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Model-Free Linkage Analysis of a Binary Trait

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Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1666))

Abstract

Genetic linkage analysis aims to detect chromosomal regions containing genetic variants that influence risk of specific inherited diseases. The presence of linkage is indicated when a disease or trait cosegregates through the families with genetic markers at a particular region of the genome. Two main types of genetic linkage analysis are in common use, namely model-based linkage analysis and model-free linkage analysis. In this chapter, we focus solely on the latter type and specifically on binary traits or phenotypes, such as the presence or absence of a specific disease. Model-free linkage analysis is based on allele-sharing, where patterns of genetic similarity among affected relatives are compared to chance expectations. Because the model-free methods do not require the specification of the inheritance parameters of a genetic model, they are preferred by many researchers at early stages in the study of a complex disease. We introduce the history of model-free linkage analysis in Subheading 1. Table 1 describes a standard model-free linkage analysis workflow. We describe three popular model-free linkage analysis methods, the nonparametric linkage (NPL) statistic, the affected sib-pair (ASP) likelihood ratio test, and a likelihood approach for pedigrees. The theory behind each linkage test is described in this section together with a simple example of the relevant calculations. Table 4 provides a summary of popular genetic analysis software packages that implement model-free linkage models. In Subheading 2, we work through the methods on a rich example providing sample software code and output. Subheading 3 contains notes with additional details on various topics that may need further consideration during analysis.

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Acknowledgments

We acknowledge the support of research grants from the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada, and the Canadian Network of Centres of Excellence in Mathematics (MITACS Inc.).

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Authors and Affiliations

  1. Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, M5G 2M9

    Wei Xu

  2. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada

    Jin Ma

  3. Lady Davis Research Institute, Jewish General Hospital, Montréal, QC, Canada, H3T 1E2

    Celia M. T. Greenwood

  4. Department of Oncology and Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, QC, Canada, H4A 3T2 and H3A 1A2

    Celia M. T. Greenwood

  5. Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada, M5G 0A4

    Andrew D. Paterson

  6. Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 60 Murray St., Box 18, Toronto, ON, Canada, M5T 3L9

    Shelley B. Bull

  7. Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada, M5T 3M7

    Wei Xu, Andrew D. Paterson & Shelley B. Bull

Authors
  1. Wei Xu
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  2. Jin Ma
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  3. Celia M. T. Greenwood
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  4. Andrew D. Paterson
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  5. Shelley B. Bull
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Corresponding author

Correspondence to Shelley B. Bull .

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Editors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Robert C. Elston

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Xu, W., Ma, J., Greenwood, C.M.T., Paterson, A.D., Bull, S.B. (2017). Model-Free Linkage Analysis of a Binary Trait. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_17

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  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_17

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7273-9

  • Online ISBN: 978-1-4939-7274-6

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