Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates

Authors

  • Desmond D. Campbell,

    1. Department of Biostatistics, Institute of Psychiatry, King's College London, London, United Kingdom
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  • Pak C. Sham,

    Corresponding author
    1. Department of Psychiatry, State Key Laboratory of Brain and Cognitive Sciences, and Genome Research Centre, University of Hong Kong, Hong Kong, China
    • Room L10-69, Laboratory Block, Genome Research Centre, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China
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  • Jo Knight,

    1. Department of Medical and Molecular Genetics, School of Medicine, King's College London, Guy's Hospital, London, United Kingdom
    2. National Institute for Health Research (NIHR), Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
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  • Harvey Wickham,

    1. Institute of Psychiatry, King's College London, London, United Kingdom
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  • Sabine Landau

    1. Department of Biostatistics, Institute of Psychiatry, King's College London, London, United Kingdom
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Abstract

For many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation. Genet. Epidemiol. 34: 159–170, 2010. © 2009 Wiley-Liss, Inc.

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