Secondary Analysis of Longitudinal Trait in Genome Wide Association Studies - Res

描述(申请人提供)：基因组广泛关联研究中纵向性状的二次分析摘要：将基因组广泛关联研究数据与纵向表型数据相关联可以提供特殊的优势，但也存在一定的挑战。这在病例对照研究的次要数据分析中尤其具有挑战性，这通常是在GWAS调查中发现的。利用现有的病例对照数据进行的二次数据分析为纵向性状的遗传研究提供了一种有效和实用的解决方案，使人们有机会检查疾病随时间的异质性和疾病的早期发现。目前的辅助方法都不适用于纵向数据。这项建议的主要目标是调查和开发统计推断方法，以使用病例对照GWAs数据来分析纵向次要性状。该项目的动机是前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验(PLCO)引发的研究问题，在该试验中，女性和男性的癌症抗原125(CA125)和前列腺癌特异性抗原(PSA)水平分别在基线(T0)和每年五年内进行测量，并在三个时间点(20岁、50岁和登记)测量或报告BMI值。美国国立卫生研究院最近为GWAS开发的总基因型集，包括PLCO内各种病例对照癌症研究对象的基因数据。这一聚合的基因数据集为评估这些血清抗原的遗传决定因素及其序列时间轨迹提供了一个独特的机会，作为二次分析。我们的具体目标是：1)发展统计方法，用于纵向性状的二次分析。我们建议将通常用于纵向分析的混合效应模型与加权似然法和回溯似然法这两种理论上合理的单时间性状二次分析方法相结合，以开发稳健而有效的纵向性状二次数据分析方法。2)将拟议的统计方法转化为实用的研究知识和软件。我们将把在AIM 1中提出的方法应用于PLCO BMI、CA125和PSA Gwas数据，以评估这些抗原的遗传决定因素及其序列时间轨迹。我们还将为本提案中使用的方法开发、分发和支持免费提供的软件包。考虑到最近产生的大量基因数据，非常需要开发复杂的二次分析统计方法，如本文所建议的。这项研究将极大地推动应用流行病学研究，使研究人员能够利用最近产生的大量病例对照数据，将纵向数据分析与基因分析结合起来。

DESCRIPTION (provided by applicant): Secondary Analysis of Longitudinal Trait in Genome Wide Association Studies Abstract: Relating genome wide association study (GWAS) data to longitudinal phenotype data can provide special advantages, but also represents certain challenges. This is particularly challenging in secondary data analyses from case-control studies, as commonly found for GWAS investigations. Secondary data analyses using existing case-control GWAS data yield an effective and practical solution for genetic investigations of longitudinal traits that afford the opportunity to examine disease heterogeneity over time and early disease detection. None of the current secondary methodologies works with longitudinal data. The primary goal of this proposal is to investigate and develop statistical inference methods to analyze longitudinal secondary traits using case-control GWAS data. This project is motivated by research problems arising from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO), in which Cancer Antigen 125 (CA125) and Prostate Specific Antigen (PSA) levels were measured at baseline (T0) and then annually for five years in women and men, respectively, and BMI values were measured or reported at three time points (age 20, 50 and enrollment). The Total Genotype Set developed recently at the NCI for GWAS, includes genotype data for subjects from various case-control cancer studies within PLCO. This aggregated genotype dataset provides a unique opportunity to evaluate the inherited determinants of these serum antigens and their serial time trajectories, as secondary analyses. Our specific aims are: 1) Develop statistical approaches for secondary analysis of longitudinal traits in GWAS. We propose to integrate the mixed-effects model, commonly used for longitudinal analysis, with weighted likelihood and retrospective likelihood methods, two theoretically justified secondary analysis methods for a single time trait, to develop robust and efficient approaches for secondary data analysis of longitudinal traits. 2) Translate the proposed statistical methodology into practical research knowledge and software. We will apply the proposed methods generated in Aim 1 to the PLCO BMI, CA125 and PSA GWAS data to evaluate the inherited determinants of these antigens and their serial time trajectories. We will also develop, distribute and support freely available software packages for the methods used in this proposal. Considering the immense amount of recently generated genotype data, there is a tremendous need for the development of sophisticated secondary analysis statistical methods, as proposed herein. This study will greatly advance applied epidemiology research, by allowing investigators to incorporate longitudinal data analysis with genetic analysis, taking advantage of the large supply of recently generated case-control GWAS data.