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Model-Based Linkage Analysis of a Quantitative Trait

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Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 850))

Abstract

Linkage analysis is a family-based method of analysis to examine whether any typed genetic markers co-segregate with a given trait, in this case a quantitative trait. If linkage exists, this is taken as evidence in support of a genetic basis for the trait. Historically, linkage analysis was performed using a binary disease trait, but has been extended to include quantitative disease measures. Quantitative traits are desirable as they provide more information than binary traits. Linkage analysis can be performed using single marker methods (one marker at a time) or multipoint (using multiple markers simultaneously). In model-based linkage analysis, the genetic model for the trait of interest is specified. There are many software options for performing linkage analysis. Here, we use the program package Statistical Analysis for Genetic Epidemiology (S.A.G.E.). S.A.G.E. was chosen because it includes programs to perform data cleaning procedures and to generate and test genetic models for a quantitative trait, in addition to performing linkage analysis. We demonstrate in detail the process of running the program LODLINK to perform single marker analysis and MLOD to perform multipoint analysis using output from SEGREG, where SEGREG was used to determine the best fitting statistical model for the trait.

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Acknowledgment

This work was supported by a US Public Health Service Resource grant (RR03655) from the National Center for Research Resources.

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Correspondence to Audrey H. Schnell .

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© 2012 Springer Science+Business Media, LLC

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Schnell, A.H., Sun, X. (2012). Model-Based Linkage Analysis of a Quantitative Trait. In: Elston, R., Satagopan, J., Sun, S. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 850. Humana Press. https://doi.org/10.1007/978-1-61779-555-8_14

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  • DOI: https://doi.org/10.1007/978-1-61779-555-8_14

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-554-1

  • Online ISBN: 978-1-61779-555-8

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