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CWAS-Plus:利用全基因组测序数据和细胞类型特异性功能数据估计罕见非编码变异的全类别关联。

CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.

作者信息

Kim Yujin, Jeong Minwoo, Koh In Gyeong, Kim Chanhee, Lee Hyeji, Kim Jae Hyun, Yurko Ronald, Kim Il Bin, Park Jeongbin, Werling Donna M, Sanders Stephan J, An Joon-Yong

机构信息

Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea.

L-HOPE Program for Community-Based Total Learning Health Systems, Korea University, Seoul, 02841, Republic of Korea.

出版信息

medRxiv. 2024 Apr 15:2024.04.15.24305828. doi: 10.1101/2024.04.15.24305828.

Abstract

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 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.

摘要

顺式调控元件中的变异将非编码基因组与人类脑部病理学联系起来;然而,目前缺乏用于理解细胞水平脑部病理学与非编码变异之间关联的详细分析工具。CWAS-Plus改编自一个用于全类别关联测试(CWAS)的Python软件包,它利用全基因组测序和用户提供的功能数据来加强非编码变异分析,能更快、更高效地执行CWAS工作流程。在这里,我们使用了转座酶可及染色质的单核测序分析,以促进在细胞类型特异性增强子和启动子处进行CWAS引导的非编码变异分析。通过检查自闭症谱系障碍全基因组测序数据(n = 7280),CWAS-Plus在保守基因座内的转录因子结合位点中识别出非编码变异关联。另外,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异关联。这些发现突出了CWAS-Plus在基因组疾病中的实用性,以及在处理大规模全基因组测序数据和多重检验校正方面的可扩展性。CWAS-Plus及其用户手册分别可在https://github.com/joonan-lab/cwas/和https://cwas-plus.readthedocs.io/en/latest/获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d08/11065022/0ed531c5fe18/nihpp-2024.04.15.24305828v1-f0001.jpg

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