Researchers have developed a novel statistical algorithm that is able to more accurately and cost-effectively identify potential disease genes.
This algorithm has also been seen as a promising new approach for identifying candidates for disease genes. how it works effectively with less genomic data and only takes a minute or two to get results, the researchers said.
In the study, published in the journal Nucleic Acids Research, researchers presented the novel method and software GSA-SNP2 for analyzing the pathway enrichment of GWAS P-value data.
According to the team, the GSA-SNP2 offers high performance, good type I error control, and fast computation through inclusion of the random set model and the SNP count-adjusted gene value.
"GSA-SNP2 is a powerful and efficient tool for signal pathway enrichment and network analysis of genome-wide association study (GWAS) summary data," said Dougu Nam of the Ulsan National Institute for Science and Technology (UNIST) in South Korea.
"With this algorithm, we can easily identify new drug targets, thereby deepening our understanding of diseases and unlocking new therapies for their treatment," added Nam.
The researchers said that the genome of each person is a unique combination of DNA sequences that play a crucial role in all individual differences including susceptibility to disease and various phenotypes.
Such genetic variation among humans is known as Single Nucleotide Polymorphisms (SNPs). SNPs that correlate with certain diseases may serve as predictive biomarkers for the development of new drugs.
Statistical analysis of GWAS summary data makes it possible to identify disease-associated SNPs, researchers said.
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