Functional regression method for whole genome eQTL epistasis analysis with sequencing data.
Functional regression method for whole genome eQTL epistasis analysis with sequencing data.
复制标题
利用测序数据进行全基因组 eQTL 上位分析的功能回归方法
DOI：
10.1186/s12864-017-3777-4
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发表时间：
2017-05-18
期刊：
影响因子：
4.4
通讯作者：
Xiong M
中科院分区：
文献类型：
--
作者：
Xu K;Jin L;Xiong M
上位性在理解基因表达的调控机制中起着重要的作用，是基因表达遗传结构的重要组成部分。然而，由于巨大的计算挑战和数据可用性，基因表达的相互作用分析仍然从根本上没有被探索。由于剪接、转录起始点、多聚腺苷化位点、整个基因的转录后RNA编辑以及细胞的转录速率的变化，RNA-SEQ测量产生了很大的表达变异性，并共同创建了观察到的位置水平阅读计数曲线。被广泛用于微阵列测量的基因表达分析的单个数字不太可能充分解释整个基因的大的表达差异。同时使用RNA-SEQ和全基因组测序数据分析上位性结构带来了巨大的挑战。方法我们建立了一个具有功能反应的非线性函数回归模型(FRGM)，其中基因内的位置水平读数是基因组位置的函数，以及功能预测器，其中将基因型分布视为基因组位置的函数，用于利用RNA-SEQ数据进行上位性分析。FRGM不是测试所有可能的配对SNPs之间的交互作用，而是将一个基因作为上位性分析的基本单位，测试所有可能的基因对的交互作用，并使用所有可以获得的信息来共同测试两个基因组区域内所有可能的SNPs对之间的交互作用。结果通过大规模模拟，我们证明了所提出的用于上位性分析的FRGM能够实现正确的类型1错误，并且比现有方法具有更高的检测基因间交互作用的能力。所提出的方法被应用于1000基因组计划的RNA-seq和WGS数据。经Bonferroni校正后，FRGM、RPKM和DESeq分别从350个欧洲样本中鉴定出16、2361、260和51对显著相互作用的基因。结论FRGM可用于RNA-seq的上位性分析，能够捕捉异构体和位置水平的信息，具有广泛的应用前景。模拟和实际数据分析都突出了FRGM作为具有测序数据的上位性分析的良好选择的潜力。
BackgroundEpistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges.MethodsWe develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions.ResultsBy large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction identified using FRGM, RPKM and DESeq were 16,2361, 260 and 51, respectively, from the 350 European samples.ConclusionsThe proposed FRGM for epistasis analysis of RNA-seq can capture isoform and position-level information and will have a broad application. Both simulations and real data analysis highlight the potential for the FRGM to be a good choice of the epistatic analysis with sequencing data.