Article | Published:

A weighted burden test using logistic regression for integrated analysis of sequence variants, copy number variants and polygenic risk score

European Journal of Human Geneticsvolume 27pages114124 (2019) | Download Citation

Abstract

Previously described methods of analysis allow variants in a gene to be weighted more highly according to rarity and/or predicted function and then for the variant contributions to be summed into a gene-wise risk score, which can be compared between cases and controls using a t-test. However, this does not allow incorporating covariates into the analysis. Schizophrenia is an example of an illness where there is evidence that different kinds of genetic variation can contribute to risk, including common variants contributing to a polygenic risk score (PRS), very rare copy number variants (CNVs) and sequence variants. A logistic regression approach has been implemented to compare the gene-wise risk scores between cases and controls, while incorporating as covariates population principal components, the PRS and the presence of pathogenic CNVs and sequence variants. A likelihood ratio test is performed, comparing the likelihoods of logistic regression models with and without this score. The method was applied to an ethnically heterogeneous exome-sequenced sample of 6000 controls and 5000 schizophrenia cases. In the raw analysis, the test statistic is inflated but inclusion of principal components satisfactorily controls for this. In this dataset, the inclusion of the PRS and effect from CNVs and sequence variants had only small effects. The set of genes which are FMRP targets showed some evidence for enrichment of rare, functional variants among cases (p = 0.0005). This approach can be applied to any disease in which different kinds of genetic and non-genetic risk factors make contributions to risk.

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Acknowledgements

The datasets used for the analysis described in this manuscript were obtained from dbGaP at http://www.ncbi.nlm.nih.gov/gap through dbGaP accession number phs000473.v2.p2. Samples used for data analysis were provided by the Swedish Cohort Collection supported by the NIMH grant R01MH077139, the Sylvan C. Herman Foundation, the Stanley Medical Research Institute and The Swedish Research Council (grants 2009–4959 and 2011–4659). Support for the exome sequencing was provided by the NIMH Grand Opportunity grant RCMH089905, the Sylvan C. Herman Foundation, a grant from the Stanley Medical Research Institute and multiple gifts to the Stanley Center for Psychiatric Research at the Broad Institute of MIT and Harvard.

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Affiliations

  1. Centre for Psychiatry, Barts and the London School of Medicine and Dentistry, Charterhouse Square, London, EC1M 6BQ, UK

    • David Curtis
  2. UCL Genetics Institute, UCL, Darwin Building, Gower Street, London, WC1E 6BT, UK

    • David Curtis

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The authors declare that they have no conflict of interest.

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Correspondence to David Curtis.

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DOI

https://doi.org/10.1038/s41431-018-0272-6