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首页> 外文期刊>International journal of computational biology and drug design >Assessing gene length biases in gene set analysis of Genome-Wide Association Studies.
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Assessing gene length biases in gene set analysis of Genome-Wide Association Studies.

机译：在全基因组关联研究的基因组分析中评估基因长度偏倚。

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摘要

Genome-Wide Association Studies (GWAS) have rapidly become a major genetics approach to studying complex diseases. Although many susceptibility variants and genes have been uncovered by single marker analysis, gene set based analysis is emerging as a very promising approach aiming to detect joint association of a set of genes with disease. In the available gene set based methods, it is often the smallest P value of the Single Nucleotide Polymorphisms (SNPs) in a gene region is used to represent the gene-level association signal. This approach may introduce strong bias of association signal towards long genes. In this study, we propose a resampling strategy by randomly generating genomic intervals across the accessible genomic region to estimate the background distribution of P values at the gene level. Comparing with the gene-wise P value in real data, the proportion of random intervals could be used to assess the bias that might be introduced by gene length and in turn to help the investigators choose the appropriate gene set analysis algorithms in their GWAS datasets. Our method uses only summarised GWAS data with no need of permutation, thus, it is computationally efficient. A computer program is freely available for the users.
机译：全基因组关联研究（GWAS）已迅速成为研究复杂疾病的主要遗传学方法。尽管已经通过单标记分析发现了许多易感性变体和基因，但是基于基因集的分析正在作为一种非常有希望的方法而出现，旨在检测一组基因与疾病的联合。在基于可用基因组的方法中，通常是基因区域中单核苷酸多态性（SNP）的最小P值用于表示基因水平的关联信号。这种方法可能会导致关联信号偏向长基因。在这项研究中，我们提出了一种通过在可访问的基因组区域内随机生成基因间隔来估计基因水平P值的背景分布的重采样策略。与真实数据中的基因方式P值相比，随机间隔的比例可以用来评估基因长度可能引起的偏差，进而帮助研究者在其GWAS数据集中选择合适的基因集分析算法。我们的方法仅使用汇总的GWAS数据，而无需置换，因此计算效率很高。用户可以免费使用计算机程序。

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