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Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests

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Abstract

For genome-wide association studies in family-based designs, a new, universally applicable approach is proposed. Using a modified Liptak’s method, we combine the p-value of the family-based association test (FBAT) statistic with the p-value for the Van Steen-statistic. The Van Steen-statistic is independent of the FBAT-statistic and utilizes information that is ignored by traditional FBAT-approaches. The new test statistic takes advantages of all available information about the genetic association, while, by virtue of its design, it achieves complete robustness against confounding due to population stratification. The approach is suitable for the analysis of almost any trait type for which FBATs are available, e.g. binary, continuous, time-to-onset, multivariate, etc. The efficiency and the validity of the new approach depend on the specification of a nuisance/tuning parameter and the weight parameters in the modified Liptak’s method. For different trait types and ascertainment conditions, we discuss general guidelines for the optimal specification of the tuning parameter and the weight parameters. Our simulation experiments and an application to an Alzheimer study show the validity and the efficiency of the new method, which achieves power levels that are comparable to those of population-based approaches.

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Authors and Affiliations

  1. Department of Statistics, Chung-Ang University, Seoul, Republic of Korea

    Sungho Won

  2. Research Center for Data Science, Chung-Ang University, Seoul, Republic of Korea

    Sungho Won

  3. Massachusetts General Hospital, Charlestown, MA, USA

    Lars Bertram & Rudolph E. Tanzi

  4. Pathway Genomics, San Diego, CA, USA

    David Becker

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

    Christoph Lange

  6. Harvard Medical School, Boston, MA, USA

    Christoph Lange

  7. Center for Genomic Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Christoph Lange

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  1. Sungho Won
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  2. Lars Bertram
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  3. David Becker
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  4. Rudolph E. Tanzi
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  5. Christoph Lange
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Correspondence to Christoph Lange.

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Won, S., Bertram, L., Becker, D. et al. Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests. Stat Biosci 1, 125–143 (2009). https://doi.org/10.1007/s12561-009-9016-z

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  • DOI: https://doi.org/10.1007/s12561-009-9016-z

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