Department of Biomedical Engineering and Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLoS One. 2013 Jul 23;8(7):e68585. doi: 10.1371/journal.pone.0068585. Print 2013.
Gene-based tests of association can increase the power of a genome-wide association study by aggregating multiple independent effects across a gene or locus into a single stronger signal. Recent gene-based tests have distinct approaches to selecting which variants to aggregate within a locus, modeling the effects of linkage disequilibrium, representing fractional allele counts from imputation, and managing permutation tests for p-values. Implementing these tests in a single, efficient framework has great practical value. Fast ASsociation Tests (Fast) addresses this need by implementing leading gene-based association tests together with conventional SNP-based univariate tests and providing a consolidated, easily interpreted report. Fast scales readily to genome-wide SNP data with millions of SNPs and tens of thousands of individuals, provides implementations that are orders of magnitude faster than original literature reports, and provides a unified framework for performing several gene based association tests concurrently and efficiently on the same data.
https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz, with documentation at https://bitbucket.org/baderlab/fast/wiki/Home.
基于基因的关联测试可以通过将基因或基因座内的多个独立效应聚合到单个更强的信号中来增加全基因组关联研究的功效。最近的基于基因的测试在选择要聚合的变体、建模连锁不平衡的效应、表示来自 imputation 的分数等位基因计数以及管理 p 值的置换检验方面具有不同的方法。在单个高效框架中实现这些测试具有很大的实际价值。Fast ASsociation Tests (Fast) 通过实现领先的基于基因的关联测试与传统的基于 SNP 的单变量测试相结合，并提供一个综合的、易于解释的报告来满足这一需求。Fast 可以轻松扩展到具有数百万个 SNP 和数万个个体的全基因组 SNP 数据，提供的实现比原始文献报告快几个数量级，并为在同一数据上同时高效地执行多个基于基因的关联测试提供了一个统一的框架。
https://bitbucket.org/baderlab/fast/downloads/FAST.tar.gz，文档在 https://bitbucket.org/baderlab/fast/wiki/Home。
