Integrating Genetics of Gene Expression into Pathway Analysis for Melanoma GWAS

描述（由申请人提供）：全基因组关联研究（GWAS）已成功鉴定了许多与黑色素瘤风险相关的单核苷酸多态性（SNP）。然而，传统的GWAS仅关注单个标记的边际效应，并且仅在识别出强大的统计关联后才纳入外部功能信息，这通常会错过大多数遗传变异所赋予的相对较小的效应。基于通路的方法，评估生物通路内基因的累积贡献，可能有助于收集嵌入在GWAS中的适度信号，并在通路水平上识别疾病相关通路。然而，传统的途径分析简单地基于物理位置将SNP分配到附近的基因中，这可能由于对非功能性SNP的多次测试而引入许多假阳性关联，并且错误地注释了调节基因表达的SNP的距离。基因表达的GWAS研究在基因组规模上调节基因表达的遗传变异，已经确定了数千个影响基因表达的表达数量性状位点（eQTL）。定义eQTL并将其分配到它们调节的基因中可能有助于功能性注释SNP并增加生物途径中功能变体的富集。通过途径分析鉴定的途径中的eQTL的选择用于复制可能比偶然地增加靶向真实信号的可能性。肝脏eQTL的整合 和脂肪组织中的GWAS的2型糖尿病的通路分析已经成功地确定了几个疾病相关的途径。最近，关于皮肤的全局基因表达的GWAS系统地产生了皮肤eQTL。本申请的目的是通过在发现阶段将皮肤eQTL整合到黑色素瘤GWAS的途径分析中来评估生物途径与黑色素瘤风险的关联，并在复制阶段验证所鉴定的途径内的特定基因座的关联。将对潜在的中间表型进行介导分析，以研究所确定的途径/SNP的病因学贡献。我们计划在发现阶段使用两个大的、充分表征的队列（护士健康研究和健康专业人员随访研究）中的420例黑色素瘤病例和2，284例对照的巢式黑色素瘤病例对照研究，并在复制阶段使用来自MD安德森癌症中心的1，804例黑色素瘤病例和1，026例对照的黑色素瘤病例对照研究。所有病例和对照组先前都在Illumina SNP芯片上进行了基因分型。现有的GWAS基因型数据为我们提供了一个将新方法应用于黑色素瘤研究的具有成本效益的机会。我们提出的研究将是第一个联合收割机基因表达的遗传学和基因的功能分类作为先验信息应用于黑色素瘤GWAS。这项创新工作将在更大程度上利用全球WAS数据。这项研究的结果将从GWAS数据中识别具有适度影响的遗传变异，并为黑色素瘤的病因学提供新的见解。

DESCRIPTION (provided by applicant): The genome-wide association studies (GWAS) 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, which often miss relatively small effects conferred by most genetic variants. The pathway-based approaches, which evaluate the cumulative contribution of genes within biological pathways, may help collect the modest signals embedded in GWASs and identify the disease-related pathways on a pathway level. Though, the traditional pathway analyses simply assign the SNPs into nearby genes based on physical location, which may introduce numerous false positive associations due to multiple testing on non- functional SNPs and mis-annotate the SNPs regulating gene expression in distance. GWASs on gene expression that study the genetic variants regulating gene expression at a genomic scale have identified thousands of expression quantitative trait loci (eQTLs) that affect gene expression. Defining the eQTLs and assigning them into the genes that they regulate may help functionally annotate SNPs and increase the enrichment of functional variants in the biological pathways. The selection of the eQTLs in the pathways identified by pathway analysis 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 disease-related pathways. More recently, a GWAS on global gene expression of the skin has systematically generated skin eQTLs. The goal of the current application is to assess the associations of biological pathways with melanoma risk by integrating the skin eQTLs into the pathway analysis for melanoma GWAS in the discovery stage and to validate the associations of specific loci within the identified pathways in the replication stage. Mediatio analysis on potential intermediate phenotypes will 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 and the Health Professionals Follow-up Study 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 SNP chips. The existing GWAS genotype data give 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 apply in melanoma GWAS. This innovative work will utilize the GWAS data to a greater extent. Findings from this study will identify the genetic variants with modest effects from the GWAS data and provide new insights into the etiology of melanoma.