Methods for Epidemiology Studies

已经对使用来自当前和未来的全基因组关联研究的数据来改善预测疾病风险的模型的性能的潜力进行了调查。提出了一种新的数学范式来表征多基因模型在训练数据集的样本大小、潜在易感位点的数量及其效应大小分布方面的预测性能。然后，该范例被应用于对包括癌症在内的10种不同复杂性状的风险预测模型的性能进行预测。这些预测表明，在未来，将需要非常大的GWAS，其样本量甚至比迄今为止最大的GWAS中的一些更大的数量级，以建立预测性能大幅提高的遗传风险模型。一种新的方法被开发用于评估基因与环境的相互作用，使用来自病例对照全基因组关联研究的数据，该研究使用公开可用的遗传控制。 结果表明，在一组假设下，有可能从这些研究中表征联合基因-环境效应，如果环境暴露的数据可从内部病例对照研究中获得，即使在这样的研究中的对照没有基因分型。 开发了新的方法来评估SNP标记与反映疾病进展的各个阶段的有序性质的疾病结果的关联。两个替代测试，最大得分测试（MAX）和自适应P值组合测试（Adapt-P），提出了与可能的替代模型，其中一个SNP可能涉及在各个阶段的效率和鲁棒性之间的平衡的目的。仿真研究表明，MAX和Adapt-P在各种现实场景下的所有测试中具有最鲁棒的性能。开发了一种基于置换的重新排序方法，用于使用代谢组学数据来检验通过中间生物标志物介导暴露（例如吸烟）对疾病（例如肺癌）风险的影响的假设。 广泛的模拟研究被用来检查所提出的测试的有效性和功率。方法开发了复杂的抽样设计，以人口为基础的病例对照研究的分析。两种方法被开发用于通过对设计变量的权重条件的样本期望进行建模来合并样本权重中包含的信息。与使用原始样本权重的标准估计相比，这些方法具有更高的效率和更小的有限样本偏差。这些方法是美国肾癌病例对照研究，以确定危险因素。一个项目开发了一个线性预期回归模型（LEXPIT），将线性和非线性风险效应结合起来，以估计二元结果研究的绝对风险。LEXPIT是二项线性和逻辑回归模型的推广。LEXPIT线性项的系数估计调整后的风险差异，而取幂的非线性项估计剩余优势比。的LEXPIT可能是特别有用的流行病学研究的风险关联，其中调整多个混杂变量是常见的。该方法用于估计在Kaiser Permanente北方加利福尼亚州接受筛查的167，171名妇女中与不同Pap和人乳头瘤病毒检测结果相关的宫颈癌前病变或癌症的绝对五年风险。LEXPIT模型发现，HPV阴性患者的异常巴氏试验导致风险增加，而logistic回归未检测到。开发了一个R软件包blm，以提供免费且易于使用的软件来拟合LEXPIT模型。

Investigations have been conducted for the potential for using data from current and future genome-wide association studies for improving performance of models for predicting disease risks. A new mathematical paradigm was developed to characterize predictive performance of polygenic models in terms of sample size for training datasets, number of underlying susceptibility loci and distribution of their effect-sizes. The paradigm was then applied to make projections for performance of risk prediction models for ten different complex traits, including cancers. These projections revealed that in the future extremely large GWAS, with sample size of a larger order magnitude than even some of the largest GWAS to date, would be needed for building genetic risk models with substantially improved predictive performance. A new method was developed for assessing gene-environment interactions using data from case-control genome-wide association studies that uses publicly available genetic controls. It was shown that under a set of assumptions it possible to characterize joint gene-environment effects from such studies if data on environmental exposures are available from an internal case-control study even if controls in such a study are not genotyped. New methods was developed for evaluating association of SNP markers with disease outcome of ordinal nature reflecting various stages of the progression of a disease. Two alternative tests, the maximum score test (MAX) and the adaptive P-value combination test (Adapt-P), are proposed with the aim of striking a balance between efficiency and robustness over possible alternative models by which a SNPs might be involved in the various stages. Simulation studies were used to demonstrates that MAX and Adapt-P have the most robust performance among all a range of tests under various realistic scenarios. 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. Extensive simulation studies were used to examine validity and power of the proposed test. Methods were developed for analysis of population-based case-control studies with complex sampling designs. Two methods were developed for incorporating the information included in the sample weights by modeling the sample expectation of the weights conditional on design variables. These methods have higher efficiency and smaller finite sample bias compared with the standard estimators that use original sample weights. The methods were to the U.S. Kidney Cancer Case-Control Study to identify risk factors. A project developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, while the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. The method was applied to estimate the absolute five-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern Califronia. The LEXPIT model found an increased risk due to abnormal Pap test in HPV-negative that was not detected with logistic regression. An R package blm was developed to provide free and easy-to-use software for fitting the LEXPIT model.