Finding causal variants within schizophrenia risk loci

 描述（由申请人提供）：精神分裂症是高度遗传性的，是导致大量发病率、死亡率以及个人和社会成本的原因。为了增加我们对精神分裂症生物学基础的理解，必须确定精确的因果突变， 影响风险。GWAS在识别与精神分裂症相关的常见SNP的基因组区域方面非常成功。最近的PGC分析产生了128个全基因组显著的基因座，并且这个数字可能会增加。然而，几乎所有GWAS基因座的致病变异已被证明是难以捉摸的。现有的插补资源没有针对GWAS基因座进行优化。该建议的首要目标是确定精神分裂症GWAS基因座内的致病突变。由拷贝数变化引起的常见风险位点（即，拷贝数多态性或CNP）是有吸引力的偶然突变。位于GWAS基因座内的CNP（即，通过相关的SNP“标记”）可以改变调节元件和基因的剂量或结构，并发挥功能影响以驱动观察到的与SCZ的关联。文献中有多个例子。CNP对精神分裂症的贡献尚不清楚，因为CNP的大规模调查很少。大多数当前方法都无法获得CNP，并且CNP插补方法还不发达。由于CNP是高度可信的，但在很大程度上未探索，我们建议在PGC GWAS基因座处对精神分裂症样本进行深度测序，以识别CNP和不常见的SNP/indel，然后将其输入到非常大的样本中，以测试与精神分裂症的关联性。识别不常见的SNP/插入缺失使所提出的研究的支出最大化。成功完成拟议的工作将提高我们对精神分裂症病因学的生物学机制的理解。如果我们能够识别出即使是一些CNPs或不常见的SNP/indels改变精神分裂症的风险，这将代表该领域的重要进展。任何CNP相关性都可能与当前分子生物学和神经科学方法具有直接的生物学相关性和顺应性。此外，这些精神分裂症相关的GWAS基因座从未在世界文献中进行过深度测序。从该R 01生成的靶向重测序数据、插补参考和CNP插补方法将成为人类遗传学界的有用资源。这些数据还将确保社区即将对这些风险位点进行的功能性工作集中在最有希望的变体上。

 DESCRIPTION (provided by applicant): Schizophrenia is highly heritable and the cause of substantial morbidity, mortality, and personal and societal costs. To increase our understanding of the biological basis of schizophrenia, it is essential to identify precise causal mutations that influence risk. GWAS have been remarkably successful in identifying genomic regions harboring common SNPs associated with schizophrenia. The most recent PGC analysis yielded 128 genome- wide significant loci and this number will likely increase. However, the causative variants underlying nearly all GWAS loci have proven elusive. Existing imputation resources are not optimized for GWAS loci. The overarching goal of this proposal is to identify causative mutations within schizophrenia GWAS loci. Common risk loci resulting from changes in copy number (i.e., copy number polymorphism, or CNP) are attractive casual mutations. CNPs that reside within GWAS loci (i.e., "tagged" by the associated SNPs) can alter the dosage or structure of regulatory elements and genes and exert functional impact to drive the observed association with SCZ. Multiple examples are in the literature. The contribution of CNPs to schizophrenia is unknown because large-scale surveys of CNPs are infrequent. CNPs are inaccessible to most current methods, and CNP imputation methods are underdeveloped. Since CNPs are highly plausible but largely unexplored, we propose to deeply sequence schizophrenia samples at the PGC GWAS loci to identify CNPs and uncommon SNP/indels, and then impute them into very large samples to test for association with very high power with schizophrenia. Identifying uncommon SNP/indels maximizes expenditure of the proposed study. Successful completion of the proposed work will enhance our understanding of biological mechanisms underlying the etiology of schizophrenia. If we can identify even a few CNPs or uncommon SNP/indels altering schizophrenia risk, it will represent an important advance in the field. Any CNP association is likely to have immediate biological relevance and amenability to current molecular biology and neuroscience methods. Furthermore, these schizophrenia-associated GWAS loci have never been deeply sequenced in the world-literature. The targeted resequencing data, the imputation reference, and CNP imputation methods generated from this R01 will be useful resources for the human genetics community. These data will also ensure that the community's forthcoming functional work on these risk loci is focused on the most promising variants.