Family-Based Association Tests with longitudinal measurements: handling missing data.
Several family-based approaches have been previously proposed to enhance the power for testing genetic association when the traits are measured longitudinally or repeatedly. In this paper, we show that some of these FBAT approaches can be easily extended to accommodate incomplete data and remain unbiased tests. We also show that because of the nature of FBAT approaches, we can impute the missing phenotypes without biasing our tests and achieve higher power. We propose two imputation techniques based on E-M algorithm and the conditional mean model, respectively. Through simulation studies, these two imputation techniques are shown to have correct false positive rate and generally achieve higher power than complete case analysis or simple mean-imputation. Application of these approaches for testing an association between Body Mass Index and a previously reported candidate SNP confirms our results.
Department of Biostatistics, Harvard School of Public Health, Boston, Mass., USA. xiao.ding@post.harvard.edu
Xiao
X
Ding
Nan
N
Laird
D000465:Algorithms; D015992:Body Mass Index; D005190:Family; D006801:Humans; D008137:Longitudinal Studies; D008962:Models, Theoretical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide