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A method for identifying genetic heterogeneity within phenotypically defined disease subgroups

Nature Genetics volume 49, pages 310–316 (2017)Cite this article

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Abstract

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.

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Figure 1: Overview of the three-category model.
Figure 2: Quantile–quantile plot from simulations demonstrating type 1 error rate control of the PLR test.
Figure 3: Observed absolute Za and Zd scores for T1D–RA comparison.
Figure 4: The power of PLR testing to reject H0 (genetic homogeneity between case subgroups) depends on the number of SNPs in category 3 and the underlying values of model parameters σ2, σ3, τ, and ρ.
Figure 5: Za and Zd scores for age at diagnosis in T1D, excluding the MHC region.

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Acknowledgements

We acknowledge the help of the Diabetes and Inflammation Laboratory Data Service for access and quality control procedures on the data sets used in this study. The JDRF/Wellcome Trust Diabetes and Inflammation Laboratory is in receipt of a Wellcome Trust Strategic Award (107212; J.A.T.) and receives funding from the JDRF (grant 5-SRA-2015-130-A-N; J.A.T.) and the NIHR Cambridge Biomedical Research Centre. The research leading to these results has received funding from the European Union's Seventh Framework Programme (grant FP7/2007-2013; J.A.T.) under grant agreement 241447 (NAIMIT). J.L. is funded by the NIHR Cambridge Biomedical Research Centre and is on the Wellcome Trust PhD program in Mathematical Genomics and Medicine at the University of Cambridge. C.W. is funded by the Wellcome Trust (grants 089989 and 107881) and the MRC (grant MC_UP_1302/5). The Cambridge Institute for Medical Research (CIMR) is in receipt of a Wellcome Trust Strategic Award (100140). We thank M. Simmonds, S. Gough, J. Franklyn, and O. Brand for sharing their AITD genetic association data set and all patients with AITD and control subjects for participating in this study. The AITD UK national collection was funded by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Medical Genetics, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK

    James Liley, John A Todd & Chris Wallace

  2. Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, UK

    James Liley & Chris Wallace

  3. Nuffield Department of Medicine, Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    John A Todd

  4. MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge, UK.,

    Chris Wallace

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  1. James Liley
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Contributions

J.L. conceived the statistical methods, wrote the software, performed the analyses, analyzed the data, and wrote the manuscript. J.A.T. analyzed the results and edited the manuscript. C.W. conceived the study, analyzed the data, and wrote the manuscript.

Corresponding authors

Correspondence to James Liley or Chris Wallace.

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Competing interests

The JDRF-Wellcome Trust Diabetes and Inflammation Laboratory receives funding from Hoffmann La Roche and Eli Lilly and Company.

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Supplementary Figures 1–9, Supplementary Tables 1–9 and Supplementary Note (PDF 9727 kb)

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Liley, J., Todd, J. & Wallace, C. A method for identifying genetic heterogeneity within phenotypically defined disease subgroups. Nat Genet 49, 310–316 (2017). https://doi.org/10.1038/ng.3751

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