A novel method for analyzing genetic association with longitudinal phenotypes

Londono Douglas, Chen Kuo-mei, Musolf Anthony, Wang Ruixue, Shen Tong, Brandon January, Herring John A., Wise Carol A., Zou Hong, Jin Meilei, Yu Lei, Finch Stephen J., Matise Tara C. and Gordon Derek ()
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Londono Douglas: Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA
Chen Kuo-mei: Department of Statistics, Rutgers, The State University of New Jersey, 110 Frelinghuysen Road, Piscataway, NJ 08854–8019, USA
Musolf Anthony: Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA
Wang Ruixue: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794–3600, USA
Shen Tong: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794–3600, USA
Brandon January: Seay Center for Musculoskeletal Research, Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 75219, USA
Herring John A.: Department of Orthopedic Surgery, Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 75219, USA
Wise Carol A.: Seay Center for Musculoskeletal Research, Texas Scottish Rite Hospital for Children, 2222 Welborn Street, Dallas, TX 75219, USA Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, USA; and McDermott Center, University of Texas Southwestern Medical Center, 6000 Harry Hines Blvd, Dallas, TX 75390, USA
Zou Hong: Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
Jin Meilei: Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China ShanghaiBio Corp., Shanghai, China
Yu Lei: Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA Center of Alcohol Studies, Rutgers, The State University of New Jersey, 607 Allison Road, Piscataway, NJ 08854, USA
Finch Stephen J.: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794–3600, USA
Matise Tara C.: Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA
Gordon Derek: Department of Genetics, Rutgers, The State University of New Jersey, 145 Bevier Road, Piscataway, NJ 08854, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 2, 241-261

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.

Keywords: mixture model; mixtures; disease course; methodology; PROC TRAJ; Mplus (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2012-0070

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