Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations.
Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations.
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DOI：
10.1371/journal.pgen.1000895
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发表时间：
2010-04-01
期刊：
影响因子：
4.5
通讯作者：
Dermitzakis ET
中科院分区：
文献类型：
--
作者：
Nica AC;Montgomery SB;Dimas AS;Stranger BE;Beazley C;Barroso I;Dermitzakis ET
全基因组关联研究（GWAS）最近取得了成功，现在面临的挑战是确定报告的易感性变异如何介导复杂的性状和疾病。表达数量性状基因座（eQTL）通过与GWAS信号的重叠而与疾病相关。然而，基因组中丰富的eQTL和强相关结构（LD）使得这些重叠中的一些可能是巧合的，而不是由相同的功能变体驱动的。在本研究中，我们提出了一个实证的方法，我们称之为监管性状一致性（RTC），占当地LD结构和整合eQTL和GWAS结果，以揭示子集的关联信号是由于顺eQTL。我们模拟了具有单个或两个因果变体的各种LD模式的基因组区域，并表明我们的得分优于SNP相关性度量，无论是统计（r2）还是历史（D '）。在观察到目前已发表的GWAS基因座中大量的调节信号后，我们应用我们的方法，目标是对每个相应复杂性状的相关基因进行优先排序。我们检测到几种潜在的致病调节作用，具有对免疫相关条件的强烈富集，与测试的细胞系（LCL）的性质一致。此外，我们提出了一个扩展的方法在反式，其中询问整个基因组的下游影响的疾病变异可以提供有关其未知的主要生物学效应。我们的结论是，整合细胞表型协会与有机体复杂的性状将有利于生物学解释这些性状的遗传效应。全基因组关联研究已经导致了各种人类复杂性状的易感基因座的鉴定。然而，在很大程度上仍然缺少的是对这些候选变异体作用的生物学背景以及它们如何决定每个特征的理解。鉴于许多GWAS基因座在编码区外的定位和调控变异在形成表型变异中的重要作用，基因表达已被提出作为一种合理的信息中间表型。在这里，我们表明，对于目前发表的GWAS的一个子集，这确实是这样的情况下，通过观察疾病位点之间的监管变异显着过剩。我们提出了一种经验方法（监管性状一致性-RTC），能够整合表达和疾病的数据，以检测因果监管的影响。我们表明，RTC优于简单的相关性指标在各种模拟的连锁不平衡（LD）的情况下。我们的方法能够从文献中恢复先前怀疑的因果调节效应，并且正如预期的那样，考虑到测试组织的性质，观察到免疫相关候选物的过度表达。随着可用组织数量的增加，这种优先排序方法将在理解疾病病因学中调节变体的含义方面变得更加有用。
The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits. Genome-wide association studies have led to the identification of susceptibility loci for a variety of human complex traits. What is still largely missing, however, is the understanding of the biological context in which these candidate variants act and of how they determine each trait. Given the localization of many GWAS loci outside coding regions and the important role of regulatory variation in shaping phenotypic variance, gene expression has been proposed as a plausible informative intermediate phenotype. Here we show that for a subset of the currently published GWAS this is indeed the case, by observing a significant excess of regulatory variants among disease loci. We propose an empirical methodology (regulatory trait concordance—RTC) able to integrate expression and disease data in order to detect causal regulatory effects. We show that the RTC outperforms simple correlation metrics under various simulated linkage disequilibrium (LD) scenarios. Our method is able to recover previously suspected causal regulatory effects from the literature and, as expected given the nature of the tested tissue, an overrepresentation of immunity-related candidates is observed. As the number of available tissues will increase, this prioritization approach will become even more useful in understanding the implication of regulatory variants in disease etiology.
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