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On the pros and cons of mega-analyses compared to meta-analyses for genome-wide association studies

全基因组关联研究中大型分析与荟萃分析的优缺点

基本信息

批准号：
360274005
负责人：
Professorin Dr. Iris M. Heid
金额：
--
依托单位：
Lehrstuhl für Genetische Epidemiologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2017
资助国家：
德国
起止时间：
2016-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/360274005?language=en
关键词：
pros cons mega analyses compared

项目摘要

Genome-wide association studies have been highly successful in identifying genetic variants associated with complex diseases. To increase the sample size and statistical power, these studies are usually conducted as meta-analyses combining study-specific aggregated statistics for each of 0.5 to 30 million genotyped and imputed variants. It is hypothesized that collecting individual participant data (IPD) to enable joint quality control, joint imputation, and joint modelling in one very big data set (mega-analysis) instead of a meta-analysis of aggregated statistics would improve the ability to detect disease loci. However, there is little work evaluating what aspect of mega-analysis versus meta-analysis contributes to how much gain in data quality and disease loci detection in real data scenarios. Such evaluations are still limited due to the high computational burden of genotype imputing and the lack of large genome-wide IPD data. The question remains how much can be gained by mega-analysis and whether it is worth the effort. We thus will set out to conduct a systematic evaluation of each aspect of a mega-analysis of large IPD versus a meta-analysis of study-specific aggregated statistics. We will quantify the gain in the data quality and the ability to detect disease loci in a real large genome-wide IPD and will combine information from the real data set with simulation approaches to expand the scenarios. We will conduct the comparisons on three levels, the quality control, the imputation, and the modelling. One special focus will be rare variants.Based on our previous work, we are in the unique situation of having a large IPD at our hand (> 40.000 persons with and without age-related macular degeneration). This disease and our data provide an ideal role model, since the data set consists of > 12 million genetic variants including 160,000 that are rare protein-altering, and exhibits 34 detectable AMD loci. The results of this project will guide future genetic studies whether the challenge to gather IPD data for mega-analysis will be worth the effort and improve disease locus detection. The results of this project will also help understand how to analyse large multi-center GWAS where all data is available as IPD.

全基因组关联研究在识别与复杂疾病相关的遗传变异方面取得了极大的成功。为了增加样本量和统计能力，这些研究通常以荟萃分析的形式进行，结合了50万到3000万个基因分型和归因型变异的特定研究的汇总统计数据。假设收集个人参与者数据(IPD)以在一个非常大的数据集(巨型分析)中实现联合质量控制、联合推算和联合建模，而不是对汇总的统计数据进行荟萃分析，将提高检测疾病地点的能力。然而，很少有工作来评估在真实数据场景中，巨型分析和荟萃分析的哪些方面有助于在数据质量和疾病位置检测方面获得多少收益。由于基因输入的高计算负担和缺乏大的全基因组IPD数据，这样的评估仍然有限。问题仍然是，通过巨型分析可以获得多少收益，以及是否值得付出努力。因此，我们将着手对大型IPD巨型分析的每一个方面进行系统评估，而不是对特定研究的汇总统计数据进行荟萃分析。我们将量化数据质量和在真正的大型全基因组IPD中检测疾病位点的能力方面的收益，并将来自真实数据集的信息与模拟方法相结合，以扩展场景。我们将从质量控制、归责和建模三个层面进行比较。一个特别的焦点将是罕见的变异。基于我们之前的工作，我们处于一个独特的情况下，我们手中有一个大的IPD(&gt；40.000患有和不患有年龄相关性黄斑变性的人)。这种疾病和我们的数据提供了一个理想的榜样，因为数据集包括1200万个遗传变异，其中包括16万个罕见的蛋白质改变，并展示了34个可检测到的AMD基因座。该项目的结果将指导未来的基因研究，收集用于大规模分析的IPD数据的挑战是否值得努力，并改进疾病基因座检测。该项目的结果还将有助于理解如何分析大型多中心全球气候分析系统，其中所有数据都可作为综合数据处理。