Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.
Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.
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DOI：
10.1371/journal.pgen.1004383
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发表时间：
2014-05
期刊：
影响因子：
4.5
通讯作者：
Plagnol V
中科院分区：
文献类型：
--
作者：
Giambartolomei C;Vukcevic D;Schadt EE;Franke L;Hingorani AD;Wallace C;Plagnol V
遗传关联研究，特别是全基因组关联研究（GWAS）的设计，提供了丰富的新的见解的病因广泛的人类疾病和性状，特别是心血管疾病和脂质生物标志物。下一个挑战是理解这些关联的分子基础。包括基因表达数据集在内的多个关联数据集的整合可以有助于实现这一目标。我们开发了一种新的统计方法来评估两个关联信号是否与共享的因果变量一致。一个应用是疾病扫描与表达数量性状基因座（eQTL）研究的整合，但任何一对GWAS数据集都可以整合到这个框架中。我们通过重新分析966个肝脏样本中的基因表达数据集来证明该方法的价值，该数据集具有已发表的脂质性状荟萃分析，包括> 100，000名欧洲血统的个体。结合所有脂质生物标志物，我们的重新分析支持了38个报告的与eQTL的共定位结果中的26个，并确定了14个新的共定位结果，从而强调了正式统计检验的价值。在三个报告的eQTL-脂质对（SYPL 2，IFFT 172，TBKBP 1）的情况下，我们的分析表明，eQTL模式与脂质关联不一致，我们确定了替代的共定位结果与SORT 1，GCKR，KPNB 1，表明这些基因更可能是因果关系，在这些基因组间隔。该方法的一个关键特征是能够从单个SNP汇总统计量导出输出统计量，因此可以在多个GWAS数据集上进行系统的荟萃分析类型比较（在http：//coloc.cs.ucl.ac.uk/coloc/在线实现）。我们的方法提供了相关间隔的候选致病基因的信息，并对复杂疾病的理解以及针对疾病途径的药物设计具有直接影响。全基因组关联研究（GWAS）已经发现了大量影响临床终点和表型的遗传区域（“基因座”），许多在编码区间之外。理解这些关联的生物学基础的一种方法是探索来自中间细胞表型（特别是基因表达）的GWAS信号是否位于相同的基因座（“共定位”）并且潜在地介导疾病信号。然而，目前尚不清楚如何评估是否相同的变体负责两个GWAS信号，或者是否是彼此接近的不同因果变体。在本文中，我们描述了一种统计方法，可以使用简单的单变量汇总统计来测试GWAS信号的共定位。我们描述了我们的方法的一个应用，血脂和肝脏表达的荟萃分析，虽然任何两个数据集产生的关联研究可以使用。我们的方法能够检测由调节效应解释的GWAS信号的子集，并识别受相同GWAS变体影响的候选基因。随着总结GWAS数据的日益可用，应用共定位方法整合研究结果对于功能随访至关重要，并且对于鉴定eQTL数据集中的组织特异性信号也特别有用。
Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways. Genome-wide association studies (GWAS) have found a large number of genetic regions (“loci”) affecting clinical end-points and phenotypes, many outside coding intervals. One approach to understanding the biological basis of these associations has been to explore whether GWAS signals from intermediate cellular phenotypes, in particular gene expression, are located in the same loci (“colocalise”) and are potentially mediating the disease signals. However, it is not clear how to assess whether the same variants are responsible for the two GWAS signals or whether it is distinct causal variants close to each other. In this paper, we describe a statistical method that can use simply single variant summary statistics to test for colocalisation of GWAS signals. We describe one application of our method to a meta-analysis of blood lipids and liver expression, although any two datasets resulting from association studies can be used. Our method is able to detect the subset of GWAS signals explained by regulatory effects and identify candidate genes affected by the same GWAS variants. As summary GWAS data are increasingly available, applications of colocalisation methods to integrate the findings will be essential for functional follow-up, and will also be particularly useful to identify tissue specific signals in eQTL datasets.
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使用基因表达研究复杂疾病的遗传基础。
影响因子：
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作者：
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通讯作者：
van Heel, David A.
影响因子：
3.8
作者：
Nasarre, L.;Juan-Babot, O.;Llorente-Cortes, V.
通讯作者：
Llorente-Cortes, V.