CASMAP: detection of statistically significant combinations of SNPs in association mapping

Felipe Llinares-López; Laetitia Papaxanthos; Damian Roqueiro; Dean Bodenham; Karsten Borgwardt

doi:10.1093/bioinformatics/bty1020

CASMAP: detection of statistically significant combinations of SNPs in association mapping

Bioinformatics. 2019 Aug 1;35(15):2680-2682. doi: 10.1093/bioinformatics/bty1020.

Authors

Felipe Llinares-López^{1

2}, Laetitia Papaxanthos^{1

2}, Damian Roqueiro^{1

2}, Dean Bodenham^{1

2}, Karsten Borgwardt^{1

2}

Affiliations

¹ Department of Biosystems Science and Engineering, ETH Zurich, Basel 4058, Switzerland.
² SIB Swiss Institute of Bioinformatics, Switzerland.

Abstract

Summary: Combinatorial association mapping aims to assess the statistical association of higher-order interactions of genetic markers with a phenotype of interest. This article presents combinatorial association mapping (CASMAP), a software package that leverages recent advances in significant pattern mining to overcome the statistical and computational challenges that have hindered combinatorial association mapping. CASMAP can be used to perform region-based association studies and to detect higher-order epistatic interactions of genetic variants. Most importantly, unlike other existing significant pattern mining-based tools, CASMAP allows for the correction of categorical covariates such as age or gender, making it suitable for genome-wide association studies.

Availability and implementation: The R and Python packages can be downloaded from our GitHub repository http://github.com/BorgwardtLab/CASMAP. The R package is also available on CRAN.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genome-Wide Association Study
Phenotype
Polymorphism, Single Nucleotide*
Software