Weighted variance FBAT: a powerful method for including covariates in FBAT analyses

Department of Human Genetics, David Geffen School of Medicine, University of California at Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA.
Genetic Epidemiology (Impact Factor: 2.6). 05/2007; 31(4):327-37. DOI: 10.1002/gepi.20213
Source: PubMed


The family-based association test (FBAT), an extension of transmission/disequilibrium test, capitalizes on linkage disequilibrium to assess the association of genetic markers and traits in nuclear families. It does not permit a formal inclusion of covariates, although an offset under the FBAT -o option allows for an overall trait-intercept adjustment. The PBAT software provides additional features and permits the inclusion of covariates in the FBAT test statistic, but does not account for the parental genotype information when the traits are adjusted for the covariates. We propose the weighted variance FBAT (WVF) method to generate trait values adjusted for both parental genotypes and covariate values. WVF is expected to be more powerful, because the variance is minimized considering both of these factors simultaneously using a weighted Gauss-Newton algorithm. Two simulated parent/child trio data sets, both with a covariate and the second with a gene by covariate interaction, were simulated to compare WVF power with FBAT and PBAT for a quantitative trait. WVF is most powerful when levels of significance are greater and covariates have a larger influence, indicating WVF may be especially effective when multiple comparisons are an important consideration, such as with whole genome association studies. WVF will also improve the cost of an association study when environmental covariates are considered. A SAS program ( for generating WVF residuals that can be input to the current versions of the FBAT ( and PBAT ( software is provided.

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