Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease.
Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease.
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DOI：
10.1371/journal.pgen.1007071
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发表时间：
2017-10
期刊：
影响因子：
4.5
通讯作者：
Vyse TJ
中科院分区：
文献类型：
--
作者：
Odhams CA;Cunninghame Graham DS;Vyse TJ
全基因组关联研究已经确定了数百个自身免疫性疾病的风险位点，但只有少数（约25%）与免疫细胞中的基因表达变化（eQTL）共享遗传效应。在全基因分辨率下基于RNA-Seq的定量，其中通过相同基因的所有转录物或外显子的最终表达来估计丰度，可能解释了这种观察到的共定位的缺乏，因为可以隐藏独立外显子中的细微同种型转换和表达变异。我们使用来自20种自身免疫性疾病（560个独立位点）的关联统计数据和来自Geuvadis队列的373名个体的RNA-Seq数据进行了整合性顺式eQTL分析，这些数据在淋巴母细胞系的基因、亚型、外显子、连接和内含子水平上进行了分析。在使用联合似然作图和监管性状一致性框架对共享的因果变异进行严格测试后，我们发现基因水平的量化显著低估了因果顺式eQTL的数量。只有5.0-5.3%的位点在基因水平上共享一个cis-eQTL，而在外显子水平上为12.9-18.4%，在连接点水平上为9.6-10.5%。超过五分之一的自身免疫基因座通过结合所有五种量化类型在单个细胞类型中共享潜在的因果变异;比目前稳态因果顺式eQTL的估计值显著增加。在不同定量类型下检测到的因果顺式eQTL定位于离散的表观遗传注释。我们应用线性混合效应模型来区分调节基因的所有表达元件的顺式eQTL和仅在元件子集中信号明显的那些顺式eQTL。外显子水平分析检测到疾病相关的顺式eQTL，微妙地改变了整个靶基因的转录。我们详细剖析了系统性红斑狼疮的遗传关联，并对候选基因进行了功能注释。许多已知的和新的基因在基因水平上被隐藏（例如IKZF 2、TYK 2、LYST）。我们的研究结果作为网络资源提供。众所周知，非编码遗传变异通过改变基因表达水平（称为eQTL）而导致疾病易感性。鉴定与疾病风险和表达水平变化有关的变异并不容易，我们认为这部分是由于如何使用RNA测序（RNA-Seq）定量表达。eQTL分析中通常使用全基因表达，其中通过相同基因的所有转录物或外显子的最终表达来估计丰度。这种低分辨率可能隐藏了独立外显子中的细微亚型转换和表达变异。使用亚型，外显子和连接水平的定量不仅可以指向候选基因参与，但也涉及特定的转录本。我们利用现有的RNA-Seq表达数据在基因，亚型，外显子，连接，和内含子水平，并进行eQTL分析，使用关联数据从20个自身免疫性疾病。我们发现外显子水平和连接水平的分析完全优于基因水平的分析，并且通过利用所有五种量化类型，我们发现>20%的自身免疫基因座与基因表达共享单一遗传效应。我们强调，使用RNA-Seq的现有和新的eQTL队列应该在多个分辨率下分析表达，以最大限度地提高检测因果eQTL和候选基因的能力。
Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share genetic effects with changes to gene expression (eQTLs) in immune cells. RNA-Seq based quantification at whole-gene resolution, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is likely to account for this observed lack of colocalisation as subtle isoform switches and expression variation in independent exons can be concealed. We performed integrative cis-eQTL analysis using association statistics from twenty autoimmune diseases (560 independent loci) and RNA-Seq data from 373 individuals of the Geuvadis cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution in lymphoblastoid cell lines. After stringently testing for a shared causal variant using both the Joint Likelihood Mapping and Regulatory Trait Concordance frameworks, we found that gene-level quantification significantly underestimated the number of causal cis-eQTLs. Only 5.0–5.3% of loci were found to share a causal cis-eQTL at gene-level compared to 12.9–18.4% at exon-level and 9.6–10.5% at junction-level. More than a fifth of autoimmune loci shared an underlying causal variant in a single cell type by combining all five quantification types; a marked increase over current estimates of steady-state causal cis-eQTLs. Causal cis-eQTLs detected at different quantification types localised to discrete epigenetic annotations. We applied a linear mixed-effects model to distinguish cis-eQTLs modulating all expression elements of a gene from those where the signal is only evident in a subset of elements. Exon-level analysis detected disease-associated cis-eQTLs that subtly altered transcription globally across the target gene. We dissected in detail the genetic associations of systemic lupus erythematosus and functionally annotated the candidate genes. Many of the known and novel genes were concealed at gene-level (e.g. IKZF2, TYK2, LYST). Our findings are provided as a web resource. It is well acknowledged that non-coding genetic variants contribute to disease susceptibility through alteration of gene expression levels (known as eQTLs). Identifying the variants that are causal to both disease risk and changes to expression levels has not been easy and we believe this is in part due to how expression is quantified using RNA-Sequencing (RNA-Seq). Whole-gene expression, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is conventionally used in eQTL analysis. This low resolution may conceal subtle isoform switches and expression variation in independent exons. Using isoform-, exon-, and junction-level quantification can not only point to the candidate genes involved, but also the specific transcripts implicated. We make use of existing RNA-Seq expression data profiled at gene-, isoform-, exon-, junction-, and intron-level, and perform eQTL analysis using association data from twenty autoimmune diseases. We find exon-, and junction-level thoroughly outperform gene-level analysis, and by leveraging all five quantification types, we find >20% of autoimmune loci share a single genetic effect with gene expression. We highlight that existing and new eQTL cohorts using RNA-Seq should profile expression at multiple resolutions to maximise the ability to detect causal eQTLs and candidate genes.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
3.7
作者：
Laiho A;Elo LL
通讯作者：
Elo LL
影响因子：
7
作者：
Battle A;Mostafavi S;Zhu X;Potash JB;Weissman MM;McCormick C;Haudenschild CD;Beckman KB;Shi J;Mei R;Urban AE;Montgomery SB;Levinson DF;Koller D
通讯作者：
Koller D
影响因子：
46.9
作者：
Gaidatzis, Dimos;Burger, Lukas;Stadler, Michael B.
通讯作者：
Stadler, Michael B.