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Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs

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Statistical Methods in Molecular Biology

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

Abstract

The analysis of genome-wide association studies (GWAS) poses statistical hurdles that have to be handled efficiently in order for the study to be successful. The two largest impediments in the analysis phase of the study are the multiple comparisons problem and maintaining robustness against confounding due to population admixture and stratification. For quantitative traits in family-based designs, Van Steen (1) proposed a two-stage testing strategy that can be considered a hybrid approach between family-based and population-based analysis. By including the population-based component into the family-based analysis, the Van Steen algorithm maximizes the statistical power, while at the same time, maintains the original robustness of family-based association tests (FBATs) (2–4). The Van Steen approach consists of two statistically independent steps, a screening step and a testing step. For all genotyped single nucleotide polymorphisms (SNPs), the screening step examines the evidence for association at a population-based level. Based on support for a potential genetic association from the screening step, the SNPs are prioritized for testing in the next step, where they are analyzed with a FBAT (3). By exploiting population-based information in the screening step that is not utilized in family-based association testing step, the two steps are statistically independent. Therefore, the use of the population-based data for the purposes of screening does not bias the FBAT statistic calculated in the testing step. Depending on the trait type and the ascertainment conditions, Van Steen-type testing strategies can achieve statistical power levels that are comparable to those of population-based studies with the same number of probands. In this chapter, we review the original Van Steen algorithm, its numerous extensions, and discuss its advantages and disadvantages.

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Authors and Affiliations

  1. Channing Laboratory, Center for Genomic Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Amy Murphy & Scott T. Weiss

  2. Center for Genomic Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Christoph Lange

  3. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Christoph Lange

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  1. Amy Murphy
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  2. Scott T. Weiss
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  3. Christoph Lange
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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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Murphy, A., T. Weiss, S., Lange, C. (2010). Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs. 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_17

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  • DOI: https://doi.org/10.1007/978-1-60761-580-4_17

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