DiGAS: Differential gene allele spectrum as a descriptor in genetic studies

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 s...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 179; p. 108924
Main Authors: Aparo, Antonino, Bonnici, Vincenzo, Avesani, Simone, Cascione, Luciano, Giugno, Rosalba
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.09.2024
Elsevier Limited
Subjects:
Alleles
Alzheimer Disease - genetics
Alzheimer's disease
Classification
Computational Biology - methods
Databases, Genetic
Gene allele
Genetic analysis
Genetic disorders
Genome-wide association studies
Genome-Wide Association Study
Genomic analysis
Genomic variations
Humans
Internal Medicine
Neurodegenerative diseases
Nucleotides
Other
Polymorphism, Single Nucleotide
Population (statistical)
Population genetics
Single-nucleotide polymorphism
Software
Spectrum analysis
Statistical analysis
99-00
00-01
Alzheimer’s disease
Classification
Genomic variations
Gene allele
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary: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.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2024.108924