.M., J.N., N.C. and J.F. performed the genetic analysis. J.N., R.W.D., N.C., R.M. and J.F. wrote the manuscript with input from co-authors.

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PDF files

  1. Supplementary Text and Figures (2,961 KB)

    Supplementary Figures 1–4, Supplementary Tables 2, 4, 6, and 7, and Supplementary Note.

Excel files

  1. Supplementary Table 1 (44,535 KB)

    List of phenotypes measured in the outbred mice.

  2. Supplementary Table 3 (44,993 KB)

    Quality control data for genotypes derived from low-pass sequencing.

  3. Supplementary Table 5 (92,447 KB)

    List of 255 QTLs mapped in the outbred mice.

Additional data