Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.
Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.
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DOI：
10.1371/journal.pcbi.1004714
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发表时间：
2016-01
影响因子：
4.3
通讯作者：
Bergmann S
中科院分区：
文献类型：
--
作者：
Lamparter D;Marbach D;Rueedi R;Kutalik Z;Bergmann S
整合来自跨基因和通路的全基因组关联研究（GWAS）的单核苷酸多态性（SNP）p值是提高统计功效和获得生物学见解的策略。在这里，我们提出了Pascal（通路评分算法），一个强大的工具，用于计算基因和通路得分从SNP-表型关联汇总统计。对于基因得分计算，我们实现了分析和有效的数值解决方案来计算测试统计量。我们特别研究了卡方统计量的总和和最大值，它们分别测量每个基因的最强和平均关联信号。对于途径评分，我们使用修改后的Fisher方法，它不仅提供了更传统的富集策略的显着的功率改善，但也消除了任意阈值选择固有的任何二进制成员基于途径富集方法的问题。我们通过分析来自数十项大型元研究的各种特征的汇总统计数据，证明了权力的显着增加。我们广泛的测试表明，我们的方法不仅在严格的I型错误控制方面表现出色，而且还导致了更多的生物学意义的发现。全基因组关联研究（GWAS）通常会生成与性状或疾病相关的SNP列表。然而，这样的输出几乎没有揭示潜在的分子机制，并且需要工具来从SNP水平的结果中提取生物学见解。途径分析工具整合来自基因组中不同位置处的多个SNP的信号，以便将相关的基因组区域映射到完善的途径，即，一组已知协同作用的基因。GWAS关联结果的性质要求为此任务专门定制的方法。在这里，我们提出了Pascal（路径评分算法），一种允许GWAS关联结果的基因和路径水平分析而无需访问原始基因型数据的工具。Pascal被设计为快速，准确，并具有高功率来检测相关途径。我们在大量真实的GWAS关联结果上广泛测试了我们的方法，发现比其他流行方法更好地发现了已确认的途径。我们相信，这些结果加上我们的公开软件的易用性，将使Pascal成为GWAS社区工具箱的有用补充。
Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful tool for computing gene and pathway scores from SNP-phenotype association summary statistics. For gene score computation, we implemented analytic and efficient numerical solutions to calculate test statistics. We examined in particular the sum and the maximum of chi-squared statistics, which measure the strongest and the average association signals per gene, respectively. For pathway scoring, we use a modified Fisher method, which offers not only significant power improvement over more traditional enrichment strategies, but also eliminates the problem of arbitrary threshold selection inherent in any binary membership based pathway enrichment approach. We demonstrate the marked increase in power by analyzing summary statistics from dozens of large meta-studies for various traits. Our extensive testing indicates that our method not only excels in rigorous type I error control, but also results in more biologically meaningful discoveries. Genome-wide association studies (GWAS) typically generate lists of trait- or disease-associated SNPs. Yet, such output sheds little light on the underlying molecular mechanisms and tools are needed to extract biological insight from the results at the SNP level. Pathway analysis tools integrate signals from multiple SNPs at various positions in the genome in order to map associated genomic regions to well-established pathways, i.e., sets of genes known to act in concert. The nature of GWAS association results requires specifically tailored methods for this task. Here, we present Pascal (Pathway scoring algorithm), a tool that allows gene and pathway-level analysis of GWAS association results without the need to access the original genotypic data. Pascal was designed to be fast, accurate and to have high power to detect relevant pathways. We extensively tested our approach on a large collection of real GWAS association results and saw better discovery of confirmed pathways than with other popular methods. We believe that these results together with the ease-of-use of our publicly available software will allow Pascal to become a useful addition to the toolbox of the GWAS community.