DiGAS: Differential gene allele spectrum as a descriptor in genetic studies - Aparo, Antonino; Bonnici, Vincenzo; Avesani, Simone; Cascione, Luciano; Giugno, Rosalba (Computers in biology and medicine, 2024)
Diagnosing individuals with complex genetic diseases is a challenging task. Computational methodologies exploit information at the genotype level by taking into account single nucleotide polymorphisms (SNPs) leveraging the results of genome-wide association studies analysis to assign a statistical significance to each SNP. Recent methodologies extend such an approach by aggregating SNP significance at the genetic level to identify genes that are related to the condition under study. However, such methodologies still suffer from the initial SNP analysis limitations. Here, we present DiGAS, a tool for diagnosing genetic conditions by computing significance, by means of SNP information, directly at the complex level of genetic regions. Such an approach is based on a generalized notion of allele spectrum, which evaluates the complete genetic alterations of the SNP set belonging to a genetic region at the population level. The statistical significance of a region is then evaluated through a differential allele spectrum analysis between the conditions of individuals belonging to the population. Tests, performed on well-established datasets regarding Alzheimer’s disease, show that DiGAS outperforms the state of the art in distinguishing between sick and healthy subjects. •We introduce a new generalized version of allele frequency spectrum.•We propose a methodology, called DiGAS, based on the new defined genomic information and independent from GWAS analysis that outperforms existing methods in distinguish healthy/ill subjects with a speed up of 5x.•On a reference Alzheimer’s disease genomic datasets, ADNI, DiGAS reaches F1 score up to 0.94.•DiGAS methodology manages any type of genomic features, such as genes, exons, upstream/downstream regions.