Quantitative trait locus analysis for next-generation sequencing with the functional linear models.
Quantitative trait locus analysis for next-generation sequencing with the functional linear models.
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DOI：
10.1136/jmedgenet-2012-100798
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发表时间：
2012-08
影响因子：
4
通讯作者：
Xiong M
中科院分区：
文献类型：
--
作者：
Luo L;Zhu Y;Xiong M
尽管在过去的几年中，我们见证了利用下一代测序（NGS）数据进行定性性状关联研究的新型统计方法的快速发展，但用于检测稀有变异与数量性状关联的统计方法却很少。罕见变异的QTL分析仍然具有挑战性。从低维数据到高维基因组数据的分析要求统计方法从多元数据分析到功能数据分析的变化。在本文中，我们提出了一个功能线性模型（FLM）作为开发用于重测序数据的新颖而强大的QTL分析方法的一般原则。通过模拟，我们计算了I型错误率，并评估了FLM和其他八种现有统计方法的功率，即使存在积极和消极的影响迹象。由于FLM保留了数据中的所有遗传信息，并探索了逐变分析和集体分析的优点，克服了它们的局限性，因此FLM在所有考虑的情况下都比其他现有统计方法具有更高的功效。为了进一步评估FLM的性能，我们将FLM应用于达拉斯心脏研究中6个数量性状的关联分析，以及1000基因组计划中低覆盖率重测序数据的遗传变异RNA-seq eQTL分析。实际数据分析表明，与其他现有方法相比，FLM具有更小的p值来识别显著相关的变量。FLM有望为QTL分析开辟一条新的途径。
Although in the past few years we have witnessed the rapid development of novel statistical methods for association studies of qualitative traits using next Generation Sequencing (NGS) data, only a few statistics are proposed for testing the association of rare variants with quantitative traits. The QTL analysis of rare variants remains challenging. Analysis from low dimensional data to high dimensional genomic data demands changes in statistical methods from multivariate data analysis to functional data analysis. In this paper, we propose a functional linear model (FLM) as a general principle for developing novel and powerful QTL analysis methods designed for resequencing data. By simulations we calculate the type I error rates and evaluate the power of the FLM and other eight existing statistical methods even in the presence of both positive and negative signs of effects. Since the FLM retains all of the genetic information in the data and explores the merits of both variant-by-variant and collective analysis and overcomes their limitation, the FLM has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the FLM is applied to association analysis of six quantitative traits in the Dallas Heart Study, and RNA-seq eQTL analysis with genetic variation in the low coverage resequencing data of the 1000 Genomes Project. Real data analysis shows that the FLM has much smaller P-values to identify significantly associated variants than other existing methods. The FLM is expected to open a new route for QTL analysis.
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