The Kinship Risk Score: An Integrative Tool to Prioritize Alcohol and Drug-Addiction Related Genes for Enhanced Risk Prediction

项目摘要/摘要 这个项目的主要目标是识别和描述反映个体差异的基因集。 发展酒精或其他形式的毒瘾的倾向，并开发一种新的工具来增强 使用全基因组数据预测药物成瘾风险。酒精、烟草和非法药物使用，以及 依赖是复杂的生物和心理社会问题，可以概念化为替代形式。 普遍药物依赖(DD)发展的潜在行为倾向。尽管 双胞胎和家庭研究表明，基因变异导致了人们对酒精的痴迷 和其他多种物质的滥用，对特定原因位点的识别一直受到限制。事实上, 研究证实，与单基因疾病不同，酒精和其他滥用药物受到 大量的基因变异。虽然人类和动物对成瘾的研究都表明， 药物成瘾的形式是受遗传影响的，完全是生物机制和遗传机制的补充 相关因素仍不得而知。当前的应用程序提出了一种新的框架和工具，它集成了 贝叶斯统计和功能基因组学加强对一组因果遗传因素(S)的识别 戒酒和其他药物依赖。在框架的第一个组件中，我们将使用贝叶斯混合 建立模型以识别一组(S)构成全身药的累加遗传效应的标记 依赖性(DD)。在第二个部分中，我们将识别跨物种的功能注释的基因集 对于酒精、烟草和其他非法药物使用/依赖，确定它们对 DD，并对用交叉法确定的(A)基因集中的贝叶斯衍生基因集(S)进行了富集性检验。 酒精和其他药物成瘾的物种功能基因组研究，以及(B)一系列生物基因组 基于已知的分子途径。在第三部分中，我们将确定和中的组合效应 在基因组之间。在最后一部分，我们开发了预测模型，并测试了一种新的亲属关系-- 在给定特定基因集的情况下，基于预测DD风险的方法。其中每个组件都提供了 有助于制定新的成瘾性研究假设和预测模型的新信息 行为和其他行为/特征。

PROJECT SUMMARY/ABSTRACT The primary goals of this project are to identify and characterize gene sets that reflect individual differences in the propensity to develop alcohol or other forms of drug addiction and to develop a novel tool to enhance the prediction of drug addiction risk using genome-wide data. Alcohol, tobacco, and illicit drug use and dependence are complex biological and psychosocial problems that can be conceptualized as alternate forms of an underlying behavioral predisposition to the development of generalized drug dependence (DD). Despite the fact that twin and family studies suggest that genetic variation contributes to the preoccupation with alcohol and other multiple substances of abuse, the identification of specific causal loci has been limited. In fact, studies have confirmed that unlike monogenic disorders, alcohol and other drugs of abuse are influenced by numerous genetic variants. While both human and animal studies of addiction have indicated that multiple forms of drug addiction are genetically influenced, the full complement of biological mechanisms and genetic factors are still unknown. The current application proposes a novel framework and tool that integrates Bayesian statistics and functional genomics to enhance the identification of a set(s) of causal genetic factors for alcohol and other drug dependence. In the first component of the framework, we will use Bayesian mixture modeling to identify a set(s) of markers that comprise the additive genetic effect on generalized drug dependence (DD). In the second component, we will identify cross-species-functionally-annotated gene sets for alcohol, tobacco, and other illicit drug use/dependence, determine their relative contribution to variation in DD, and test for enrichment of the Bayesian-derived gene set(s) in (a) gene sets ascertained using cross- species-functional genomic studies of alcohol and other drug addiction, and (b) a range of biological gene sets based on known molecular pathways. In the third component, we will identify combinatorial effects within and between gene sets. In the final component, we develop prediction models, as well as test a novel kinship- based approach to predict the risk for DD given a specified gene set. Each of these components provides novel information that will help to formulate new research hypotheses and prediction models for addictive behaviors and other behaviors/traits.