Leveraging allelic imbalance to refine fine-mapping for eQTL studies
Leveraging allelic imbalance to refine fine-mapping for eQTL studies
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DOI：
10.1371/journal.pgen.1008481
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发表时间：
2019-12-01
期刊：
影响因子：
4.5
通讯作者：
Eskin, Eleazar
中科院分区：
文献类型：
--
作者：
Zou, Jennifer;Hormozdiari, Farhad;Eskin, Eleazar
在全基因组关联研究中鉴定的许多疾病风险位点存在于基因组的非编码区中。以往的研究发现，丰富的表达数量性状基因座（eQTL）的疾病风险位点，表明鉴定基因表达的因果变异是重要的，不仅阐明基因表达的遗传基础，而且复杂的性状。然而，由于变异之间复杂的遗传相关性（称为连锁不平衡（LD））和一个基因座内存在多个致病变异，检测致病变异具有挑战性。虽然已经开发了几种精细映射方法来克服这些挑战，但当真正的因果变体与许多非因果变体处于高LD时，它们可能会产生大量推定的因果变体。在eQTL研究中，还有一个额外的信息来源可用于改善称为等位基因不平衡（AIM）的精细定位，该信息来源可测量二倍体生物体两条染色体上基因表达的不平衡。在这项工作中，我们开发了一种新的统计方法，利用AIM和总表达数据来检测调节基因表达的因果变异。我们通过模拟和应用于基因型-组织表达（GTEx）数据集的10个组织来说明，我们的方法比仅使用eQTL信息的方法具有更高的特异性来识别真正的因果变异。在所有组织和基因中，我们的方法在推定的因果变异数量上实现了11%的中位数减少率。我们使用来自路线图表观基因组学联盟的染色质状态数据来表明，我们的方法识别的推定因果变体富集了基因组的活性区域，为我们的方法识别具有更高特异性的因果变体提供了正交支持。
Many disease risk loci identified in genome-wide association studies are present in non-coding regions of the genome. Previous studies have found enrichment of expression quantitative trait loci (eQTLs) in disease risk loci, indicating that identifying causal variants for gene expression is important for elucidating the genetic basis of not only gene expression but also complex traits. However, detecting causal variants is challenging due to complex genetic correlation among variants known as linkage disequilibrium (LD) and the presence of multiple causal variants within a locus. Although several fine-mapping approaches have been developed to overcome these challenges, they may produce large sets of putative causal variants when true causal variants are in high LD with many non-causal variants. In eQTL studies, there is an additional source of information that can be used to improve fine-mapping called allelic imbalance (AIM) that measures imbalance in gene expression on two chromosomes of a diploid organism. In this work, we develop a novel statistical method that leverages both AIM and total expression data to detect causal variants that regulate gene expression. We illustrate through simulations and application to 10 tissues of the Genotype-Tissue Expression (GTEx) dataset that our method identifies the true causal variants with higher specificity than an approach that uses only eQTL information. Across all tissues and genes, our method achieves a median reduction rate of 11% in the number of putative causal variants. We use chromatin state data from the Roadmap Epigenomics Consortium to show that the putative causal variants identified by our method are enriched for active regions of the genome, providing orthogonal support that our method identifies causal variants with increased specificity.