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性（snp）。然而，传统的GWASs只关注单个标记的边际效应，并且只有在确定了强大的统计关联之后才纳入外部功能信息，这通常会错过大多数遗传变异所带来的相对较小的影响。基于通路的方法评估了基因在生物通路中的累积贡献，可能有助于收集嵌入在GWASs中的适度信号，并在通路水平上识别疾病相关通路。然而，传统的通路分析简单地根据物理位置将SNPs分配到附近的基因中，这可能会由于对非功能性SNPs的多次检测而导致许多假阳性关联，并且错误地注释了远距离调节基因表达的SNPs。基因表达GWASs在基因组尺度上研究调控基因表达的遗传变异，已经鉴定出数千个影响基因表达的表达数量性状位点（eqtl）。定义eqtl并将其分配到它们所调节的基因中，可能有助于在生物学途径中对snp进行功能注释，并增加功能变异的富集。选择途径分析鉴定的复制途径中的eqtl可以增加靶向真实信号的可能性，而不是偶然的。肝脏eqtl的整合

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.