3/5-The Psychiatric GWAS Consortium: Integrated & Coordinated GWAS Meta-Analyses

描述（由申请人提供）：本申请由五个协作 R01 组成，这些 R01 是响应 NIMH RFA-MH-08-121“精神疾病全基因组关联研究的数据沉积和分析的有限竞争（协作 R01）”而提交的。这些应用程序来自精神病学 GWAS 联盟 (PGC)。到 2008 年底，将获得 47 个患有注意力缺陷多动障碍 (ADHD)、自闭症 (AUT)、双相情感障碍 (BIP)、重度抑郁症 (MDD) 或精神分裂症 (SCZ) 个体样本的 GWAS 数据。总的来说，这些 GWAS 构成了精神病学领域有史以来最大规模的生物学实验——超过 80,000 名受试者（59,000 名独立病例/对照和超过 7700 名家庭三人组），每个受试者约 500,000 个 SNP 基因型，约 400 亿个总基因型。尽管 GWAS 数据的可用性非常有吸引力，但确实存在主张冲突和混乱的风险。任何疾病的 GWAS 荟萃分析都是复杂的，需要大量的细心和专业知识才能有效地进行。鉴于迫切需要知道是否存在可复制的基因型-表型关联，因此需要一种新型的合作。为了实现这些目标，我们于 2007 年初启动了 PGC，以进行严格且全面的疾病内和跨疾病 GWAS 荟萃分析。 PGC 的总体理念是尽可能包容、民主和快速。 PGC 进展顺利，设有一个协调委员会、五个疾病工作组、一个跨疾病组、统计分析组以及用于数据仓库和统计分析的集群计算机。来自 11 个国家和 48 个机构的 101 名科学家。值得注意的是，除了一项符合条件的研究外，所有研究都参与其中，而且加入 PGC 的研究没有一个人离开。 PGC 的具体目标是： (1) 疾病内荟萃分析：对 ADHD、AUT、BIP、MDD 和 SCZ 的所有可用 GWAS 数据进行单独荟萃分析，以尝试识别令人信服的基因型-表型关联。 (2)跨疾病分析：人们普遍怀疑临床衍生的DSM-IV和ICD-10定义在基本遗传结构方面可能不具有“关节处的雕刻性质”。有两个子目标：(2a) 进行荟萃分析，试图识别 ADHD、AUT、BIP、MDD 和 SCZ =2 所共有的令人信服的基因型-表型关联。 (2b) 召集一个专家工作组，将流行病学和遗传流行病学证据转化为关于这些疾病之间重叠的严格而明确的假设。该分析计划遵循当前 GWAS 质量控制和荟萃分析的最佳实践，特别是在关注异质性来源的调查方面。统计能力应该优于任何先前的精神遗传学研究。结果将尽快公布。最后，PGC建议尽可能全面遵守NIH的GWAS数据共享政策，我们提供了详细的数据共享计划和时间表。

DESCRIPTION (provided by applicant): This application consists of five collaborative R01s submitted in response to NIMH RFA-MH-08-121, "Limited Competition for Data Deposition and Analyses of Genome Wide Association Studies of Mental Disorders (Collaborative R01)". These applications are from the Psychiatric GWAS Consortium (PGC). By the end of 2008, there will be GWAS data on 47 samples of individuals with either attention-deficit hyperactivity disorder (ADHD), autism (AUT), bipolar disorder (BIP), major depressive disorder (MDD), or schizophrenia (SCZ). Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry - over 80,000 subjects (59,000 independent cases/controls and over 7700 family trios), ~500,000 SNP genotypes per subject, and ~40 billion total genotypes. Although the availability of GWAS data is highly attractive, there is a real risk of conflicting claims and confusion. GWAS meta-analysis for any disease is complex and requires considerable care and expertise in order to be done validly. Given the urgent need to know if there are replicable genotype-phenotype associations, a new type of collaboration is required. To accomplish these ends, we initiated the PGC in early 2007 to conduct rigorous and comprehensive within- and cross- disorder GWAS meta-analyses. The overall philosophy of the PGC is to be as inclusive, democratic, and rapid as possible. The PGC is well-underway with a coordinating committee, five disease working groups, a cross-disorder group, statistical analysis group, and a cluster computer for data warehousing and statistical analysis. 101 scientists from 11 countries and 48 institutions. It is remarkable that all but one eligible study is participating and that no one who has joined the PGC has left. The Specific Aims of the PGC are: (1) Within-disorder meta-analyses: conduct separate meta-analyses of all available GWAS data for ADHD, AUT, BIP, MDD, and SCZ to attempt to identify convincing genotype-phenotype associations. (2) Cross-disorder analyses: it is widely suspected that the clinically-derived DSM-IV and ICD-10 definitions may not have "carved nature at the joint" with respect to the fundamental genetic architecture. There are two sub-aims: (2a) Conduct meta-analysis to attempt to identify convincing genotype-phenotype associations that are common to =2 of ADHD, AUT, BIP, MDD, and SCZ. (2b) Convene an expert working group to convert epidemiological and genetic epidemiological evidence into rigorous and explicit hypotheses about overlap amongst these disorders. The analytic plan abides to current best practices for GWAS quality control and meta-analysis, particularly in its attention to investigating sources of heterogeneity. Statistical power should be superior to any prior study in psychiatric genetics. Results will be made available as soon as possible. Finally, the PGC proposes to abide as fully as possible with the NIH's GWAS data sharing policies and we provide a detailed data sharing plan and timetable.