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-Adjusted p-values for genome-wide regression analysis with non-normally distributed quantitative phenotypes


  • Gregory Connor

    () (Department of Economics, Finance and Accounting, Maynooth University.)

  • Michael O?Neill

    (School of Business, University College, Dublin)


This paper provides a small-sample adjustment for Bonferonni- corrected p-values in multiple univariate regressions of a quantitative phenotype (such as a social trait) on individual genome markers. The p-value estimator conventionally used in existing genome-wide asso- ciation (GWA) regressions assumes a normally-distributed dependent variable, or relies on a central limit theorem based approximation. We show that the central limit theorem approximation is unreliable for GWA regression Bonferonni-corrected p-values except in very large samples. We note that measured phenotypes (particularly in the case of social traits) often have markedly non-normal distributions. We propose a mixed normal distribution to better ?t observed pheno- typic variables, and derive exact small-sample p-values for the stan- dard GWA regression under this distributional assumption.

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  • Gregory Connor & Michael O?Neill, 2016. "-Adjusted p-values for genome-wide regression analysis with non-normally distributed quantitative phenotypes," Economics, Finance and Accounting Department Working Paper Series n274-16.pdf, Department of Economics, Finance and Accounting, National University of Ireland - Maynooth.
  • Handle: RePEc:may:mayecw:n274-16.pdf

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