Deep sequencing studies for cannabis and stimulant dependence

描述（由申请人提供）：本提案的目标是使用基因型植入的全基因组测序鉴定影响兴奋剂和大麻依赖易感性的基因。越来越经济的全基因组测序的出现提供了新的机会，以确定性状相关的序列变异没有检测到连锁或关联方法。然而，鉴定性状相关位点最有效的分析方法是什么还不确定。因此，我们建议使用最先进的信息学基础设施研究四种不同的分析方法，以确定影响兴奋剂和大麻依赖的序列变异。这三个研究队列被确定为印第安人任务研究（PI Cindy Ehlers），耶鲁大学-康涅狄格大学成瘾研究联合样本（PI Joel Gelernter）和旧金山家庭研究（PI Kirk Wilhelmsen）的一部分。因为我们正在研究具有三个不同大陆起源的种群，我们将确定我们感兴趣的性状的主要遗传风险因素是共有的还是特定于种群的。在构成我们样本的三个人群中，有两个（即印第安人和非裔美国人）没有得到充分的研究。纳入这些队列将允许在确定策略和种族组成不同的人群内和人群之间进行分析，允许对人群之间的复制进行强有力的测试，并能够直接比较确定和分析方法，因为它们适用于成瘾的遗传研究。我们期望用于这项研究的方法将不断发展。目前，我们计划使用四种互补分析策略：1)使用>5X序列覆盖率检测基因型的传统SNP分析，用于次要等位基因频率（MAF）大于0.1%的多态性；2)基于基因的方法，以确定是否有更多的病例比对照组具有可能影响给定基因功能的罕见序列变异（MAF<1%）；3)使用一种简化形式的已知和远亲个体的仅受影响连锁分析，以确定可能通过血统共享相同的长染色体片段；4)高层结构变异的扩展分析。DNA序列分析的技术和经济正在迅速发展。根据目前的成本，我们计划完成至少3000名受试者的50倍全基因组测序。我们决定追求低通基因组测序的关键是多点插补方法的发展。仿真分析表明，在相同的成本下，与外显子组测序相比，更多的受试者可以进行>5X的全基因组插入测序，从而提供了更多的外显子组数据，也为基因组的其余部分提供了丰富的数据。这项研究的发现可能为其他滥用物质的遗传学提供见解，这可以通过与响应该RFA的其他项目以及NIDA遗传学联盟的其他项目共享数据来证实。

DESCRIPTION (provided by applicant): The goal of this proposal is to identify genes that affect susceptibility to stimulant and cannabis dependence using whole genome sequencing with genotype imputation. The advent of increasingly economical whole genome sequencing provides new opportunities to identify trait-associated sequence variations not detected by linkage or association methods. It is not certain, however, what the most effective analytic method for identifying trait-related loci will be. Thus, we propose to study four distinct analytic approaches to identify sequence variants that affect stimulant and cannabis dependence in three cohorts using a state-of-the-art informatics infrastructure. The three study cohorts were ascertained as part of the Mission Indian Study (PI Cindy Ehlers), the combined Yale-University of Connecticut Addiction Study samples (PI Joel Gelernter) and the San Francisco Family Study (PI Kirk Wilhelmsen). Because we are studying populations with three different continental origins, we will ascertain whether the major genetic risk factors for the traits of interest are shared or population-specific. Two of the three populations that make up our sample (i.e., Native Americans and African Americans) are understudied. Inclusion of these cohorts will allow for analyses within and across populations that differ in ascertainment strategies and ethnic composition, permitting strong tests of replication across populations and enabling direct comparisons of ascertainment and analytic approaches as they apply to genetic studies of addiction. We expect that the approach used for this study will evolve. Currently we plan to use four complementary analytic strategies: 1) a conventional SNP analysis of genotypes detected using >5X sequence coverage for polymorphisms with minor allele frequency (MAF) greater than 0.1%; 2) a gene-based approach to determine whether more cases than controls have rare sequence variants (MAF<1%) likely to affect the function of a given gene; 3) use of a simplified form of affected-only linkage analysis of known and distantly related individuals to identify long chromosomal segments that are likely to be shared identical by descent; and 4) extended analysis of higher-level structural variation. The technology and economics of DNA sequence analysis is rapidly evolving. Based on current costs we plan to complete >5X whole genome sequencing of at least 3000 subjects. Critical to our decision to pursue low-pass genomic sequencing was the development of multipoint imputation methods. Simulation analysis indicates that for the same cost more subjects can undergo >5X whole genome sequencing with imputation than exomic sequencing, thus providing more exomic data as well as rich data for the rest of the genome. The findings from this study may provide insight into the genetics of other substances of abuse which can be confirmed by data sharing with other projects responding to this RFA and to other projects in the NIDA Genetics Consortium. PUBLIC HEALTH RELEVANCE: Cannabis and stimulants (methamphetamine and cocaine) are the most commonly used illicit drugs worldwide, and thus pose significant public health issues. Because cannabis and stimulant dependence have a substantial shared genetic etiology and exhibit significant genetic correlations with other drug dependencies, identifying sequence variants that affect cannabis and stimulant dependence should provide insights into the pathophysiology of dependence and inform prevention and treatment strategies.