Integrative functional characterization of genetic loci for cutaneous basal cell carcinoma

皮肤基底细胞癌(BCC)是美国最常见的恶性肿瘤。 在更年轻的个体中变得更加常见。与BCC相关的沉重的公共卫生负担 强调针对这一问题进行有效管理和预防的重要性 恶性，特别是针对高危人群。我们最近发表了一份全基因组的大型研究报告 对17,187例基底细胞癌和287,054例对照进行了相关性研究。我们给出了一个31个基因座的列表 进一步研究以阐明真正的因果变异和特定的基因或DNA功能元件 基因变异通过它来发挥它们的作用。在本应用程序中，我们的目标是实现第一个 良好定位后对BCC的调查，以整合补充战略，包括精细- 作图和转录组范围的关联研究(TWAS)，目标是潜在的因果变异和 功能相关的基因。我们提出了以下具体目标：(1)执行经验贝叶斯精细化- 使用概率注释积分器(PAINTOR)软件进行映射以预测因果单项 核苷酸多态(SNP)。PAINTOR将汇总地理信息系统数据、关于当地联系的信息 不平衡(LD)模式，以及来自公共可用的SNP的功能注释 数据库(如ENCODE、Eigenome Roadmap、GTEx和代谢GWAS服务器)。(2)表演 Twas分析，将当前BCC上的GWAS元数据与Expression中的数据进行集成 数量性状基因座(EQTL)研究以确定其表达水平显著 与密件抄送风险相关。几种新的和新兴的可靠的方法将被用于我们的TWAS (包括我们开发的多变稳健MR-Egger回归的扩展)以减少 由于这些混杂因素造成的假阳性风险。我们将使用以下工具进行一系列TWAS分析 利用血液和皮肤组织样本中的eQTL数据区分具有皮肤特异性eQTL的SNP 效应和具有跨组织效应的效应。这项研究不仅将极大地促进我们对 BCC发病机制的研究，也为开发临床有用的基因提供了强有力的科学基础 精准防范的风险预测模型。此外，这项拟议的研究可能会精准一些 治疗干预和降低风险的潜在/可操作目标。

Cutaneous basal cell carcinoma (BCC) is the most common malignancy diagnosed in the USA and is becoming more frequent in younger individuals. The heavy public health burden associated with BCC underscores the importance of efficient management and prevention efforts directed toward this malignancy, especially targeting the high-risk population. We recently published a large genome-wide association study (GWAS) on BCC with 17,187 cases and 287,054 controls. We yield a list of 31 loci for further investigation to elucidate true causal variants and the specific genes or DNA functional elements through which the genetic variants exert their effects. In this application, we aim to carry out the first well-positioned post-GWAS investigation of BCC to integrate complementary strategies including fine- mapping and transcriptome-wide association study (TWAS), targeting potentially causal variants and functionally relevant genes. We propose the following specific aims: (1) Perform empirical Bayes fine- mapping using the Probabilistic Annotation INTegratOR (PAINTOR) software to predict causal single- nucleotide polymorphisms (SNPs). PAINTOR will summarize GWAS data, information on local linkage disequilibrium (LD) patterns, as well as functional annotations for SNPs from publicly available databases (e.g. ENCODE, Epigenome Roadmap, GTEx, and Metabolomic GWAS Server). (2) Perform TWAS analysis, which integrates current GWAS-meta data on BCC with data from expression quantitative trait loci (eQTL) studies to identify genes whose expression levels are significantly associated with BCC risk. Several novel and emerging robust approaches will be used in our TWAS (including an extension of pleiotropy-robust MR-Egger regression that we have developed) to reduce the risk of false positives due to these confounders. We will conduct a series of TWAS analyses using eQTL data from blood samples and skin tissue samples to distinguish SNPs with skin-specific eQTL effects and those with cross-tissue effects. This study will not only greatly advance our understanding of BCC pathogenesis, but also provide a robust scientific basis for developing clinically useful genetic risk prediction model for precision prevention. Moreover, this proposed study may pinpoint some potential/actionable targets for therapeutic intervention and risk reduction.