Elucidating Genes Regulating Sleep Using Diversity Outbred Mice

利用多样性远交小鼠阐明调节睡眠的基因

• 批准号: 10623210
• 负责人: Allan I Pack
• 金额: $ 25.91万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10623210
• 关键词: Address; Affect; Algorithms; Behavior; Biological Models; Breeding; Caenorhabditis elegans; Candidate Disease Gene; Characteristics; Chromosome 3; Circadian Rhythms; Complex; Complex Genetic Trait; DNA; Darkness; Data; Dihydropyrimidine Dehydrogenase; Drosophila genus; Electroencephalography; Environment; Follow-Up Studies; Genes; Genetic; Genetic Epistasis; Genetic Variation; Genetic study; Genome; Genotype; Haplotypes; Heritability; Hippocampus; Human; Inherited; Knock-out; Knockout Mice; Machine Learning; Manuscripts; Measurement; Measures; Methods; Mouse Strains; Movement; Mus; Mutagens; Phenotype; Population; Population Heterogeneity; Preparation; Quantitative Trait Loci; Questionnaires; Randomized; Resources; Running; Sampling; Sleep; Sleep Deprivation; Sleep Fragmentations; Sleep Stages; Stains; Techniques; Testing; Validation; Variant; Work; Zebrafish; biobank; candidate validation; causal variant; circadian; design; gene interaction; genetic approach; genetic architecture; genetic association; genetic variant; genome wide association study; high risk; insight; interest; learning strategy; loss of function; mosaic; mouse model; novel; phenotypic data; rare variant; response; screening; trait; transcriptome sequencing; vigilance; whole genome

ABSTRACT Most aspects of sleep and circadian rhythm are heritable, i.e., are in part determined by sequence variations in DNA. Multiple approaches are currently being applied to identify relevant genes influence sleep. One cutting- edge strategy being employed is high-diversity mouse models – particularly Diversity Outbred mice. These mice are derived through a novel randomized outbreeding approach seeded by the Collaborative Cross mice. This creates a genetically heterogeneous population of mice with genetic diversity approaching that in human populations. The genome of each Diversity Outbred mouse is a unique mosaic inherited from the original 8 Founder strains of mice. Accurate measurement of the genetic variation across the whole genome can be obtained using a specialized genotyping array. This data can then be combined with careful phenotyping of large numbers of Diversity Outbred mice to identify small quantitative trait loci containing only a few candidate causal genes. To utilize this resource to identify genes relevant for sleep, we have developed a high-throughput phenotyping pipeline that assesses multiple heritable aspects of sleep and circadian rhythm, including amounts of sleep, degree of sleep consolidation, vigilance, sleep drive, and circadian period. After discovering important loci using this high-throughput approach, robust validation of identified genes is carried out with gold-standard EEG/EMG recording of sleep in relevant strains of Collaborative Cross mice and, subsequently, in mice with a knockout of the predicted causal gene. Using this exact approach, we have already identified a novel gene regulating sleep in mice. Since this discovery, we have further increased the size of our phenotyped and genotyped sample of Diversity Outbred mice, and have now identified several other interesting quantitative trait loci containing candidate causal genes requiring validation. While existing analyses have shown promise in identifying important genes for sleep in mice, evidence from our work supports sleep as a complex genetic trait. That is, the phenotype is not simply determined by a single gene variant. Rather, there are likely to be complex genetic effects involving multiple interacting genes that determine additional variability in sleep/wake behavior. However, current analysis approaches in Diversity Outbred mice focus on additive genetic associations, and do not allow us to adequately address this concept. Thus, to uncover these more complex genetic effects, we propose to employ novel machine learning approaches to the wealth of available genetic and phenotypic data we have for hundreds of Diversity Outbred mice. Altogether, this is a high-risk, high-impact proposal appropriate for an R21 given the novel analytic strategy based on machine learning and the focus on validation of candidate causal genes affecting sleep. If successful, this proposal will provide new insights into the genetic underpinnings of sleep and a novel analysis resource to benefit any ongoing or completed studies in Diversity Outbred mice.

抽象的 睡眠和昼夜节律的大多数方面都是遗传的，即部分由序列变化确定 在DNA中。目前正在采用多种方法来识别相关基因影响睡眠。一个剪裁 - Edge策略成为员工是高多样性小鼠模型，尤其是多样性杂种小鼠。这些老鼠 通过一种由协作杂交小鼠种植的新型随机爆发方法得出的。这 在人类中创建具有遗传多样性的遗传多样性的遗传异质种群 人群。每种多样性小鼠的基因组是从原始8继承的独特镶嵌物 小鼠的创始人菌株。可以准确测量整个基因组的遗传变异 使用专门的基因分阵列获得。然后可以将这些数据与仔细的大型表型结合在一起 多样性杂种小鼠的数量，以识别仅包含少量候选催化的小定量性状基因座 基因。要利用此资源来识别与睡眠相关的基因，我们已经开发了高通量 评估睡眠和昼夜节律的多个可遗传方面的表型管道，包括 睡眠，睡眠巩固程度，警惕，睡眠驱动和昼夜节律时期。发现重要之后 使用这种高通量方法的基因座，使用金标准进行了良好的验证识别基因 脑电图记录与协作杂交小鼠相关菌株中的睡​​眠记录，随后，在带有A 预测因果基因的敲除。使用这种精确的方法，我们已经确定了一个新的基因 调节小鼠的睡眠。自从这一发现以来，我们进一步增加了表型的大小和 基因分型的多样性繁殖小鼠样本，现在已经确定了其他一些有趣的定量性状 含有候选因果基因的基因座需要验证。虽然现有的分析已显示出希望 确定小鼠睡眠的重要基因，我们工作的证据支持睡眠作为复杂的遗传特征。 也就是说，表型不简单地由单个基因变体确定。相反，可能会有复杂 涉及多个相互作用基因的遗传效应，这些基因决定了睡眠/唤醒行为的额外变化。 然而，当前的分析方法在多样性小鼠中的方法专注于加性遗传关联，并做 不允许我们充分解决这个概念。为了发现这些更复杂的遗传效应，我们 对员工新颖的机器学习方法的建议，以实现可用的遗传和表型数据 我们有数百种多样性小鼠。总之，这是一个高风险，高影响的建议 对于R21，鉴于基于机器学习的新分析策略，并关注候选人的验证 影响睡眠的因果基因。如果成功，该建议将为遗传基础提供新的见解 睡眠和新颖的分析资源，以使多样性杂种小鼠进行的任何正在进行的或完成的研究受益。