Elucidating Genes Regulating Sleep Using Diversity Outbred Mice

摘要 睡眠和昼夜节律的大多数方面是可遗传的，即部分由序列变化决定 在DNA中。目前，人们正在应用多种方法来识别影响睡眠的相关基因。一次切割- EDGE策略采用的是高多样性的小鼠模型-特别是多样性近交系小鼠。这些老鼠 是通过合作杂交小鼠播种的一种新的随机远缘繁殖方法衍生出来的。这 创造了一个遗传异质性的小鼠种群，其遗传多样性接近人类 人口。每一种多样性杂交小鼠的基因组都是从最初的8只小鼠遗传而来的独特的马赛克 方正品系的小鼠。准确测量整个基因组的遗传变异可以是 使用专门的基因分型阵列获得的。然后，可以将这些数据与大型 用于识别仅包含几个候选因果关系的小数量性状基因座的多样性杂交小鼠的数量 基因。为了利用这一资源来识别与睡眠相关的基因，我们开发了一种高通量 表型流水线，评估睡眠和昼夜节律的多个可遗传方面，包括数量 睡眠、睡眠巩固程度、警觉性、睡眠驱动力和昼夜节律。在发现重要的 使用这种高通量方法，识别的基因的稳健验证是以黄金标准进行的 脑电/肌电记录相关品系协作杂交小鼠的睡眠，以及随后在患有 预测的因果基因的敲除。使用这种精确的方法，我们已经确定了一种新的基因 调节小鼠的睡眠。自从这一发现以来，我们进一步增加了我们的表型和 不同的近交系小鼠的基因样本，现在已经确定了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。虽然现有的分析表明在 在小鼠中发现了影响睡眠的重要基因，我们工作的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。相反，很可能会有复杂的 基因效应涉及多个相互作用的基因，这些基因决定了睡眠/醒来行为的额外变异性。 然而，目前对多样性近交系小鼠的分析方法侧重于加性遗传关联，并确实 不允许我们充分处理这一概念。因此，为了发现这些更复杂的遗传效应，我们 建议使用新的机器学习方法来丰富可用的遗传和表型数据 我们有成百上千种不同的杂交鼠。总而言之，这是一项高风险、高影响的适当建议 对于R21，给出了一种基于机器学习的分析策略，重点验证了候选对象 影响睡眠的因果基因。如果成功，这项提议将为基因基础提供新的见解 睡眠和一种新的分析资源，使正在进行的或已完成的多样性近交系小鼠研究受益。

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.