Model-Free Linkage Analysis of a Binary Trait

  • Wei Xu
  • Jin Ma
  • Celia M. T. Greenwood
  • Andrew D. Paterson
  • Shelley B. Bull
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)


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.

Key words

Genetic linkage analysis Nonparametric linkage (NPL) score Identity by descent (IBD) sharing Affected relative pairs (ARP) Affection status Likelihood ratio-based linkage model Pedigree structure Kong and Cox model Genetic heterogeneity GENEHUNTER MERLIN S.A.G.E. 



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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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Wei Xu
    • 1
    • 7
  • Jin Ma
    • 2
  • Celia M. T. Greenwood
    • 3
    • 4
  • Andrew D. Paterson
    • 5
    • 7
  • Shelley B. Bull
    • 6
    • 7
  1. 1.Department of Biostatistics, Princess Margaret Cancer CentreUniversity Health NetworkTorontoCanada
  2. 2.Lunenfeld-Tanenbaum Research Institute, Sinai Health SystemTorontoCanada
  3. 3.Lady Davis Research InstituteJewish General HospitalMontréalCanada
  4. 4.Department of Oncology and Department of Epidemiology, Biostatistics & Occupational HealthMcGill UniversityQCCanada
  5. 5.Genetics and Genome BiologyThe Hospital for Sick ChildrenTorontoCanada
  6. 6.Lunenfeld-Tanenbaum Research Institute, Sinai Health SystemTorontoCanada
  7. 7.Dalla Lana School of Public HealthUniversity of TorontoTorontoCanada

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