A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.
A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.
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DOI：
10.1371/journal.pcbi.1000770
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发表时间：
2010-05-06
影响因子：
4.3
通讯作者：
Winn J
中科院分区：
文献类型：
--
作者：
Stegle O;Parts L;Durbin R;Winn J
基因表达测量受多种因素的影响，例如细胞状态、实验条件和调控区序列的变体。为了理解感兴趣的变量（例如基因座的基因型）的影响，重要的是要考虑由于混杂原因引起的变异。在这里，我们提出了VBQTL，一个概率的方法映射表达数量性状基因座（eQTL），共同模型的贡献，从基因型以及已知和隐藏的混杂因素。VBQTL是在一个高效灵活的推理框架内实现的，使其在大规模问题上快速且易于处理。我们比较了VBQTL与其他方法的性能，用于处理来自模拟、酵母、小鼠和人类的eQTL作图数据集的混淆变异性。采用贝叶斯复杂性控制和联合建模，导致更精确的估计不同的混杂因素的贡献，从而导致额外的协会测量的转录水平相比，替代方法。我们提出了一个三倍大的收集顺eQTL比以前发现的全基因组eQTL扫描远交人群。总的来说，27%的测试探针显示出显着的顺式遗传关联，我们验证了额外的eQTL很可能是真实的复制他们在不同的个人。我们的方法是高维表型数据分析的下一步，它的应用揭示了基因表达的遗传调控的见解，通过展示更丰富的顺式作用的eQTL在人类比以前显示。我们的软件可在http://www.sanger.ac.uk/resources/software/peer/网站上免费获得。基因表达是一个复杂的表型。实验中测量的表达水平可能受到广泛因素的影响-细胞状态、实验条件、调控区序列的变体等。为了理解基因型与表型之间的关系，我们需要能够区分由遗传状态引起的变异与所有混淆原因。我们提出了VBQTL，解剖基因表达变异的概率方法，通过联合建模的变异性和遗传效应的潜在全球原因。我们的方法是在一个灵活的框架，允许快速的模型适应和比较与替代模型。概率的方法产生更准确的估计的贡献，从不同的变异来源。应用VBQTL，我们发现，在人类中控制基因表达水平的常见遗传变异比以前显示的更丰富，这对基因型与表型相关的广泛研究具有意义。
Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/. Gene expression is a complex phenotype. The measured expression level in an experiment can be affected by a wide range of factors—state of the cell, experimental conditions, variants in the sequence of regulatory regions, and others. To understand genotype-to-phenotype relationships, we need to be able to distinguish the variation that is due to the genetic state from all the confounding causes. We present VBQTL, a probabilistic method for dissecting gene expression variation by jointly modelling the underlying global causes of variability and the genetic effect. Our method is implemented in a flexible framework that allows for quick model adaptation and comparison with alternative models. The probabilistic approach yields more accurate estimates of the contributions from different sources of variation. Applying VBQTL, we find that common genetic variation controlling gene expression levels in human is more abundant than previously shown, which has implications for a wide range of studies relating genotype to phenotype.
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