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，在某种程度上是由序列变异决定的 在DNA中。目前有多种方法被用于确定影响睡眠的相关基因。一次切割- 采用的边缘策略是高度多样性的小鼠模型-特别是多样性远交小鼠。这些小鼠 是通过一种新的随机远系繁殖的方法，由协作杂交小鼠播种。这 创造了一个遗传异质的小鼠群体，其遗传多样性接近人类 人口。每只远交小鼠的基因组都是一个独特的嵌合体， 创始人品系的老鼠。精确测量整个基因组中的遗传变异， 使用专门的基因分型阵列获得。然后，可以将这些数据与大样本的仔细表型分析相结合。 许多多样性远交小鼠，以确定只含有少数候选致病基因的小数量性状基因座， 基因.为了利用这一资源来鉴定与睡眠相关的基因，我们开发了一种高通量的 评估睡眠和昼夜节律的多个可遗传方面的表型分析管道， 睡眠强度、睡眠巩固程度、警觉性、睡眠驱力和昼夜节律周期。在发现重要的 基因座使用这种高通量方法，用金标准进行鉴定基因的稳健验证 EEG/EMG记录相关品系的协作交叉小鼠的睡眠，随后，在小鼠中， 预测的致病基因的敲除。使用这种方法，我们已经发现了一种新的基因， 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 基因型样本的多样性远交小鼠，现在已经确定了其他几个有趣的数量性状 含有需要验证的候选致病基因的基因座。虽然现有的分析表明， 通过在小鼠中识别睡眠的重要基因，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。相反，可能会有复杂的 涉及多个相互作用的基因的遗传效应，这些基因决定了睡眠/觉醒行为的额外可变性。 然而，目前的分析方法在多样性远交小鼠集中在加性遗传协会，并没有 不允许我们充分处理这个概念。因此，为了揭示这些更复杂的遗传效应，我们 建议采用新的机器学习方法来获得丰富的遗传和表型数据 我们有数百只不同的远系小鼠。总的来说，这是一个高风险、高影响的建议， 对于R21，给出了基于机器学习的新分析策略，并重点关注候选人的验证 影响睡眠的基因如果成功的话，这一提议将为遗传基础提供新的见解。 睡眠和一种新的分析资源，有利于任何正在进行或完成的研究多样性远交小鼠。

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.