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eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data.

Authors :
Yang, Yingxi
Sun, Quan
Huang, Le
Broome, Jai G
Correa, Adolfo
Reiner, Alexander
Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed)
Raffield, Laura M
Yang, Yuchen
Li, Yun
Source :
Briefings in Bioinformatics. Jan2022, Vol. 23 Issue 1, p1-13. 13p.
Publication Year :
2022

Abstract

Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
23
Issue :
1
Database :
Academic Search Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
155892380
Full Text :
https://doi.org/10.1093/bib/bbab497