Fine mapping a gene sub-network underlying alcohol dependence

精细绘制酒精依赖背后的基因子网络

• 批准号: 8887090
• 负责人: SHIZHONG HAN
• 金额: $ 39.47万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-05 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8887090
• 关键词: Adoption; African American; Alcohol consumption; Alcohol dependence; American; Bioinformatics; Biological; Candidate Disease Gene; Cations; Chromosome Mapping; Code; Data; Data Set; Disease; European; Family; Frequencies; Gene Frequency; Genes; Genetic; Genetic study; Genomics; Genotype; Goals; Health; Human; Human Genetics; Individual; Lead; Logistic Regressions; Maps; Methods; Minor; Noise; Pathway interactions; Phenotype; Predisposition; Prevention; Process; Risk; Role; Sampling; Sequence Analysis; Signal Transduction; Societies; Staging; Synaptic Transmission; System; Technology; Testing; Twin Multiple Birth; United States; Untranslated RNA; Variant; Work; addiction; alcohol use disorder; base; case control; collaborative environment; design; follow-up; genetics of alcoholism; genome wide association study; improved; innovation; neurotransmission; next generation sequencing; novel; protein protein interaction; rare variant; research study; targeted sequencing; tool

DESCRIPTION (provided by applicant): This proposal aims to fine map a gene sub-network that is robustly associated with alcohol dependence (AD). AD is extremely costly to individuals and to society in the United States and throughout the world. Family, twin, and adoption studies have established a genetic contribution to the risk for AD. We recently identified a sub-network of 39 genes that collectively contribute to the susceptibility for AD through the integrated analysis of genome-wide association studies (GWAS) and human protein-protein interaction networks, using two GWAS datasets from the Study of Addiction: Genetics and Environment (SAGE) and the Collaborative Study on the Genetics of Alcoholism (COGA). We replicated the association of this gene sub-network with AD in three independent samples, including the European-ancestry Australian sample from the GWAS of alcohol use and alcohol use disorder in Australian Twin-Families (p = 0.006), and two samples of European-Americans (EA) (p = 0.0001) and African-Americans (AA) (p = 0.007) at Yale. Functional enrichment analysis revealed that the sub-network is enriched for genes involved in cation transport, synaptic transmission, and transmission of nerve impulse. We now aim to refine candidate causal genes and determine candidate causal variants within the gene sub-network. To accomplish this, we propose to follow up the 16 most promising candidate genes in the sub-network using targeted next-generation sequencing, advanced statistical genetics and bioinformatics approaches. Our specific aims are: 1) whole gene-based targeted sequencing of candidate genes. Here we seek to identify all sequence variants, including coding and noncoding, for the most promising candidate genes selected from the sub-network. We will sequence the whole genes in 500 cases and 500 controls taken from the EA portion of COGA using the SureSelect Target Enrichment system and the Illumina HiSeq 2000. The sequence data will be analyzed and annotated using a state-of-the-art bioinformatics pipeline; 2) Identification of rare causal variants. We will use logistic regression to test the association of each low-frequency variant (0.005 < minor allele frequency (MAF) < 0.05) with AD. Rare variants will also be tested for association at the gene level using advanced statistical genetic methods such as SKAT-O. We will prioritize the 384 most promising variants discovered from sequencing using both statistical evidence and functional information for replication in independent EAs (1,911 AD and 1,578 controls) and AAs (2,232 AD and 1,657 controls) using a customized GoldenGate genotyping array. We have gathered an outstanding team with expertise in statistical genetics, bioinformatics, human genetics, and phenotyping, with the goal of applying cutting-edge genomic technologies and advanced analytical strategies to advance the genetic study for AD. The work proposed in this application will contribute significantly to genetic studies of AD. The identification and characterization of potential rare causal variants would help improve our understanding of the biological mechanisms that underlie AD, moving us closer to designing effective prevention and treatment for the disorder.

描述(由申请人提供)：这项提案旨在精细定位与酒精依赖(AD)密切相关的基因子网络。在美国和全世界，广告对个人和社会来说都是极其昂贵的。家庭、双胞胎和收养研究已经确定了遗传因素对AD风险的影响。最近，我们利用来自成瘾研究：遗传学与环境研究(SAGE)和酒精中毒遗传学合作研究(COGA)的两个GWAS数据集，通过对全基因组关联研究(GWAS)和人类蛋白质-蛋白质相互作用网络的综合分析，确定了一个由39个基因组成的子网络，这些基因共同参与了AD的易感性。我们在三个独立的样本中复制了这个基因子网络与AD的关联，包括来自澳大利亚双胞胎家庭酒精使用和酒精使用障碍的欧洲血统的澳大利亚样本(p=0.006)，以及耶鲁大学的两个欧洲裔美国人(EA)(p=0.0001)和非裔美国人(AA)(p=0.007)。功能丰富分析表明，该子网络富含参与阳离子转运、突触传递和神经冲动传递的基因。我们现在的目标是提炼候选因果基因，并确定基因子网络中的候选因果变体。为了实现这一点，我们建议使用有针对性的下一代测序、高级统计遗传学和生物信息学方法来跟踪子网络中最有希望的16个候选基因。我们的具体目标是：1)基于全基因的候选基因的靶向测序。在这里，我们试图识别从子网络中选择的最有希望的候选基因的所有序列变体，包括编码和非编码。我们将使用SureSelect靶浓缩系统和Illumina HiSeq 2000对500例病例和500名对照的COGA EA部分的全部基因进行测序。序列数据将使用最先进的生物信息学管道进行分析和注释；2)识别罕见的因果变异。我们将使用Logistic回归来检验每个低频变异(0.005)与AD的关联。还将使用SKAT-O等先进的统计遗传学方法在基因水平上测试稀有变异的关联。我们将使用统计证据和功能信息对从测序中发现的384个最有希望的变异进行优先排序，以便使用定制的GoldenGate基因分型阵列在独立的EA(1,911个AD和1,578个对照)和AA(2,232个AD和1,657个对照)中进行复制。我们聚集了一支在统计遗传学、生物信息学、人类遗传学和表型鉴定方面具有专业知识的优秀团队，目标是应用尖端基因组技术和先进的分析策略来推进AD的遗传学研究。本申请中提出的工作将对阿尔茨海默病的遗传学研究做出重大贡献。识别和表征潜在的罕见原因变异将有助于提高我们对阿尔茨海默病背后的生物学机制的理解，使我们更接近于设计有效的预防和治疗AD的疾病。