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肌肉骨骼系统知识库:使组学数据对更广泛的科学界有用。

The Musculoskeletal Knowledge Portal: Making Omics Data Useful to the Broader Scientific Community.

机构信息

Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife Boston, MA, USA.

Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.

出版信息

J Bone Miner Res. 2020 Sep;35(9):1626-1633. doi: 10.1002/jbmr.4147.

Abstract

The development of high-throughput genotyping technologies and large biobank collections, complemented with rapid methodological advances in statistical genetics, has enabled hypothesis-free genome-wide association studies (GWAS), which have identified hundreds of genetic variants across many loci associated with musculoskeletal conditions. Similarly, basic scientists have valuable molecular cellular and animal data based on musculoskeletal disease that would be enhanced by being able to determine the human translation of their findings. By integrating these large-scale human genomic musculoskeletal datasets with complementary evidence from model organisms, new and existing genetic loci can be statistically fine-mapped to plausibly causal variants, candidate genes, and biological pathways. Genes and pathways identified using this approach can be further prioritized as drug targets, including side-effect profiling and the potential for new indications. To bring together these big data, and to realize the vision of creating a knowledge portal, the International Federation of Musculoskeletal Research Societies (IFMRS) established a working group to collaborate with scientists from the Broad Institute to create the Musculoskeletal Knowledge Portal (MSK-KP)(http://mskkp.org/). The MSK consolidates omics datasets from humans, cellular experiments, and model organisms into a central repository that can be accessed by researchers. The vision of the MSK-KP is to enable better understanding of the biological mechanisms underlying musculoskeletal disease and apply this knowledge to identify and develop new disease interventions. © 2020 American Society for Bone and Mineral Research (ASBMR).

摘要

高通量基因分型技术和大型生物库的发展,加上统计遗传学方法的快速进展,使得无假设的全基因组关联研究(GWAS)成为可能,GWAS 已经确定了数百个与肌肉骨骼疾病相关的遗传变异。同样,基础科学家也有基于肌肉骨骼疾病的有价值的分子细胞和动物数据,如果能够确定其研究结果在人类中的转化,这些数据将得到增强。通过整合这些大规模的人类肌肉骨骼基因组数据集和来自模式生物的互补证据,可以对新的和现有的遗传基因座进行统计精细映射,以确定可能的因果变异、候选基因和生物学途径。使用这种方法鉴定的基因和途径可以进一步被优先作为药物靶点,包括副作用分析和新适应症的潜力。为了汇集这些大数据,并实现创建知识门户的愿景,国际肌肉骨骼研究学会联合会(IFMRS)成立了一个工作组,与 Broad 研究所的科学家合作创建了肌肉骨骼知识门户(MSK-KP)(http://mskkp.org/)。MSK 将人类、细胞实验和模式生物的组学数据集整合到一个中央存储库中,研究人员可以访问该存储库。MSK-KP 的愿景是更好地理解肌肉骨骼疾病的生物学机制,并将这一知识应用于识别和开发新的疾病干预措施。© 2020 美国骨骼与矿物质研究协会(ASBMR)。

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