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


    Connor, Gregory (2016) Adjusted p-values for genome-wide regression analysis with non-normally distributed quantitative phenotypes. Working Paper. Department of Economics, Finance & Accounting Working Paper N274-16. (Unpublished)

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    Abstract

    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 association (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 fit observed phenotypic variables, and derive exact small-sample p-values for the standard GWA regression under this distributional assumption.

    Item Type: Monograph (Working Paper)
    Keywords: Adjusted p-values; genome; regression analysis; quantitative phenotypes;
    Academic Unit: Faculty of Social Sciences > Economics, Finance and Accounting
    Item ID: 7473
    Depositing User: Ms Sandra Doherty
    Date Deposited: 28 Sep 2016 15:37
    Publisher: Department of Economics, Finance & Accounting Working Paper N274-16
    URI:

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