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利用整合多组学和多表型分析对血脂异常的治疗靶点进行优先排序。

Prioritization of therapeutic targets for dyslipidemia using integrative multi-omics and multi-trait analysis.

机构信息

Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea.

Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.

出版信息

Cell Rep Med. 2023 Sep 19;4(9):101112. doi: 10.1016/j.xcrm.2023.101112. Epub 2023 Aug 14.

DOI:10.1016/j.xcrm.2023.101112
PMID:37582372
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10518515/
Abstract

Drug targets with genetic support are several-fold more likely to succeed in clinical trials. We introduce a genetic-driven approach based on causal inferences that can inform drug target prioritization, repurposing, and adverse effects of using lipid-lowering agents. Given that a multi-trait approach increases the power to detect meaningful variants/genes, we conduct multi-omics and multi-trait analyses, followed by network connectivity investigations, and prioritize 30 potential therapeutic targets for dyslipidemia, including SORT1, PSRC1, CELSR2, PCSK9, HMGCR, APOB, GRN, HFE2, FJX1, C1QTNF1, and SLC5A8. 20% (6/30) of prioritized targets from our hypothesis-free drug target search are either approved or under investigation for dyslipidemia. The prioritized targets are 22-fold higher in likelihood of being approved or under investigation in clinical trials than genome-wide association study (GWAS)-curated targets. Our results demonstrate that the genetic-driven approach used in this study is a promising strategy for prioritizing targets while informing about the potential adverse effects and repurposing opportunities.

摘要

具有遗传支持的药物靶点在临床试验中成功的可能性要高出数倍。我们引入了一种基于因果推理的遗传驱动方法,该方法可以为药物靶点的优先级排序、重新定位和降脂药物的不良反应提供信息。鉴于多特征方法可以提高检测有意义的变异/基因的能力,我们进行了多组学和多特征分析,然后进行网络连接性研究,为血脂异常优先排序了 30 个潜在的治疗靶点,包括 SORT1、PSRC1、CELSR2、PCSK9、HMGCR、APOB、GRN、HFE2、FJX1、C1QTNF1 和 SLC5A8。在我们无假设的药物靶点搜索中,优先排序的靶点有 6/30%(6/30)已经被批准或正在研究用于血脂异常。与全基因组关联研究(GWAS) curated 靶点相比,优先排序的靶点在临床试验中被批准或正在研究的可能性要高出 22 倍。我们的研究结果表明,本研究中使用的遗传驱动方法是一种有前途的策略,可用于优先排序靶点,同时提供潜在的不良反应和重新定位机会的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/789f163d1138/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/5458b92183d2/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/555070bf6d2e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/9d277a01c035/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/7c0feb10e399/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/51bfd5f49fe4/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/a784ab243839/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/789f163d1138/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/5458b92183d2/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/555070bf6d2e/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/9d277a01c035/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/7c0feb10e399/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/51bfd5f49fe4/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/a784ab243839/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9844/10518515/789f163d1138/gr6.jpg

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