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

초록

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. Graphical AbstractFlowchart illustrating the CWAS-Plus workflow. The process starts with a variant list (showing chromosome positions and reference/alternate alleles) combined with annotation datasets (enhancer, promoter, conserved, constrained regions, and disease risk genes). CWAS-Plus processes this information, creating categories by combining variant annotations (variant type, gene set, functional score, genomic region, and functional annotation). These categories undergo annotation and categorization, followed by a burden test (volcano plot displaying p-values and relative risk). The analysis includes risk score analysis (bar graph of model performance for different genomic regions), feature selection (density plot showing higher phenotype association), and DAWN analysis (network graph highlighting disease-associated clusters with varying z-scores).

키워드