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Single Marker Association Analysis for Unrelated Samples

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Statistical Human Genetics

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

Abstract

Methods for single marker association analysis are presented for binary and quantitative traits. For a binary trait, we focus on the analysis of retrospective case–control data using Pearson’s chi-squared test, the trend test and a robust test. For a continuous trait, typical methods are based on a linear regression model or the analysis of variance. We illustrate how these tests can be applied using a publicly available R package “Rassoc” and some existing R functions. Guidelines for single-marker analysis are provided.

G. Zheng (deceased)

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Author information

Authors and Affiliations

  1. Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 4000 Reservoir Rd. NW, Washington, DC, 20057, USA

    Ao Yuan

  2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

    Qizhai Li

  3. Department of Statistics, George Washington University, Washington, DC, 20892, USA

    Joseph L. Gastwirth

Authors
  1. Gang Zheng
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  2. Ao Yuan
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  3. Qizhai Li
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  4. Joseph L. Gastwirth
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Corresponding author

Correspondence to Ao Yuan .

Editor information

Editors and Affiliations

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

    Robert C. Elston

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© 2017 Springer Science+Business Media LLC

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Zheng, G., Yuan, A., Li, Q., Gastwirth, J.L. (2017). Single Marker Association Analysis for Unrelated Samples. 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_18

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  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_18

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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

  • eBook Packages: Springer Protocols

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