Integrating Genetics of Gene Expression into Pathway Analysis for Melanoma GWAS

全基因组关联研究(GWAS)已经成功地确定了一些单核苷酸 与黑色素瘤风险相关的基因多态性(SNP)。然而，传统的GWAS只关注边缘 单个标记的影响，并且仅在识别稳健性之后才结合外部功能信息 统计关联。它往往缺乏能力来检测大多数基因变异带来的相对较小的影响。 基于途径的方法评估生物途径中基因的累积贡献， 可能有助于收集GWAS数据中包含的适度信号，并确定致病原因中的生物学途径 疾病在路径层面上。就像在传统的途径方法中使用的那样，将SNPs分配给他们的物理 位置可能会引入许多假阳性关联，这些关联来自对非功能SNPs和未命中的多次测试 注释在距离中调节基因表达的SNP。相反，定义表达数量性状 基因座(EQTL)并将其分配到它们所调控的基因中可能有助于从功能上诠释SNPs并增加 途径分析中的功能变异体的丰富。在GWA中具有弱关联性的众多SNP中， 结合基因遗传学的通径分析在已鉴定途径中的筛选 表达和用于复制的GWAS数据可以比偶然地增加以真信号为靶点的可能性。 将肝脏和脂肪组织的eQTL整合到2型糖尿病GWAS的通路分析中 成功地确定了几种与疾病相关的新途径。最近，一项关于全球基因表达的GWA 系统地产生了皮肤eQTL。然而，还没有对其进行路径分析 黑色素瘤。当前提案的目标是系统地评估生物与 在黑色素瘤发现阶段将这一新方法应用于黑色素瘤风险的途径 在复制阶段验证与黑色素瘤风险相关的特定基因座。调解 还将对潜在的中间表型进行分析，以调查病因的贡献。 已识别的途径/SNP。我们计划对420例黑色素瘤病例进行嵌套式黑色素瘤病例对照研究。 在两个大的、特征良好的队列中进行了2,284名对照，护士健康研究(NHS)和健康 专业人员在发现阶段进行随访研究(HPFS)，并使用1,804例黑色素瘤病例对照研究 来自MD Anderson癌症中心的1,026例黑色素瘤病例和1,026名对照处于复制阶段。所有的案子 而对照之前已经在Illumina芯片上进行了基因分型。先前存在的GWAS型数据为我们提供了一个 将新方法应用于黑色素瘤研究具有成本效益的机会。我们提议的研究将是第一个 将基因表达的遗传学和基因的功能分类结合起来作为黑色素瘤的先验信息 GWARD。这项研究的结果将从Gwas的数据中提取影响不大的遗传变异，这将 更大程度地利用Gwas的数据，为黑色素瘤的病因提供新的见解。

The genome-wide association studies (GWASs) have successfully identified a number of single nucleotide polymorphisms (SNPs) associated with melanoma risk. However, the traditional GWASs focus only on marginal effects of individual markers and have incorporated external functional information only after identifying robust statistical associations. It is often lack power to detect relatively small effects conferred by most genetic variants. The pathway-based approaches, which evaluate the cumulative contribution of the genes within biological pathways, may help collect the modest signals contained in the GWAS data and identify biological pathways in the etiology of disease on a pathway level. As used in the traditional pathway approaches, assigning SNPs to their physical location may introduce many false positive associations from multiple testing on non-functional SNPs and miss- annotate SNPs that regulate the expression of genes in distance. Instead, defining the expression quantitative trait loci (eQTLs) and assigning them into the genes they regulate may help functionally annotate SNPs and increase the enrichment of functional variants in the pathway analysis. Among numerous SNPs with weak associations in GWAS, the selection of those from the identified pathways by using the pathway analysis integrating the genetics of gene expression and the GWAS data for replication may increase the likelihood of targeting true signals than by chance. The integration of eQTLs of liver and adipose tissues into the pathway analysis for the GWAS of type 2 diabetes has successfully identified several novel disease-related pathways. More recently, a GWAS on global gene expression of the skin have systematically generated skin eQTLs. However, no pathway analysis has been conducted for melanoma GWAS. The goal of the current proposal is to systematically assess the associations of biological pathways with melanoma risk by applying this new approach to melanoma GWAS in the discovery stage and to validate specific loci associated with melanoma risk within the identified pathways in the replication stage. Mediation analysis on potentially intermediate phenotypes will also be conducted to investigate the etiological contribution of the identified pathways/SNPs. We plan to use a nested melanoma case-control study of 420 melanoma cases and 2,284 controls in two large, well-characterized cohorts, the Nurses’ Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) in the discovery stage, and use a melanoma case-control study of 1,804 melanoma cases and 1,026 controls from the MD Anderson Cancer Center in the replication stage. All the cases and controls have been previously genotyped on Illumina chips. The pre-existing GWAS genotype data gives us a cost-effective opportunity to apply the new approach to melanoma research. Our proposed study would be the first to combine the genetics of gene expression and functional classification of genes as prior information to melanoma GWAS. Findings from this study will pick up the genetic variants with modest effects from the GWAS data, which will utilize the GWAS data to a greater extent and provide new insights into the etiology of melanoma.