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)已经成功地确定了一些与黑色素瘤风险相关的单核苷酸多态(SNPs)。然而，传统的GWAS只关注单个标记的边际效应，并且只有在确定了稳健的统计关联之后才纳入外部功能信息，这往往忽略了大多数遗传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中嵌入的适度信号，并在途径水平上识别与疾病相关的途径。然而，传统的途径分析只是简单地根据物理位置将SNPs分配到附近的基因，这可能会由于对非功能SNPs的多次测试而引入大量的假阳性关联，并在距离上错误地诠释了调控基因表达的SNPs。在基因组水平上研究调控基因表达的遗传变异的基因表达GWAS已经确定了数千个影响基因表达的表达数量性状基因座(EQTL)。定义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注释SNPs，并增加生物途径中功能变异的丰富。在通过路径分析确定的复制路径中选择eQTL可以比偶然地增加靶向真实信号的可能性。肝脏eQTL的整合 而脂肪组织进入的通路分析对于2型糖尿病的GWA已经成功地确定了几条与疾病相关的通路。最近，一项关于皮肤全球基因表达的研究已经系统地产生了皮肤eQTL。目前应用的目的是通过在发现阶段将皮肤eQTL整合到黑色素瘤GWAs的路径分析中来评估生物路径与黑色素瘤风险的关联，并在复制阶段验证已识别的路径内特定基因的关联。将对潜在的中间表型进行中介分析，以调查已识别的途径/SNPs的病因学贡献。我们计划在发现阶段使用针对420例黑色素瘤病例和2284例对照的嵌套黑色素瘤病例对照研究，并在复制阶段使用MD Anderson癌症中心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。所有病例和对照之前都已在Illumina SNP芯片上进行了基因分型。现有的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的经济有效的机会。我们提出的研究将是首次将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的研究。这项创新工作将在更大程度上利用全球气候变化网络的数据。这项研究的发现将从Gwas的数据中识别出影响不大的遗传变异，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.