Statistical Applications in Genetics and Molecular Biology
Statistical Applications in Genetics and Molecular Biology
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DOI：
10.1515/sagmb
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发表时间：
2012
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通讯作者：
Stopping-Time Resampling
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作者：
Stopping-Time Resampling
摘要近年来，表达数量基因座（eQTL）定位研究，将成千上万个基因的表达水平视为数量性状，已被用于提供更深入的基因调控生物学研究。最初，eQTL是通过应用标准QTL检测工具（使用“一次一个基因”的方法）来检测的，但这种方法忽略了基因之间许多可能的相互作用。已经提出了其他几种方法来克服这些限制，但它们都有一些特定的缺点。在本文中，我们提出了一个集成hierarchicalBayesian模型，联合模型的所有基因和SNPs检测eQTL。我们提出了一个模型（名为iBMQ），专门设计用于处理大量G的基因表达，大量S的回归（遗传标记）和少量n的个人，我们称之为“大G，大S，小n”的范例。该方法将基因型数据和基因表达数据整合到一个模型中，同时1）专门处理eQTL数据的高维性（大量基因），2）借用所有基因表达数据的力量进行作图程序，3）将假阳性的数量控制在理想的水平。为了验证我们的模型，我们进行了模拟研究，并表明它优于其他流行的eQTL检测方法，包括QTLBIM，R-QTL，remMap和M-SPLS。最后，我们使用我们的模型分析了一组小鼠BXD重组近交（RI）品系中获得的真实的表达数据集，用iBMQ分析这些数据显示存在多个热点，这些热点显示属于一个或多个注释类别的基因显著富集。
Abstract Recently, expression quantitative loci (eQTL) mapping studies, where expression levels ofthousands of genes are viewed as quantitative traits, have been used to provide greater insightinto the biology of gene regulation. Originally, eQTLs were detected by applying standard QTLdetection tools (using a “one gene at-a-time” approach), but this method ignores many possibleinteractions between genes. Several other methods have proposed to overcome these limitations, buteach of them has some specific disadvantages. In this paper, we present an integrated hierarchicalBayesian model that jointly models all genes and SNPs to detect eQTLs. We propose a model(named iBMQ) that is specifically designed to handle a large number G of gene expressions, a largenumber S of regressors (genetic markers) and a small number n of individuals in what we call a``large G, large S, small n'' paradigm. This method incorporates genotypic and gene expression datainto a single model while 1) specifically coping with the high dimensionality of eQTL data (largenumber of genes), 2) borrowing strength from all gene expression data for the mapping procedures,and 3) controlling the number of false positives to a desirable level. To validate our model, wehave performed simulation studies and showed that it outperforms other popular methods for eQTLdetection, including QTLBIM, R-QTL, remMap and M-SPLS. Finally, we used our model toanalyze a real expression dataset obtained in a panel of mice BXD Recombinant Inbred (RI) strains.Analysis of these data with iBMQ revealed the presence of multiple hotspots showing significantenrichment in genes belonging to one or more annotation categories.