regulatory noncoding variant

  • 文章类型: Journal Article
    顺式调控元件的变异将非编码基因组与人类病理学联系起来;然而,缺乏详细的分析工具来理解细胞水平的脑病理学与非编码变异之间的关联.CWAS-Plus,改编自用于类别范围关联测试(CWAS)的Python包,通过整合全基因组测序(WGS)和用户提供的功能数据来增强非编码变异分析。通过简化的参数设置和高效的多重测试校正方法,CWAS-Plus执行CWAS工作流程的速度比CWAS快50倍,使研究人员更容易获得和用户友好。这里,我们对转座酶可接近的染色质进行了单核测定,并进行了测序,以促进CWAS指导的细胞类型特异性增强子和启动子的非编码变异分析.检查自闭症谱系障碍WGS数据(n=7280),CWAS-Plus在保守基因座内的转录因子结合位点中鉴定出非编码从头变体关联。独立地,在阿尔茨海默病WGS数据(n=1087)中,CWAS-Plus在小胶质细胞特异性调控元件中检测到罕见的非编码变体关联。这些发现强调了CWAS-Plus在基因组疾病中的实用性和处理大规模WGS数据和多重测试校正的可扩展性。CWAS-Plus及其用户手册可在https://github.com/joonan-lab/cwas/和https://cwas-plus获得。readthedocs.io/en/latest/,分别。
    Variants in cis-regulatory elements link the noncoding genome to human pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS), enhances noncoding variant analysis by integrating both whole-genome sequencing (WGS) and user-provided functional data. With simplified parameter settings and an efficient multiple testing correction method, CWAS-Plus conducts the CWAS workflow 50 times faster than CWAS, making it more accessible and user-friendly for researchers. Here, we used a single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type-specific enhancers and promoters. Examining autism spectrum disorder WGS data (n = 7280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer\'s disease WGS data (n = 1087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus\'s utility in genomic disorders and scalability for processing large-scale WGS data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.
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  • 文章类型: Preprint
    顺式调控元件的变异将非编码基因组与人脑病理学联系起来;然而,缺乏详细的分析工具来理解细胞水平的脑病理学与非编码变异之间的关联.CWAS-Plus,适用于类别范围关联测试(CWAS)的Python软件包采用全基因组测序和用户提供的功能数据来增强非编码变异分析,更快、更高效地执行CWAS工作流。这里,我们对转座酶可接近的染色质进行了单核测定,并进行了测序,以促进CWAS指导的细胞类型特异性增强子和启动子的非编码变异分析.检查自闭症谱系障碍全基因组测序数据(n=7,280),CWAS-Plus在保守基因座内的转录因子结合位点中鉴定出非编码从头变体关联。独立地,在阿尔茨海默病全基因组测序数据(n=1087)中,CWAS-Plus在小胶质细胞特异性调控元件中检测到罕见的非编码变体关联。这些发现强调了CWAS-Plus在基因组疾病中的实用性和处理大规模全基因组测序数据和多重测试校正的可扩展性。CWAS-Plus及其用户手册可在https://github.com/joonan-lab/cwas/和https://cwas-plus获得。readthedocs.io/en/latest/,分别。
    结论:CWAS-Plus可有效识别WGS数据中的非编码关联,支持用户友好的分类和负担丰富测试。CWAS-Plus集成了各种功能数据集,强调细胞类型特异性非编码关联。CWAS-Plus为多重测试校正提供了一种新颖的方法,提高结果的可靠性。自闭症谱系障碍风险非编码变体被鉴定为富含转录因子,表明它们在病理学中的作用。阿尔茨海默病样本的罕见变异分析揭示了与小胶质细胞的强烈关联,支持CWAS-Plus产生的结果的可靠性。
    Variants in cis-regulatory elements link the noncoding genome to human brain pathology; however, detailed analytic tools for understanding the association between cell-level brain pathology and noncoding variants are lacking. CWAS-Plus, adapted from a Python package for category-wide association testing (CWAS) employs both whole-genome sequencing and user-provided functional data to enhance noncoding variant analysis, with a faster and more efficient execution of the CWAS workflow. Here, we used single-nuclei assay for transposase-accessible chromatin with sequencing to facilitate CWAS-guided noncoding variant analysis at cell-type specific enhancers and promoters. Examining autism spectrum disorder whole-genome sequencing data (n = 7,280), CWAS-Plus identified noncoding de novo variant associations in transcription factor binding sites within conserved loci. Independently, in Alzheimer\'s disease whole-genome sequencing data (n = 1,087), CWAS-Plus detected rare noncoding variant associations in microglia-specific regulatory elements. These findings highlight CWAS-Plus\'s utility in genomic disorders and scalability for processing large-scale whole-genome sequencing data and in multiple-testing corrections. CWAS-Plus and its user manual are available at https://github.com/joonan-lab/cwas/ and https://cwas-plus.readthedocs.io/en/latest/, respectively.
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