title: A two-phase procedure for non-normal quantitative trait genetic association study.
abstract: BACKGROUND
  The nonparametric trend test (NPT) is well suitable for identifying the genetic variants associated with quantitative traits when the trait values do not satisfy the normal distribution assumption. If the genetic model, defined according to the mode of inheritance, is known, the NPT derived under the given genetic model is optimal. However, in practice, the genetic model is often unknown beforehand. The NPT derived from an uncorrected model might result in loss of power. When the underlying genetic model is unknown, a robust test is preferred to maintain satisfactory power.
  
  
  RESULTS
  We propose a two-phase procedure to handle the uncertainty of the genetic model for non-normal quantitative trait genetic association study. First, a model selection procedure is employed to help choose the genetic model. Then the optimal test derived under the selected model is constructed to test for possible association. To control the type I error rate, we derive the joint distribution of the test statistics developed in the two phases and obtain the proper size.
  
  
  CONCLUSIONS
  The proposed method is more robust than existing methods through the simulation results and application to gene DNAH9 from the Genetic Analysis Workshop 16 for associated with Anti-cyclic citrullinated peptide antibody further demonstrate its performance.
authors.affiliation: Key Laboratory of Systems Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. zhangwei@amss.ac.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China. lihuiyun@bit.edu.cn.
authors.forename: Huiyun
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Statistics, George Washington University, Washington, 20052, DC, USA. zli@gwu.edu.
authors.forename: Zhaohai
authors.initials: Z
authors.lastname: Li
authors.affiliation: Key Laboratory of Systems Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China. liqz@amss.ac.cn.
authors.forename: Qizhai
authors.initials: Q
authors.lastname: Li
mesh_terms: D001323:Autoantibodies / Q000276:immunology; D056444:Axonemal Dyneins / Q000235:genetics* / Q000276:immunology; D056726:Genetic Association Studies; D014644:Genetic Variation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010456:Peptides, Cyclic / Q000276:immunology; D010641:Phenotype
chemical_list: D001323:Autoantibodies; D010456:Peptides, Cyclic; C487763:cyclic citrullinated peptide; D056444:Axonemal Dyneins; C430745:DNAH9 protein, human
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