论文标题
使用摘要统计数据的自适应和健壮方法用于全基因组关联研究的多特征分析
An Adaptive and Robust Method for Multi-trait Analysis of Genome-wide Association Studies Using Summary Statistics
论文作者
论文摘要
在过去的十年中，全基因组关联研究（GWAS）已经确定了数千种与人类性状或疾病相关的遗传变异。然而，许多特征的遗传力仍然没有说明。常用的单特征分析方法是保守的，而多特征方法通过整合跨多个性状的关联证据来改善统计能力。与个体级别的数据相反，GWAS摘要统计数据通常是公开可用的，因此仅使用摘要统计信息的方法具有更大的用法。尽管已经开发了许多使用摘要统计数据来对多个性状进行联合分析的方法，但是在考虑很多特征时，还有许多问题，包括绩效不一致，计算效率低下和数值问题。为了应对这些挑战，我们提出了一种用于摘要统计数据（MTAFS）的多特征自适应Fisher方法，这是一种具有强大功率性能的计算高效方法。我们将MTAF应用于英国生物库的两组大脑图像衍生的表型（IDP），包括一组58个体积IDP和一组212个区域IDP。与模拟研究的结果一起，MTAFS显示了其优于现有多特征方法的优势，并且在一系列基础设置中具有良好的性能。它很好地控制了1型错误，并且可以有效地处理大量特征。
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human traits or diseases in the past decade. Nevertheless, much of the heritability of many traits is still unaccounted for. Commonly used single-trait analysis methods are conservative, while multi-trait methods improve statistical power by integrating association evidence across multiple traits. In contrast to individual-level data, GWAS summary statistics are usually publicly available, and thus methods using only summary statistics have greater usage. Although many methods have been developed for joint analysis of multiple traits using summary statistics, there are many issues, including inconsistent performance, computational inefficiency, and numerical problems when considering lots of traits. To address these challenges, we propose a multi-trait adaptive Fisher method for summary statistics (MTAFS), a computationally efficient method with robust power performance. We applied MTAFS to two sets of brain image-derived phenotypes (IDPs) from the UK Biobank, including a set of 58 Volumetric IDPs and a set of 212 Area IDPs. Together with results from a simulation study, MTAFS shows its advantage over existing multi-trait methods, with robust performance across a range of underlying settings. It controls type 1 error well, and can efficiently handle a large number of traits.
