Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245, USA.
Genet Epidemiol. 2009;33 Suppl 1(Suppl 1):S33-9. doi: 10.1002/gepi.20470.
The complex etiology of common diseases like cardiovascular disease, diabetes, hypertension, and rheumatoid arthritis has led investigators to focus on the genetics of correlated phenotypes and risk factors. Joint analysis of multiple disease-related phenotypes may reveal genes of pleiotropic effect and increase analytical power, but at the cost of increased analytical and computational complexity. All three data sets provided for analysis at the Genetic Analysis Workshop 16 offered multiple quantitative measures of phenotypes related to underlying disease processes as well as discrete measures of affection status. Participants in Group 6 addressed the challenges and possibilities of association analysis of these data sets on multiple levels, including phenotype definition and data reduction, multivariate approaches to gene discovery, analysis of causality and data structure, and development of predictive models. These approaches included combinations of continuous and discrete phenotypes, use of repeated measures in longitudinal data, and models that included multiple phenotypic measures and multiple single-nucleotide polymorphism variants. Most research teams regarded the use of multiple related phenotypes as a tool for increasing analytical power, as well as for clarifying the underlying biology of complex diseases.
常见疾病（如心血管疾病、糖尿病、高血压和类风湿性关节炎）的复杂病因导致研究人员关注相关表型和风险因素的遗传学。对多种疾病相关表型进行联合分析可能会揭示具有多种效应的基因，并提高分析能力，但代价是分析和计算的复杂性增加。遗传分析研讨会 16 提供的所有三个数据集都提供了与潜在疾病过程相关的多种定量表型测量值，以及离散的发病状态测量值。第 6 组的参与者在多个层面上解决了对这些数据集进行关联分析的挑战和可能性，包括表型定义和数据减少、基因发现的多变量方法、因果关系和数据结构分析以及预测模型的开发。这些方法包括连续和离散表型的组合、纵向数据中重复测量的使用以及包含多个表型测量值和多个单核苷酸多态性变体的模型。大多数研究团队认为，使用多种相关表型既是提高分析能力的工具，也是阐明复杂疾病潜在生物学的工具。
