Methods for Epidemiology Studies

进行了一项详细的模拟研究，以评估从全基因组关联研究中建立多基因风险预测模型的替代策略。这项研究在SNP选择的最优策略、模型中SNP系数的估计以及连锁不平衡中SNP之间的相关性处理等方面提出了一些重要的观察结果。已经进行了调查，以测试在疾病风险极端情况下模型的充分性，在这些极端情况下，数据通常很稀少，但模型偏离的可能性更大，具有重要的临床意义。将该方法应用于一项大型乳腺癌病例对照研究的分析表明，在Logistic模型中，常见的SNPs在疾病风险上起加性作用。基于对家系内独立染色体重新采样的新方法，已经开发出检验受影响家系中观察到的突变模式的统计意义的方法。将该方法应用于外显子组测序数据，为最近发现黑色素瘤的一个主要基因提供了统计支持。考虑到复杂相互作用的可能性，已经开发了检测疾病与区域内多个SNPs的遗传关联的方法。一种创新的计算方法被开发出来，用于快速评估基因-基因相互作用的树结构模型，并结合基于排列的重采样方法来测试关联的统计意义。已发展了一种方法，利用随机效应或脆弱模型来评估由于未观察到的危险因素而导致的宫颈癌风险的异质性。该模型适用于一项对近百万女性进行的队列研究的数据，以深入了解参加大型医疗保健组织的女性的风险分布。发展了流行病学研究的加权Logistic分析的新方法，该方法涉及再分层和人口扩张的扩大抽样。通过模拟研究和人乳头瘤病毒血清学研究对该方法的性能进行了评价。一种基于排列的重采样方法被开发出来，用于使用代谢组数据来检验暴露(例如吸烟)通过中间生物标记物对疾病(例如肺癌)风险的中介作用的假设。研究了自适应套索算法的理论性质，该算法在高维协变量存在的情况下被广泛用于模型拟合，因为它能够在基础变量选择过程中控制错误发现率。

A detailed simulation study was conducted to evaluate alternate strategies for building polygenic risk prediction models from genome-wide association studies. The study made a number of important observations regarding optimal strategies for SNP selection, estimation of their coefficients in the model and handling of correlation among SNPs in linkage disequilibrium. Investigations have been conducted for testing the adequacy of models at extremes of disease-risk where data are often sparse but model departure is more likely with important clinical implications. Application of the method to analysis of a large case-control study of breast cancer indicate common SNPs act in additive fashion on the risk of the disease in a logistic scale. Methods have been developed for testing statistical significance of observed pattern of mutations in affected pedigrees based on novel method of resampling of independent chromosomes within pedigree. Application of the method to exome sequencing data has provided the statistical support behind the finding of a major gene for melanoma recently. Methods have been developed for testing genetic association of diseases with multiple SNPs within a region taking into account for possibility of complex interactions. An innovative computational approach was developed for rapid evaluation of tree-structure models for gene-gene interactions combined with permutation-based resampling methods for testing statistical significance of associations. Method has been developed to use random effect or frailty model to assess heterogeneity of risks of cervical cancer due to unobserved risk-factors. Model was fitted to data available from a cohort study of almost million women to obtain insight into risk distribution of women enrolled in a large HMO. New method was developed for weighting logistic analysis of epidemiologic studies that involves augmentation sampling with re-stratification and population expansion. Performance of the method was evaluated using simulation study and study of human papiloma virus serology. A permutation-based resampling method was developed for using metabolomic data for testing the hypothesis of mediation of the effect of an exposure (e.g smoking) on the risk of a disease (e.g lung cancer) through intermediate biomarkers. A study was conducted to investigate the theoretical properties of the adaptive LASSO algorithm, which is popularly used for model fitting in the presence of high-dimensional covariates, for its ability to control false discovery rate in the underlying variable selection procedure.