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

基本信息

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
项目类别：
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

项目摘要

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可遗传的，即部分由序列变化决定目前正在采用多种方法来识别影响睡眠的相关基因。所采用的优势策略是高多样性小鼠模型——特别是多样性远交小鼠。是通过协作杂交小鼠播种的新型随机近交方法衍生的。创建了一个遗传异质性的小鼠群体，其遗传多样性接近人类每只 Diversity Outbred 小鼠的基因组都是继承自原始 8 只小鼠的独特嵌合体。可以准确测量整个基因组的遗传变异。然后可以将这些数据与大型的仔细表型分析相结合。数量的多样性远交小鼠来识别仅包含少数候选因果关系的小数量性状基因座为了利用这种资源来识别与睡眠相关的基因，我们开发了一种高通量的方法。表型分析管道，评估睡眠和昼夜节律的多个遗传方面，包括数量睡眠时间、睡眠巩固程度、警惕性、睡眠巩固驱动力和昼夜节律在发现重要后。使用这种高通量方法定位基因座，并按照金标准对已识别基因进行稳健验证脑电图/肌电图记录了协作交叉小鼠相关品系以及随后具有使用这种精确的方法敲除预测的因果基因，我们已经鉴定了一个新基因。自从这一发现以来，我们进一步增加了表型和睡眠的大小。对多样性远交小鼠样本进行基因分型，现已鉴定出其他几个有趣的数量性状包含需要验证的候选因果基因的基因座，而现有的分析已显示出希望。确定了小鼠睡眠的重要基因，我们的工作证据支持睡眠是一种复杂的遗传特征。也就是说，表型不是简单地由单个基因变异决定的，而是可能是复杂的。遗传效应涉及多个相互作用的基因，这些基因决定了睡眠/觉醒行为的额外变异性。然而，目前对多样性远交小鼠的分析方法侧重于加性遗传关联，并且确实不允许我们充分解决这个概念因此，为了揭示这些更复杂的遗传效应，我们。提议采用新颖的机器学习方法来获取大量可用的遗传和表型数据总的来说，这是一个高风险、高影响力的建议。对于 R21，考虑到基于机器学习的新颖分析策略以及对候选验证的关注如果成功，该提议将为遗传基础提供新的见解。睡眠和新颖的分析资源，有利于任何正在进行或已完成的多样性远交小鼠研究。