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Nonparametric Methods for Molecular Biology

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Statistical Methods in Molecular Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 620))

Abstract

In 2003, the completion of the Human Genome Project (1) together with advances in computational resources (2) were expected to launch an era where the genetic and genomic contributions to many common diseases would be found. In the years following, however, researchers became increasingly frustrated as most reported ‘findings’ could not be replicated in independent studies (3). To improve the signal/noise ratio, it was suggested to increase the number of cases to be included to tens of thousands (4), a requirement that would dramatically restrict the scope of personalized medicine. Similarly, there was little success in elucidating the gene–gene interactions involved in complex diseases or even in developing criteria for assessing their phenotypes. As a partial solution to these enigmata, we here introduce a class of statistical methods as the ‘missing link’ between advances in genetics and informatics. As a first step, we provide a unifying view of a plethora of nonparametric tests developed mainly in the 1940s, all of which can be expressed as u-statistics. Then, we will extend this approach to reflect categorical and ordinal relationships between variables, resulting in a flexible and powerful approach to deal with the impact of (1) multiallelic genetic loci, (2) poly-locus genetic regions, and (3) oligo-genetic and oligo-genomic collaborative interactions on complex phenotypes.

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Acknowledgments

The work was supported in part by Grant No. UL1RR024143 from the U.S. National Center for Research Resources (NCRR). Of the many colleagues who have contributed to this chapter through discussions and suggestions, I would like to thank, in particular, Jose F. Morales, Ephraim Sehayek, Sreeram Ramagopalan, and Martina Durner for their input on the biological background, Sreeram Ramagopalan, Bill Raynor, and Norman Cliff for their helpful comments, an anonymous reviewer for an inspiring discussion, and Daniel Eckardt for help with Latin grammar.

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Authors and Affiliations

  1. Center for Clinical and Translational Science, The Rockefeller University, New York, NY, USA

    Knut M. Wittkowski & Tingting Song

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  1. Knut M. Wittkowski
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  2. Tingting Song
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Editors and Affiliations

  1. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Heejung Bang

  2. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Xi Kathy Zhou

  3. Journal of Experimental Medicine, Rockefeller University Press, First Ave. 1114, New York, 10021, New York, USA

    Heather L. van Epps

  4. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Madhu Mazumdar

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Wittkowski, K.M., Song, T. (2010). Nonparametric Methods for Molecular Biology. In: Bang, H., Zhou, X., van Epps, H., Mazumdar, M. (eds) Statistical Methods in Molecular Biology. Methods in Molecular Biology, vol 620. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-580-4_2

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  • DOI: https://doi.org/10.1007/978-1-60761-580-4_2

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