Article ; Online: LDmat: efficiently queryable compression of linkage disequilibrium matrices.
Bioinformatics (Oxford, England)
2023 Volume 39, Issue 2
Abstract: Motivation: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can ... ...
| Abstract | Motivation: Linkage disequilibrium (LD) matrices derived from large populations are widely used in population genetics in fine-mapping, LD score regression, and linear mixed models for Genome-wide Association Studies (GWAS). However, these matrices can reach large sizes when they are derived from millions of individuals; hence, moving, sharing and extracting granular information from this large amount of data can be cumbersome. Results: We sought to address the need for compressing and easily querying large LD matrices by developing LDmat. LDmat is a standalone tool to compress large LD matrices in an HDF5 file format and query these compressed matrices. It can extract submatrices corresponding to a sub-region of the genome, a list of select loci, and loci within a minor allele frequency range. LDmat can also rebuild the original file formats from the compressed files. Availability and implementation: LDmat is implemented in python, and can be installed on Unix systems with the command 'pip install ldmat'. It can also be accessed through https://github.com/G2Lab/ldmat and https://pypi.org/project/ldmat/. Supplementary information: Supplementary data are available at Bioinformatics online. |
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| MeSH term(s) | Humans ; Linkage Disequilibrium ; Software ; Genome-Wide Association Study ; Data Compression ; Genome | |||||
| Language | English | |||||
| Publishing date | 2023-03-13 | |||||
| Publishing country | England | |||||
| Document type | Journal Article ; Research Support, N.I.H., Extramural | |||||
| ZDB-ID | 1422668-6 | |||||
| ISSN | 1367-4811 ; 1367-4803 | |||||
| ISSN (online) | 1367-4811 | |||||
| ISSN | 1367-4803 | |||||
| DOI | 10.1093/bioinformatics/btad092 | |||||
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| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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