Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping-猫眼课题宝

DOI：

10.1101/015164

发表时间：

2015-06

期刊：

PLoS Genetics

影响因子：

4.5

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

C. Wallace;A. Cutler;Nikolas Pontikos;M. Pekalski;O. Burren;J. Cooper;Arcadio Rubio García;R. Ferreira;Hui Guo;N. Walker;D. Smyth;S. Rich;S. Onengut-Gumuscu;S. Sawcer;M. Ban;S. Richardson;J. Todd;L. Wicker

由于连锁不平衡（LD）和多重关联信号，与常见疾病风险相关的区域中候选因果变异的识别变得复杂。尽管如此，仍然需要这些变异的准确图谱，以便充分利用详细的细胞特异性染色质注释数据来突出疾病因果机制和细胞，并设计最终需要确认因果机制的功能研究。我们将贝叶斯进化随机搜索算法应用于精细映射问题，并通过模拟研究证明了其相对于传统逐步和正则回归的改进性能。然后，我们应用它来精细绘制 IL-2RA (CD25) 基因区域中已建立的多发性硬化症 (MS) 和 1 型糖尿病 (T1D) 关联。对于 T1D，逐步和随机搜索方法都识别出四个 T1D 关联信号，主要效应由单核苷酸多态性 rs12722496 标记。相比之下，对于 MS，随机搜索发现了两个不同的竞争模型：一个候选因果变异，由 rs2104286 标记，之前使用逐步分析进行报告；以及具有两个关联信号的更复杂模型，其中一个由主要 T1D 相关 rs12722496 标记，另一个由 rs56382813 标记。 rs2104286 与 rs12722496 和 rs56382813 之间存在低至中度 LD (r2 ≃ 0.3)，并且我们的两个 SNP 模型在对 rs2104286 进行调节后无法通过向前逐步搜索来恢复。 MS 的两种变异模型中的两种信号都会影响 CD4+ T 细胞不同亚群的 CD25 表达，而 CD4+ T 细胞是自身免疫过程中的关键细胞。结果支持 T1D 和 MS 存在共同的因果变异。我们的研究说明了使用专门设计的模型搜索策略进行精细绘图的好处以及结合疾病和蛋白质表达数据的优势。

Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium (LD) and multiple association signals. Nonetheless, accurate maps of these variants are needed, both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells, and for design of the functional studies that will ultimately be required to confirm causal mechanisms. We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem, and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies. We then applied it to fine map the established multiple sclerosis (MS) and type 1 diabetes (T1D) associations in the IL-2RA (CD25) gene region. For T1D, both stepwise and stochastic search approaches identified four T1D association signals, with the major effect tagged by the single nucleotide polymorphism, rs12722496. In contrast, for MS, the stochastic search found two distinct competing models: a single candidate causal variant, tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals, one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813. There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 (r2 ≃ 0.3) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286. Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells, which are key cells in the autoimmune process. The results support a shared causal variant for T1D and MS. Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data.

Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping

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