The Puzzle of Disease-Causing Gene Clusters: Insights from Genome-Wide Studies
Extent of Genetic Variants Linked to Diseases: Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to human diseases. However, these studies often face limitations in detecting rare and low-frequency alleles and tracing causal mechanisms to specific genes. A significant study combined whole-exome sequencing from 392,814 UK Biobank participants with genotypes from 260,405 FinnGen participants, covering a total of 653,219 individuals. This approach allowed for meta-analyses across the protein-coding allelic frequency spectrum for 744 disease endpoints, identifying 975 associations with more than one-third being previously unreported.
Types of Diseases Associated with Genetic Variants: Diseases can be dichotomized into those caused by coding mutations in single genes, which are often rare but highly penetrant (Mendelian diseases), and common diseases showing complex patterns of inheritance influenced by hundreds of low-impact, typically non-coding genetic variants (complex diseases). Large human cohorts systematically characterized for specific traits have been instrumental in identifying thousands of disease-relevant variants through sequencing-based approaches or GWAS.
Specific Findings: About 48,000 coding variants were tested for associations with 744 distinct disease endpoints. This study identified 975 significant associations, with 534 variants across 301 regions linked to 148 disease clusters. It was found that 482 unique genes are associated with these disease clusters. Notably, 92% of the associated regions for each disease cluster (excluding the MHC cluster) harbor a single gene with coding associations.
Importance of Disease-Causing Gene Clusters
Understanding disease-causing gene clusters is crucial because:
It enables the identification of specific genes that contribute to disease risk, onset, and progression.
Helps in elucidating disease mechanisms, potentially leading to novel therapeutic targets.
Facilitates the identification of mutations previously ascribed to single-gene disorders and establishes their relevance at the population level.
Disease Pleiotropy Index (DPI) and Disease Specificity Index (DSI)
The DPI and DSI are metrics used to evaluate the specificity and pleiotropy of genes in relation to diseases:
Disease Pleiotropy Index (DPI):
It considers whether the diseases associated with a gene are similar or belong to different disease classes. A higher DPI indicates that a gene is associated with a diverse range of diseases.
Disease Specificity Index (DSI):
This is inversely proportional to the number of diseases associated with a gene. A lower DSI indicates a gene is linked to multiple diseases.
These indexes were used in a study from DisGeNET, which includes 6,780 disease associations with 36,169 gene-disease association pairs. The distribution of DSI and DPI for 255 genes was analyzed, highlighting the complex interplay between genes and the diseases they influence.
Conclusion
The research in this domain emphasizes the intricate relationship between genetic variants and diseases. The findings not only highlight the prevalence of disease associations in the human genome but also underscore the importance of understanding gene-disease interactions for medical research and potential therapeutic interventions. The DPI and DSI scores provide a nuanced view of the specificity and pleiotropy of genes, crucial for comprehending the genetic underpinnings of diseases. As the field advances, such insights promise to pave the way for more targeted and effective treatments for various diseases.
Reference:
Sun, B. B., Kurki, M. I., Foley, C. N., Mechakra, A., Chen, C. Y., Marshall, E., ... & Runz, H. (2022). Genetic associations of protein-coding variants in human disease. Nature, 603(7899), 95-102.
Piñero, J., Ramírez-Anguita, J. M., Saüch-Pitarch, J., Ronzano, F., Centeno, E., Sanz, F., & Furlong, L. I. (2020). The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic acids research, 48(D1), D845-D855.
Salnikova, L. E., Chernyshova, E. V., Anastasevich, L. A., & Larin, S. S. (2019). Gene-and disease-based expansion of the knowledge on inborn errors of immunity. Frontiers in Immunology, 10, 2475.
