AI Insight
Researchers developed targPred, a new computational tool that uses genomic regulatory blocks to identify which genes are affected by disease-associated genetic variants located in non-coding regions of the genome. The approach addresses a major challenge in genome-wide association studies by connecting distant regulatory elements to their target genes, revealing that diseases like cancer and psychiatric disorders frequently involve long-range genetic regulation. As a proof of concept, the team demonstrated a connection between a childhood obesity-associated genetic locus and the distant BDNF gene.
Why it matters
This tool could significantly accelerate the translation of genetic discoveries into biological understanding by helping researchers identify the actual genes responsible for disease risk, even when the genetic variants are located far from the genes they regulate. The web-based service makes this analytical capability accessible to the broader research community studying various diseases and traits.
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⚠️ Preprint – Noch nicht peer-reviewed
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Genome-wide association studies (GWAS) are the key tools for the discovery of associations between single nucleotide polymorphisms (SNPs) and phenotypic traits and have been successfully applied to many diseases and disorders. However, a great challenge is to find the gene affected by the non-coding fraction of SNPs, especially if the gene is distal in terms of genomic distance. In this study, we present a novel approach, named targPred, which utilises genomic regulatory blocks (GRBs) for inference of a connection between a certain SNP/locus and the target gene located in the same GRB, in a more robust and generalisable manner. We identified that many disease traits such as cancer and psychiatric disease have a propensity for long-range regulation. Furthermore, we showcased a childhood obesity locus which is connected to the distal BDNF gene. Finally, we propose a new web-based service based on enhancer-promoter association, to facilitate finding the causal genes for a wide array of traits and conditions.