Fine mapping a gene sub-network underlying alcohol dependence

描述（由申请人提供）：该提案旨在精细映射与酒精依赖（AD）密切相关的基因子网络。AD对美国和全世界的个人和社会来说代价极其高昂。家庭、双胞胎和收养研究已经确定了遗传因素对AD风险的贡献。我们最近通过全基因组关联研究（GWAS）和人类蛋白质-蛋白质相互作用网络的综合分析，使用成瘾研究：遗传学与环境（SAGE）和酒精中毒遗传学合作研究（COGA）的两个GWAS数据集，确定了一个由39个基因组成的子网络，这些基因共同促成了AD的易感性。我们在三个独立样本中复制了该基因子网络与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GWAS的欧洲血统澳大利亚样本（p = 0.006），以及耶鲁大学的两个欧洲裔美国人（EA）（p = 0.0001）和非洲裔美国人（AA）（p = 0.007）。功能富集分析显示，该子网络富集了参与阳离子转运、突触传递和神经冲动传递的基因。我们现在的目标是细化候选因果基因，并确定基因子网络中的候选因果变体。为了实现这一目标，我们建议使用有针对性的下一代测序，先进的统计遗传学和生物信息学方法跟踪子网络中16个最有希望的候选基因。我们的具体目标是：1）基于全基因的候选基因靶向测序。在这里，我们试图确定所有的序列变体，包括编码和非编码，从子网络中选择的最有前途的候选基因。我们将使用SureSelect靶标富集系统和Illumina HiSeq 2000对取自COGA EA部分的500例病例和500例对照的全基因进行测序。将使用最先进的生物信息学管道分析和注释序列数据; 2）鉴定罕见的致病变体。我们将使用逻辑回归来检验每个低频变异（0.005 <次要等位基因频率（MAF）< 0.05）与AD的关联。还将使用先进的统计遗传学方法（如SKAT-O）在基因水平上检测罕见变异的相关性。我们将使用定制的GoldenGate基因分型阵列，使用统计学证据和功能信息，在独立的EA（1，911 AD和1，578对照）和AA（2，232 AD和1，657对照）中对测序发现的384种最有希望的变异进行优先级排序。我们聚集了一个在统计遗传学，生物信息学，人类遗传学和表型方面具有专业知识的优秀团队，旨在应用尖端的基因组技术和先进的分析策略来推进AD的遗传研究。本申请中提出的工作将为AD的遗传学研究做出重大贡献。潜在罕见致病变异的鉴定和表征将有助于提高我们对AD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方法。

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.