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While FBAT can perform univariate or multivariate association tests, EIGENSTRAT can only perform univariate tests.
Another feature of the proposed method that can improve statistical power is the ability to perform multivariate association tests.
For genetic association studies, methods have also been proposed for performing multivariate association tests on unrelated samples [43] and on families [44], separately.
In this study, we propose a simple approach to perform univariate or multivariate association tests when correcting for population stratification with data generated by combining unrelated samples and nuclear families.
In this study, by extending generalized estimating equations (GEEs), we propose a simple approach for performing univariate or multivariate association tests for the combined data of unrelated subjects and nuclear families.
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Ancestry-informative transcripts were identified using a rank-score-based multivariate association test, and ancestry-informative eQTL were identified using Fisher's exact test.
Results: Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals.
Previous work has contributed to addressing association tests for multivariate phenotypes.
Quantitative data were analysed using descriptive statistics, association tests, and a multivariate analysis using logistic regression.
It implements three types of multivariate tests that encompass all commonly used association tests for rare variants.
In summary, we have developed a simple and novel method for performing univariate/multivariate association tests while correcting for population stratification, in samples combining nuclear families and unrelated subjects.
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