Mapping epistatic modifiers of human psychiatric risk using mouse genetics

使用小鼠遗传学绘制人类精神病风险的上位修饰因子

• 批准号: 8712849
• 负责人: LAURA J. SITTIG
• 金额: $ 5.58万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8712849
• 关键词: Accounting; Affect; Age; Alleles; Animal Model; Area; Attenuated; Bacteria; Behavior; Behavioral; Bioinformatics; Biological; Biological Assay; Bipolar Disorder; Brain; Candidate Disease Gene; Collaborations; Complex; Computer Simulation; Cystic Fibrosis; Data; Data Set; Databases; Diagnosis; Disease; Epilepsy; Exhibits; Female; Gene Expression; Gene Targeting; Generations; Genes; Genetic; Genetic Epistasis; Genetic Markers; Genome; Genomics; Genotype; Goals; Health; Heritability; Human; Human Genetics; Human Genome; Huntington Disease; Inbred Strain; Inbred Strains Mice; Inbreeding; Individual; Individual Differences; Inherited; Knockout Mice; L-Type Calcium Channels; Major Depressive Disorder; Maps; Mental disorders; Modeling; Mus; Muscle; Muscular Dystrophies; Mutant Strains Mice; Mutation; Pathway interactions; Patients; Pattern; Phenotype; Plants; Population; Predisposition; Rare Diseases; Research; Research Design; Resistance; Resolution; Resources; Risk; Schizophrenia; Source; Statistical Models; Testing; Training; Transgenes; Translating; Validation; Variant; Work; Yeasts; behavior test; career development; congenic; design; disorder risk; experience; gene interaction; genetic variant; genome wide association study; genome-wide; human data; improved; innovation; insight; male; mutant; neuropsychiatry; novel; offspring; psychogenetics; public health relevance; resilience; success; trait; treatment strategy

DESCRIPTION (provided by applicant): Bipolar disorder and schizophrenia are highly heritable disorders that are likely due to the actions of a large number of genes. Genome-wide association studies (GWAS) in humans have identified some key candidate genes, but together they explain only a fraction of the heritability. One of the strongest and best replicated candidat genes is CACNA1C, which has been shown to significantly contribute to disease risk for bipolar disorder, schizophrenia, and major depression. Genetic interactions are important in determining a number of traits in model organisms, and may also account for missing heritability in human psychiatric disease. Despite this promise, little progress has been made in understanding epistasis due to the statistical burden of testing for all pairwise interactions across the genome. The key long-term goal of this project is to use mouse genetics to identify epistatic modifier genes that influence risk for psychiatric disease in humans. We will use the Cacna1c+/- (heterozygous knockout) mouse, which exhibits key phenotypes involved in several psychiatric diseases. We will create a panel of isogenic F1 (first filial generation) offspring tha express the Cacna1c+/- allele on a variety of inbred backgrounds. As the phenotypic effects of transgenes are well known to differ across genetic backgrounds, we will take advantage of the phenotypic differences in Cacna1c+/- mice of different backgrounds to map the genetic variants that underlie them. All F1s will be isogenic for half their genome on which they inherit the Cacna1c +/- or +/+ allele on a B6 background; the other half will be of a variable inbred strain. I Specific Aim 1 we will generate this panel of F1 offspring harboring the Cacna1c mutant or wild-type allele on a variety of inbred backgrounds. We will phenotype the panel of F1 mice on a battery of behavioral tests that model specific aspects of neuropsychiatric disease relevant to bipolar disorder and schizophrenia and are already known to be altered in Cacna1c+/- mice. In Specific Aim 2 we will use this phenotypic data to conduct a GWAS using a statistical model that we developed for this study. Aside from genotype at the Cacna1c allele, genotype data for each F1 will be available from public databases. Our GWAS analysis will reveal markers that are associated with the phenotypic traits themselves, similar to results of a conventional GWAS study, as well as markers that modify the susceptibility or resilience to the effect of Cacna1c observed in some but not other F1s. We will then prioritize and narrow down candidate loci using bioinformatics resources; validate candidates using gene expression assays from brains of F1 mice; and conduct final validation by testing in human genetic datasets to which we will have access by collaboration. This is the first large-scale, efficient approach to mapping modifier loci. Findings of this work will be translated directly to human datasets where they may prove to explain individual risk to psychiatric disorder and generate new biological insights and treatment options for psychiatric disease.

描述（由申请人提供）：双相情感障碍和精神分裂症是高度遗传性疾病，可能是由于大量基因的作用所致。人类全基因组关联研究（GWAS）已经确定了一些关键的候选基因，但它们只能解释遗传力的一小部分。 CACNA1C 是最强、复制最好的候选基因之一，它已被证明会显着增加双相情感障碍、精神分裂症和重度抑郁症的疾病风险。遗传相互作用对于确定模式生物的许多特征很重要，也可能解释人类精神疾病遗传性的缺失。尽管有这样的希望，但由于测试整个基因组中所有成对相互作用的统计负担，在理解上位性方面进展甚微。该项目的关键长期目标是利用小鼠遗传学来识别影响人类精神疾病风险的上位修饰基因。我们将使用 Cacna1c+/-（杂合基因敲除）小鼠，该小鼠表现出与多种精神疾病有关的关键表型。我们将创建一组在各种近交背景下表达 Cacna1c+/- 等位基因的同基因 F1（第一代）后代。众所周知，转基因的表型效应因遗传背景而异，我们将利用不同背景的 Cacna1c+/- 小鼠的表型差异来绘制它们背后的遗传变异图谱。所有 F1 的一半基因组都是同基因的，它们在 B6 背景上继承了 Cacna1c +/- 或 +/+ 等位基因；另一半将是可变的近交系。 I 具体目标 1 我们将生成这组 F1 后代，它们在各种近交背景下含有 Cacna1c 突变体或野生型等位基因。我们将通过一系列行为测试对 F1 小鼠进行表型分析，这些行为测试模拟与双相情感障碍和精神分裂症相关的神经精神疾病的特定方面，并且已知在 Cacna1c+/- 小鼠中发生了改变。在具体目标 2 中，我们将使用这些表型数据，使用我们为本研究开发的统计模型进行 GWAS。除了 Cacna1c 等位基因的基因型外，每个 F1 的基因型数据也可从公共数据库获得。我们的 GWAS 分析将揭示与表型性状本身相关的标记，类似于传统 GWAS 研究的结果，以及改变在一些 F1 中观察到的 Cacna1c 效应的易感性或恢复力的标记。然后，我们将利用生物信息学资源确定候选基因座的优先顺序并缩小范围；使用 F1 小鼠大脑的基因表达测定来验证候选者；并通过在人类基因数据集中进行测试来进行最终验证，我们将通过合作访问这些数据集。这是第一个大规模、高效的映射修改器方法 基因座。这项工作的结果将直接转化为人类数据集，可能可以解释个人患精神疾病的风险，并为精神疾病产生新的生物学见解和治疗方案。