2/2-Whole Genome and Exome Sequencing for Bipolar Disorder

描述(由申请人提供)：这项合作的R01提案(由密歇根大学的Michael Boehnke博士及其同事提交的协调提案)的目标是使用高通量DNA测序和基因分型技术来识别导致双相情感障碍(BD)风险的基因和途径。这项建议建立在我们密歇根大学和HudsonAlpha小组在BD和其他情绪障碍方面的积极合作基础上。我们的研究团队结合了高通量遗传学和基因组学以及创新计算和统计方法的开发和应用方面的优势，以最大限度地发挥这些新技术的优势。在具体目标1中，我们将对2,000名个体的DNA进行测序，其中1,025名患有BD，975名对照，基因组的AT&gt；4倍覆盖，44 Mb外显子的AT&gt；60倍平均覆盖。在具体目标2中，我们将根据这2,000个样本的序列数据和至少另外6,085个BD病例和7,116个对照GWAS样本的输入数据进行BD关联分析，以确定至少7,110个病例和8,091个对照中与BD相关的变异。我们将分别分析MAF&GT；0.5%的变种。对于MAF&lt；1%的变异，我们将使用“负担”测试，旨在识别病例中稀有变异簇比对照更常见的区域(反之亦然)。对于归因，我们将使用来自该项目、1000基因组计划和我们的GOT2D计划的序列数据作为参考集，将具有先前发表的GWA型数据的BD病例对照样本个体作为目标集。在具体目标3中，我们将使用定制的SNP阵列对~5,000个序列变异进行分型和深度测序，对7592名个体、3854名BD患者和3738名对照的500个选定基因进行重新测序，并对所得数据进行BD关联分析。这一套将包括在特定目标1中测序的2,000个人，以验证基于序列的基因类型，以及另外5,592个人，以验证基于归因的关联结果和/或加强更罕见变异关联的证据。与帕梅拉·斯克拉尔博士合作，我们将通过对32,000例BD患者和30,000名对照进行基因分型，对最有趣的SNPs进行追踪，并对结果数据进行荟萃分析。在具体目标4中，我们将分享数据和方法，以支持对BD和其他精神病学表型的类似研究，并更广泛地支持整个科学界。这些目标的完成将为疾病机制提供新的见解，有可能催化在BD预防、治疗和诊断方面的突破。 公共卫生相关性：双相情感障碍是一种慢性疾病，严重致残，常常危及生命。尽管估计终生患病率约为1%，并对个人、家庭和公共健康造成巨大影响，但人们对其复杂的病因知之甚少。提高对双相情感障碍遗传基础的了解，有可能通过提高我们对疾病病因的理解，支持识别新的药物和治疗方法，使预防和治疗方法更有针对性，并提供更准确的风险预测，从而减少对疾病的影响。

DESCRIPTION (provided by applicant): The goal of this collaborative R01 proposal (a coordinating proposal is being submitted by Dr. Michael Boehnke and colleagues at the University of Michigan) is to use high-throughput DNA sequencing and genotyping technologies to identify genes and pathways that contribute to the risk for bipolar disorder (BD). This proposal builds on the active collaboration between our groups at the University of Michigan and HudsonAlpha on BD and other mood disorders. Our research team combines strengths in high-throughput genetics and genomics and development and application of innovative computational and statistical methods to maximize the benefits of these new technologies. In Specific Aim 1, we will sequence DNA from 2,000 individuals, 1,025 with BD and 975 controls, at >4X coverage for the genome and at >60X average coverage for the 44 Mb exome. In Specific Aim 2, we will carry out BD association analyses based on sequence data from these 2,000 samples and imputed data from at least an additional 6,085 BD case and 7,116 control GWAS samples to identify BD-associated variants in at least 7,110 cases and 8,091 controls. We will analyze variants with MAF>0.5% individually. For variants with MAF<1%, we will use "burden" tests designed to identify regions where clusters of rare variants are more common in cases than controls (or vice versa). For imputation, we will use sequence data from this project, the 1000 Genomes Project, and our GOT2D project as reference sets, and BD case-control sample individuals with GWAS genotype data from previously-published GWAS as target sets. In Specific Aim 3, we will use custom SNP arrays to genotype ~5,000 sequence variants and deep sequencing to resequence 500 selected genes in 7,592 individuals, 3,854 with BD and 3,738 controls, and carry out BD association analysis on the resulting data. This set will include the 2,000 individuals sequenced in Specific Aim 1 to validate the sequence-based genotypes and 5,592 additional individuals to validate imputation-based association findings and/or strengthen evidence for rarer variant association. In collaboration with Dr. Pamela Sklar, we will follow-up the most interesting SNPs by genotyping 32,000 BD cases and 30,000 controls and carry out a meta-analysis of the resulting data. In Specific Aim 4, we will share data and methods to support similar studies for BD and other psychiatric phenotypes, and more broadly across the scientific community. Completion of these aims will provide new insights into disease mechanism that have the potential to catalyze breakthroughs in BD prevention, treatment, and diagnosis. PUBLIC HEALTH RELEVANCE: Bipolar disorder is chronic, severely disabling, and often life-threatening. Despite an estimated lifetime prevalence of ~1% and a huge impact on individuals, families, and public health, little is known about its complex etiology. Improved understanding of the genetic basis of bipolar disorder has the potential to reduce disease impact by improving our understanding of disease etiology, supporting identification of novel drugs and therapies, enabling better targeting of preventive and therapeutic approaches, and providing more accurate risk prediction.