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Working Paper
Gregory Connor and Michael O'Neill
2016
October
Finite-sample genome-wide regression p-values with a non-normally distributed phenotype
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Unpublished
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()
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This paper derives the exact finite-sample p-value for univariate regression of a quantitative phenotype on individual genome markers, relying on a mixture distribution for the dependent variable. The p-value estimator conventionally used in existing genome-wide association study (GWAS) regressions assumes a normally-distributed dependent variable, or relies on a central limit theorem based approximation. The central limit theorem approximation is unreliable for GWAS regression p-values, and measured phenotypes often have markedly non-normal distributions. A normal mixture distribution better fits observed phenotypic variables, and we provide exact small sample p-values for the standard GWAS regressions under this flexible distributional assumption. We illustrate the adjustment using a years-of-education phenotypic variable.
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