A new SNP comparison (SnpC) method for detecting unique/enriched gene-SNP variants and comparing population gene mutation diversity

Comparing mutation patterns and population-specific genetic signatures, such as single-nucleotide polymorphisms (SNPs), is of fundamental importance in genetic and genomic studies today. The problem is challenging because it falls into the category of NP-hard problems, meaning that the computational time increases exponentially, or even faster, as the problem size grows. Additionally, existing approaches often fails to account for the exact number of SNPs per gene (gene-SNP abundance) nor their distribution across populations, hindering the detection of genes that are uniquely or richly mutated in specific groups. To bridge this gap, by harnessing gene-SNP abundance and distribution information as well as their heterogeneity across individuals and populations, we develop the SnpC (SNP comparison) method (framework) for detecting genes with unique or enriched SNPs, referred to as unique genes (UGs) and enriched genes (EGs) for specific population. The power of SnpC lies in two novel metrics: Gene-SNP Heterogeneity (GSH), which identifies genes with unique or enriched number of SNPs in one population compared to others, and Population Gene-SNP Diversity (PGSD), which measures the population-level diversity of gene mutations including SNP. These metrics are coupled with robust permutation tests to provide statistical significance. We demonstrate SnpC on data from the 1000 Genomes Project, where it successfully catalogs population-specific (unique/enriched) genes and quantifies their inter-population mutation diversity pattern. SnpC delivers a unique, actionable output for gene prioritization in association studies and comparative population genomic analysis. It is easily extended to other variation types, and the R code is provided in the article's supplements.