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

描述（由申请人提供）：在全基因组关联研究中纵向性状的二次分析摘要：将全基因组关联研究（GWAS）数据与纵向表型数据相关联可以提供特殊的优势，但也代表了某些挑战。这在病例对照研究的次要数据分析中尤其具有挑战性，这在GWAS研究中很常见。使用现有的病例对照GWAS数据进行的二级数据分析为纵向性状的遗传调查提供了一个有效和实用的解决方案，为研究疾病随时间的异质性和早期疾病检测提供了机会。目前的次要方法都不适用于纵向数据。本提案的主要目标是调查和开发统计推断方法，以分析纵向次要性状使用病例对照GWAS数据。 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）中出现的研究问题，在该试验中，分别在基线（T0）测量女性和男性的癌症抗原125（CA125）和前列腺特异性抗原（PSA）水平，然后每年测量一次，持续5年，并在3个时间点测量或报告BMI值（20岁，50岁和入学）。最近在NCI为GWAS开发的总基因型集包括来自PLCO内各种病例对照癌症研究的受试者的基因型数据。这种汇总的基因型数据集提供了一个独特的机会，以评估这些血清抗原的遗传决定因素及其连续时间轨迹，作为次要分析。 我们的具体目标是：1）发展统计方法，在GWAS纵向性状的二次分析。我们建议整合混合效应模型，通常用于纵向分析，加权似然和回顾似然方法，两个理论上合理的二次分析方法，为一个单一的时间性状，开发强大的和有效的方法，纵向性状的二次数据分析。2)将建议的统计方法转化为实用的研究知识和软件。我们将把目标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连续时间轨迹。我们还将开发、分发和支持本提案中使用的方法的免费软件包。 考虑到最近产生的基因型数据的巨大数量，如本文所提出的，非常需要开发复杂的二次分析统计方法。这项研究将极大地推进应用流行病学研究，通过允许研究人员将纵向数据分析与遗传分析相结合，利用最近生成的大量病例对照GWAS数据。

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.