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
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 (www.SAS.com) for generating WVF residuals that can be input to the current versions of the FBAT (www.biostat.harvard.edu/fbat/fbat.html) and PBAT (www.biostat.harvard.edu/clange/default.htm) software is provided.
- "While model choice can affect power (Lange and Laird (2002); Lange, DeMeo and Laird (2002)), choice of the wrong disease model does not affect robustness, as the test is conditioned on the trait. When samples are selected on the basis of the disease trait, as is generally the case with dichotomous traits, the nuisance parameters cannot be estimated from the data; methods for specifying E(Y ) have been suggested (Lunetta et al. (2000); Lange and Laird (2002); Lu and Cantor (2007); Dudbridge (2008)). "
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ABSTRACT: Genome-Wide Association Studies (GWAS) offer an exciting and promising new research avenue for finding genes for complex diseases. Traditional case-control and cohort studies offer many advantages for such designs. Family-based association designs have long been attractive for their robustness properties, but robustness can mean a loss of power. In this paper we discuss some of the special features of family designs and their relevance in the era of GWAS. Comment: Published in at http://dx.doi.org/10.1214/08-STS280 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Available from: Scott Weiss
- "Failure to adjust for confounders and other covariates can greatly diminish the efficiency of genetic association studies. Traditional regression methods that control for confounders often apply directly to genetic association studies, and these techniques have been extended and adapted in settings where this is not the case.[1-3] Despite this, researchers conducting genetic association studies of quantitative traits do not always take full advantage of their ability to adjust for important covariates. "
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ABSTRACT: Genetic association studies of complex traits often rely on standardised quantitative phenotypes, such as percentage of predicted forced expiratory volume and body mass index to measure an underlying trait of interest (eg lung function, obesity). These phenotypes are appealing because they provide an easy mechanism for comparing subjects, although such standardisations may not be the best way to control for confounders and other covariates. We recommend adjusting raw or standardised phenotypes within the study population via regression. We illustrate through simulation that optimal power in both population- and family-based association tests is attained by using the residuals from within-study adjustment as the complex trait phenotype. An application of family-based association analysis of forced expiratory volume in one second, and obesity in the Childhood Asthma Management Program data, illustrates that power is maintained or increased when adjusted phenotype residuals are used instead of typical standardised quantitative phenotypes.
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