A genome-wide association study to detect genetic variation for schizophrenia

描述（由申请人提供）：鉴定增加精神分裂症易感性的特定遗传变异对于进一步了解其病理生理和开发有效的治疗方法非常重要。为此，有必要在整个基因组中筛选与精神分裂症相关的许多标记。这种全基因组关联（GWA）研究目前在CATIE样本中完成，另一项精神分裂症的GWA研究将由GAIN在2007年年中完成。然而，这些目前的GWAs可能被更好地视为“筛选”研究，并且可能需要一系列精心设计的复制研究来消除假阳性并验证发现的标记物-疾病关联。在这个应用程序中，我们将尝试为精神分裂症的常见疾病/共同变异模型的严格评估做出贡献。我们将设计一系列具有成本效益的后续研究，而不是使用非最佳样本量和任意规则（如p值小于0.05）来表示复制，我们将使用我们的自适应多阶段研究的统计框架，一种系统的生物信息学方法来整合WGA数据与其他信息源，并与专家小组进行为期两天的会议。最初的复制工作涉及美国病例对照样本（5000个样本中有16K个snp，这些样本具有相同的病例和对照），其目标是以尽可能低的成本检测80%的增加精神分裂症易感性的常见遗传变异，同时将FDR控制在0.1的水平。为了验证病例对照样本中确定的关联不是群体分层/确定伪像的结果，并研究这些基因影响可能的群体差异，随后进行了“基于基因复制”的研究，对来自1,400个家庭的5,500个个体的300个snp进行基因分型。最后，我们将寻找基因型与环境变量之间的相互作用以及抗精神病药物的作用，而不仅仅是测试主效应，我们将使用我们的人工智能“模型发现”软件来搜索具有不同遗传和环境病因的精神分裂症亚型，并搜索拷贝数多态性。所有的基因型和临床数据都将保存在公共领域。我们试图为精神分裂症的常见疾病/共同遗传变异模型的严格评估做出贡献，这是进一步了解其病理生理和开发有效治疗方法的关键。

DESCRIPTION (provided by applicant): The identification of the specific genetic variants that increase susceptibility to schizophrenia is important to further understand its pathophysiology and develop effective treatments. For this purpose it will be necessary to screen many markers across the whole genome for association with schizophrenia. Such a genome-wide association (GWA) study is currently completed in the CATIE sample and another GWA for schizophrenia will be completed by GAIN in the middle of 2007. However, these current GWAs are perhaps better viewed as "screening" studies and a series of well- designed replication studies may be required to eliminate false positives and validate discovered marker-disease associations. In this application, we will attempt to contribute to the rigorous evaluation of the common disease/common variant model for schizophrenia. Rather than using non- optimal sample sizes and arbitrary rules such as P-values smaller than 0.05 suggest a replication, we will design a series of cost-effective follow up studies using our statistical framework for adaptive multi-stage studies, a systematic bioinformatic approach to integrate WGA data with other sources of information, and a two-day meeting with a panel of experts. The initial replication effort involves US case-control samples (16K SNPs in 5,000 samples with equal cases and controls) where the goal is to detect, with the lowest possible costs, 80 percent of the common genetic variants that increase susceptibility to schizophrenia while controlling the FDR at the 0.1 level. To verify that the identified associations in the case-control samples are not the result of population stratification/ascertainment artifacts and to study possible population differences in the effects of these genes, this is followed by "gene-based replication" study genotyping 300 SNPs in 5,500 individuals from 1,400 families. Finally, instead of merely testing for main effects, we will search for interactions between genotypes and environmental variables plus effects of anti-psychotic medication, use our artificial intelligent "model discovery" software to search for schizophrenia subtypes with different genetic and environment etiology, and search for copy number polymorphisms. All genotype and clinical data will be deposited in the public domain. We attempt to contribute to the rigorous evaluation of the common disease/common genetic variant model for schizophrenia, which is key to further understand its pathophysiology and develop effective treatments.