ABACUS

ABACUS identifies single nucleotide polymorphisms (SNPs) significantly associated with diseases within predefined SNP sets, enabling detection of combined effects of rare and common variants in complex pathologies such as diabetes and neurological disorders.

Key Features:

• Robustness to Variant Effects: Handles SNPs with both protective and detrimental effects and accommodates the presence of both common and rare variants within the same dataset.
• High Power with Sparse Signals: Maintains statistical power when only a subset of SNPs within a set is associated with the phenotype.
• Pathway and Region-Based Analysis: Analyzes predefined SNP sets such as biological pathways or specific genomic regions to assess collective variant impact.
• SNP-set and SNP-level Output: Reports associated SNP-sets and the significant SNPs within each set when applied to genome-wide SNP data.

Scientific Applications:

• Pathway discovery in complex diseases: Identifies biologically relevant pathways associated with multifactorial diseases, including applications to diabetes and neurological disorders.
• Diabetes genetic association analysis: Applied to type 1 and type 2 diabetes datasets to confirm known associations and to identify novel pathways implicated in disease etiology.
• Benchmarking on simulated and real data: Used on both simulated and real-world datasets to demonstrate efficacy in uncovering SNP-set associations.

Methodology:

ABACUS implements an Algorithm based on a BivAriate CUmulative Statistic (bivariate cumulative statistic) to test associations between SNPs and phenotypes, explicitly accommodating protective and detrimental effects and both rare and common variants.