NEBULA (Novel Entropy-Based framework for Unbiased Locus Analysis) is an open-source R package for SNP-set association analysis in case-control studies with binary phenotypes. The method uses entropy-based statistics to capture pairwise interactions among SNPs within predefined genomic regions, genes, pathways, or other functionally relevant SNP-sets.
NEBULA is designed to jointly analyze common and rare variants, including variants with protective and deleterious effects. It is implemented in R and C++, combining an accessible R interface with optimized, multithreaded routines for the analysis of medium- and large-scale genomic datasets. Chunk-based data processing and adaptive resampling reduce memory usage and computational time, making the framework suitable for whole-genome sequencing studies.
Software Notes
Motivation: Genome-wide association studies generally test variants individually and may therefore fail to capture the cumulative or interacting effects of multiple variants contributing to complex phenotypes. SNP-set methods address this limitation by aggregating variants according to genomic or biological criteria. However, entropy-based approaches such as ABACUS, although robust to mixtures of common and rare variants and to effects acting in opposite directions, are computationally demanding when applied to modern whole-genome sequencing datasets.
Results: NEBULA introduces a redesigned computational architecture based on efficient memory management, parallel execution, chunk-based data processing, and optimized statistical computation. An adaptive priority-queue strategy limits the storage and computation required for null-distribution estimation, while multithreaded processing and filtering of uninformative comparisons improve the efficiency of association testing. Benchmarking results show that NEBULA substantially reduces memory consumption and execution time, enabling entropy-based SNP-set analyses on genomic datasets that were previously computationally infeasible.
Citation
If you use NEBULA in your research, please refer to the original paper with the following citation:
Mikele Milia, Giacomo Baruzzo, Barbara Di Camillo, NEBULA: A scalable and efficient framework for SNP-set analysis in the era of whole genome sequencing, Journal of Computational Science, Volume 100, 2026, 102960, https://doi.org/10.1016/j.jocs.2026.102960.
Download
The latest version of NEBULA is freely available under the GNU General Public License v3.0 at https://gitlab.com/sysbiobig/nebula.