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基于微计算机断层扫描的斑马鱼骨骼表型组学研究揭示了分布式器官系统中深度表型分析的优点。

MicroCT-based phenomics in the zebrafish skeleton reveals virtues of deep phenotyping in a distributed organ system.

机构信息

Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, United States.

Center for Medical Genetics, Ghent University, Ghent, Belgium.

出版信息

Elife. 2017 Sep 8;6:e26014. doi: 10.7554/eLife.26014.

Abstract

Phenomics, which ideally involves in-depth phenotyping at the whole-organism scale, may enhance our functional understanding of genetic variation. Here, we demonstrate methods to profile hundreds of phenotypic measures comprised of morphological and densitometric traits at a large number of sites within the axial skeleton of adult zebrafish. We show the potential for vertebral patterns to confer heightened sensitivity, with similar specificity, in discriminating mutant populations compared to analyzing individual vertebrae in isolation. We identify phenotypes associated with human brittle bone disease and thyroid stimulating hormone receptor hyperactivity. Finally, we develop allometric models and show their potential to aid in the discrimination of mutant phenotypes masked by alterations in growth. Our studies demonstrate virtues of deep phenotyping in a spatially distributed organ system. Analyzing phenotypic patterns may increase productivity in genetic screens, and facilitate the study of genetic variants associated with smaller effect sizes, such as those that underlie complex diseases.

摘要

表型组学(phenomics),理想情况下涉及整个生物体规模的深入表型分析,可能会增强我们对遗传变异功能的理解。在这里,我们展示了在成年斑马鱼的轴向骨骼的大量部位中分析数百种表型测量值的方法,这些表型测量值包括形态和密度特征。我们表明,与单独分析单个椎体相比,椎体模式具有更高的敏感性,同时具有相似的特异性,可用于区分突变群体。我们确定了与人类脆骨病和促甲状腺激素受体活性过度相关的表型。最后,我们开发了异速生长模型,并展示了它们在区分由生长变化掩盖的突变表型方面的潜力。我们的研究证明了在空间分布的器官系统中进行深度表型分析的优点。分析表型模式可能会提高遗传筛选的效率,并有助于研究与较小效应大小相关的遗传变异,例如那些与复杂疾病相关的遗传变异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f42/5606849/67287d1f751e/elife-26014-fig1.jpg

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