Analyzing Genome-wide Association Study Data with the R Package genMOSS

2016/11/22 by Matthew Friedlander, Adrian Dobra, Friedlander, Matthew +6
Biochemistry, Genetics and Molecular Biology · Mathematics · #62-07 (Primary) #Bioinformatics and Genomic Networks #Computation (stat.CO) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Quantitative Methods (q-bio.QM) #msc:62-07 #q-bio.QM #stat.CO

paper · pdf · doi:10.48550/arxiv.1611.07537

10 pages

arxiv created 2016/11/22 · openalex publication_date 2016/11/22 · arxiv updated 2016/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The R package (R Core Team (2016)) genMOSS is specifically designed for the Bayesian analysis of genome-wide association study data. The package implements the mode oriented stochastic search (MOSS) procedure as well as a simple moving window approach to identify combinations of single nucleotide polymorphisms associated with a response. The prior used in Bayesian computations is the generalized hyper Dirichlet.

Citations

• Statistical significance for genomewide studies

Related

• SAGA (Simplified Association Genome-wide Analyses): a user-friendly Pipeline to Democratize Genome-Wide Association Studies
• BREADR: An R Package for the Bayesian Estimation of Genetic Relatedness from Low-coverage Genotype Data
• PopGenome: An Efficient Swiss Army Knife for Population Genomic Analyses in R
• rMVP: A Memory-Efficient, Visualization-Enhanced, and Parallel-Accelerated Tool for Genome-Wide Association Study
• Gene and Gene-Set Analysis for Genome-Wide Association Studies