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A generalized robust allele‐based genetic association test
Author(s) -
Zhang Lin,
Sun Lei
Publication year - 2022
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13456
Subject(s) - covariate , test statistic , allele , statistics , allele frequency , sample size determination , mathematics , type i and type ii errors , statistic , econometrics , statistical hypothesis testing , genetics , biology , gene
The allele‐based association test, comparing allele frequency difference between case and control groups, is locally most powerful. However, application of the classical allelic test is limited in practice, because the method is sensitive to the Hardy–Weinberg equilibrium (HWE) assumption, not applicable to continuous traits, and not easy to account for covariate effect or sample correlation. To develop a generalized robust allelic test, we propose a new allele‐based regression model with individual allele as the response variable. We show that the score test statistic derived from this robust and unifying regression framework contains a correction factor that explicitly adjusts for potential departure from HWE and encompasses the classical allelic test as a special case. When the trait of interest is continuous, the corresponding allelic test evaluates a weighted difference between individual‐level allele frequency estimate and sample estimate where the weight is proportional to an individual's trait value, and the test remains valid under Y ‐dependent sampling. Finally, the proposed allele‐based method can analyze multiple (continuous or binary) phenotypes simultaneously and multiallelic genetic markers, while accounting for covariate effect, sample correlation, and population heterogeneity. To support our analytical findings, we provide empirical evidence from both simulation and application studies.