Integrating Genetics of Gene Expression into Pathway Analysis for Melanoma GWAS

全基因组关联研究（GWASs）已经成功地鉴定了一些单核苷酸 与黑色素瘤风险相关的SNP。然而，传统的GWAS仅关注边缘 个体标记物的影响，并且仅在识别出鲁棒性后才纳入外部功能信息。 统计协会。它通常缺乏检测大多数遗传变异所带来的相对较小的影响的能力。 基于途径的方法，评估生物途径内基因的累积贡献， 这可能有助于收集GWAS数据中包含的适度信号，并确定病因学中的生物学途径。 疾病的一种途径。如在传统途径方法中所使用的，将SNP分配到它们的物理位置， 位置可能会引入许多假阳性关联，来自对非功能性SNP和缺失的多次测试。 注释调节基因表达的SNP。相反，定义数量性状的表达 基因座（eQTL），并将它们分配到它们调节的基因中，可能有助于功能性地注释SNP， 在途径分析中富集功能变体。在GWAS中具有弱关联的众多SNP中， 利用整合基因遗传学的途径分析，从已鉴定的途径中筛选 与偶然相比，GWAS表达和用于复制的GWAS数据可以增加靶向真实信号的可能性。 将肝脏和脂肪组织eQTL整合到2型糖尿病GWAS的途径分析中， 成功地确定了几种新的疾病相关途径。最近，一个关于全球基因表达的GWAS 的皮肤有系统地产生皮肤eQTL。然而，尚未对以下因素进行路径分析： 黑色素瘤GWAS目前建议的目标是系统地评估生物学的关联， 通过在发现阶段将这种新方法应用于黑色素瘤GWAS， 在复制阶段验证所鉴定的通路内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，以调查以下因素的病因学贡献： 识别的途径/SNP。我们计划对420例黑色素瘤病例进行巢式黑色素瘤病例对照研究， 在两个大型的、特征良好的队列中的2，284名对照，护士健康研究（NHS）和健康研究（NHS）。 专业人员随访研究（HPFS）的发现阶段，并使用黑色素瘤病例对照研究1804 来自MD安德森癌症中心的黑色素瘤病例和1，026名对照在复制阶段。所有病例 和对照组之前已经在Illumina芯片上进行了基因分型。预先存在的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是一个具有成本效益的机会。我们提出的研究将是第一个 联合收割机将基因表达的遗传学和基因的功能分类作为黑色素瘤的先验信息 GWAS。这项研究的结果将从GWAS数据中挑选出具有适度影响的遗传变异， 在更大程度上利用GWAS数据，并为黑色素瘤的病因学提供新的见解。

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.