Abstract
Knowledge of genes influencing longitudinal patterns may offer information about predicting disease progression. We developed a systematic procedure for testing association between SNP genotypes and longitudinal phenotypes. We evaluated false positive rates and statistical power to localize genes for disease progression. We used genome-wide SNP data from the Framingham Heart Study. With longitudinal data from two real studies unrelated to Framingham, we estimated three trajectory curves from each study. We performed simulations by randomly selecting 500 individuals. In each simulation replicate, we assigned each individual to one of the three trajectory groups based on the underlying hypothesis (null or alternative), and generated corresponding longitudinal data. Individual Bayesian posterior probabilities (BPPs) for belonging to a specific trajectory curve were estimated. These BPPs were treated as a quantitative trait and tested (using the Wald test) for genome-wide association. Empirical false positive rates and power were calculated. Our method maintained the expected false positive rate for all simulation models. Also, our method achieved high empirical power for most simulations. Our work presents a method for disease progression gene mapping. This method is potentially clinically significant as it may allow doctors to predict disease progression based on genotype and determine treatment accordingly.
Appendix
The appendix for this work may be freely downloaded at: http://linkage.rockefeller.edu/derek/Londono-et-al-LongitudinalDataAnalysis-GWAS-SAGMB-Appendix.pdf
The authors gratefully acknowledge Dr. Glen Satten (Center for Disease Control) and Dr. Steven Buyske (Rutgers University), each of whom suggested use of the posterior probability of being in the fast trajectory group as being the quantitative trait for the GWAS. Data for the scoliosis project was funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH), the Crystal Charity Ball, the Scoliosis Research Society, the Cain Foundation, and the TSRHC Research Fund (to C.A.W.). Data for the mouse alcohol consumption was supported in part by grants from the National Institutes of Health of the United States (DA013471 and DA020555).
The authors also gratefully acknowledge The Genetic Analysis Workshop (GAW). GAW has been continuously funded since 1982 by the National Institute of General Medical Sciences (NIGMS), through grant R01 GM031575 to Drs. Jean MacCluer and Laura Almasy.
Finally, the authors are most grateful to The Framingham Heart Study for use of their data. The Framingham Heart Study is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with Boston University (Contract No. N01-HC-25195). This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.
Author contributions
DL, KC, AM, and DG equally conceived of all study designs, methods, and statistical analyses, with expert counsel from SJF (longitudinal data analysis issues) and TCM (genetic data issues). RW and TS assisted in the application of the longitudinal data analyses, and shared research they performed regarding this subject matter. JB, JAH, and CAW provided all scoliosis data, and provided expert interpretation of scoliosis disease trajectories. HZ, MJ, and LY provided all mouse alcohol consumption data, having previously performed all experiments on the mice and having extracted all longitudinal data measures for the mice. All authors wrote portions of the manuscript, with the majority of the manuscript having been written by DL, KC, AM, and DG.
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