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A new statistic and its power to infer membership in a genome-wide association study using genotype frequencies

Nature Genetics volume 41, pages 12531257 (2009) | Download Citation


Aggregate results from genome-wide association studies (GWAS)1,2,3, such as genotype frequencies for cases and controls, were until recently often made available on public websites4,5 because they were thought to disclose negligible information concerning an individual's participation in a study. Homer et al.6 recently suggested that a method for forensic detection of an individual's contribution to an admixed DNA sample could be applied to aggregate GWAS data. Using a likelihood-based statistical framework, we developed an improved statistic that uses genotype frequencies and individual genotypes to infer whether a specific individual or any close relatives participated in the GWAS and, if so, what the participant's phenotype status is. Our statistic compares the logarithm of genotype frequencies, in contrast to that of Homer et al.6, which is based on differences in either SNP probe intensity or allele frequencies. We derive the theoretical power of our test statistics and explore the empirical performance in scenarios with varying numbers of randomly chosen or top-associated SNPs.

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Author information

Author notes

    • Stephen J Chanock
    •  & Nilanjan Chatterjee

    These authors contributed equally to this work.


  1. Core Genotyping Facility, Advanced Technology Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, Maryland, USA.

    • Kevin B Jacobs
    •  & Meredith Yeager
  2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

    • Kevin B Jacobs
    • , Meredith Yeager
    • , Sholom Wacholder
    • , Margaret Tucker
    • , Robert N Hoover
    • , Gilles D Thomas
    • , Stephen J Chanock
    •  & Nilanjan Chatterjee
  3. BioInformed LLC, Gaithersburg, Maryland, USA.

    • Kevin B Jacobs
  4. Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, Arizona, USA.

    • David Craig
  5. Program in Molecular and Genetic Epidemiology, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA.

    • Peter Kraft
    •  & David J Hunter
  6. National Center for Biotechnology Information, National Library of Medicine, Bethesda, Maryland, USA.

    • Justin Paschal
  7. Office of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA.

    • Teri A Manolio


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K.B.J. and N.C. devised the statistical methods; K.B.J. implemented these methods in software and applied them to simulated and empirical data; K.B.J., M.Y. and S.J.C. drafted the article; S.W. and P.K. made important suggestions to the analytic plan and aided in the interpretation of the results; D.C. and J.P. aided in the study design and verification of methodology; D.J.H., T.A.M., M.T., R.N.H. and G.D.T. participated in revising the manuscript and made important intellectual contributions. All the authors and reviewed and approved the final manuscript.

Corresponding author

Correspondence to Kevin B Jacobs.

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