Utilizing Interaction to Identify Novel Genetic Factors for Nicotine Dependence

描述（由申请人提供）：吸烟是癌症、心脏病、中风和肺病的主要诱因，也是美国和世界范围内可预防死亡的主要原因。尼古丁依赖（ND）和其他吸烟表型的全基因组关联研究（GWAS）已经明确地确定了与染色体15q25 （CHRNA5-CHRNA3-CHRNB4）和染色体8p11 （CHRNB3- CHRNA6）上跨越烟碱乙酰胆碱受体（nAChR）基因的单核苷酸多态性（snp）的关联。功能变异改变戒烟治疗反应，并与吸烟的健康后果独立相关。然而，它们解释了吸烟的表型变异的<10%。在本研究中，我们将超越标准的GWAS，进行第一次使用基因-基因相互作用应用联合2自由度（2df）统计方法的研究。这将是最大的ND全基因组研究。新开发的联合2df方法同时考虑了SNP的主效应和交互效应，但不专注于检测相互作用本身。相反，该方法利用相互作用来增加SNP关联测试的能力。当考虑到基因与环境的相互作用时，它已被用于成功识别其他复杂性状的标准GWAS中遗漏的SNP关联。在这里，我们将应用联合2df方法同时考虑SNP主要关联及其与已建立的nAChR变体的相互作用。我们利用现有的GWAS数据，这些数据来自我们的ND协作遗传研究（COGEND）、deCODE Genetics和其他通过基因型和表型数据库（dbGaP）和尼古丁依赖法格斯特罗姆测试（FTND）数据提供的研究：总共有23,000名参与者。在Aim1中，我们将从14个研究样本中获得全基因组SNP基因型数据，并协调它们的FTND表型数据。使用COGEND和deCODE Genetics（总N=10,319名高加索人），我们将并行进行ND的全基因组联合2df荟萃分析，考虑与CHRNA5- CHRNA3-CHRNB4 （Aim 2）或CHRNB3-CHRNA6 （Aim 3）的相互作用。联合2df荟萃分析中排名靠前的snp将使用生物信息学分析来评估其注释、功能和调控潜力。在Aim 4a中，我们将选择

DESCRIPTION (provided by applicant): Cigarette smoking is a major contributor to cancer, heartdisease, stroke, and lung disease and is the leading cause of preventable mortality in the United States and worldwide. Genome-wide association studies (GWAS) of nicotine dependence (ND) and other smoking phenotypes have unequivocally identified associations with single nucleotide polymorphisms (SNPs) spanning the nicotinic acetylcholine receptor (nAChR) genes on chromosome 15q25 (CHRNA5-CHRNA3-CHRNB4) and on chromosome 8p11 (CHRNB3- CHRNA6). The functional variants alter cessation treatment response and are independently associated with health consequences of smoking. However, they explain <10% of the phenotypic variability for smoking. In this study, we will move beyond standard GWAS, conducting the first study to apply the joint 2 degree-of- freedom (2df) statistical method using gene-gene interaction. It will be the largest genome-wide study of ND. The newly developed joint 2df method simultaneously considers SNP main and interactive effects but does not focus on detecting interaction per se. Instead, the method leverages interaction to increase power of the SNP association test. It has been used to successfully identify SNP associations missed in standard GWAS for other complex traits when accounting for gene-environment interactions. Here, we will apply the joint 2df method to simultaneously consider SNP main associations and their interactions with the established nAChR variants. We capitalize on existing GWAS data from our Collaborative Genetic Study of ND (COGEND), deCODE Genetics, and other studies available via the Database of Genotypes and Phenotypes (dbGaP) with Fagerstrom Test for Nicotine Dependence (FTND) data: collective size of >23,000 participants. In Aim1, we will obtain genome-wide SNP genotype data from 14 study samples and harmonize their FTND phenotype data. Using COGEND and deCODE Genetics (total N=10,319 Caucasians), we will conduct genome-wide joint 2df meta-analyses of ND, in parallel, accounting for an interaction with either CHRNA5- CHRNA3-CHRNB4 (in Aim 2) or CHRNB3-CHRNA6 (in Aim 3). The top-ranking SNPs from the joint 2df meta-analyses will be prioritized using bioinformatics analyses to evaluate their annotation, functionality, and regulatory potential. In Aim 4a, we will select up to 100 top-ranking SNPs for replication testing in seven independent Caucasian samples from dbGaP (total N=8,913). Replicated SNPs from Aim 4a will be further tested for association with ND in Aim 4b using two of our African American samples and three dbGaP samples (total N=4,145). Our prior GWAS of smoking and others have identified robust, functionally important variants that also contribute to cessation treatment response and smoking-related diseases (e.g., lung cancer). This study leverages these early findings and a new statistical method to discover novel variants contributing to ND, which may similarly add to our understanding of smoking cessation and smoking's health consequences.