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Novel algorithm developed for finding disease-susceptible genes
Researchers have developed a novel statistical algorithm that is capable of identifying potential disease genes in a more accurate and cost-effective way. This algorithm has also been considered as a new promising approach for the identification of candidate disease genes, as it works effectively with less genomic data and takes only a minute or two to get results, the researchers said.
Seoul : Researchers have developed a novel statistical algorithm that is capable of identifying potential disease genes in a more accurate and cost-effective way. This algorithm has also been considered as a new promising approach for the identification of candidate disease genes, as it works effectively with less genomic data and takes only a minute or two to get results, the researchers said.
In the study, published in the journal Nucleic Acids Research, the researchers presented the novel method and software GSA-SNP2 for pathway enrichment analysis of GWAS P-value data. According to the team, GSA-SNP2 provides high power, decent type I error control and fast computation by incorporating the random set model and SNP-count adjusted gene score.
The researchers said that each individual's genome is a unique combination of DNA sequences that play major roles in determining who we are, accounting for all individual differences including susceptibility for disease and diverse phenotypes.
Such genetic variation among humans are known as single nucleotide polymorphisms (SNPs). SNPs that correlate with specific diseases could serve as predictive biomarkers to aid the development of new drugs.
