| Title: | Understanding disease subtypes from genotype information |
| Lead Institution: | University of Tokyo |
| Principal investigator: | Dr Shinichi Morishita |
Application 48405
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About
A number of genetic loci that are associated with common diseases have been identified. Several methods have been developed to estimate the effect of each single nucleotide variant in candidate loci for a specific disorder; however, these methods, however, likely underestimate or overlook disease subtypes that have more than one subtype with different characters. For example, different subtypes of breast cancer are associated with bone, brain, and lung metastases and require different treatments. While subtypes have typically been characterised based on phenotypes or transcriptomes, alternative approaches have also been explored to define subtypes in terms of genotype. Genotypic differences could be a clue to understanding genetic causes that lead to different subtypes and could therefore be useful in finding new mechanisms or uncovering drug targets. Given that developing new drugs is prohibitively expensive, drug repositioning towards finding effective existing drugs has been widely attempted to reduce the associated costs. For example, two FDA-approved drugs are effective at reducing brain metastasis derived from breast cancer, although they do not work for bone and lung metastases. This example of drug repurposing demonstrates the importance of analysing individual disease subtypes independently when some drugs work well only on a proportion of patients. We exploited a computational method of estimating subtypes according to the genotypes relevant to the phenotype. To demonstrate the effectiveness of this approach, we propose to apply our method to UK Biobank data. We hope this approach will provide a greater understanding of the causes of diseases and will be able to predict, for example, candidate subtypes for which existing drugs may work. We would conduct this research for 3 years. This research agrees with the stated aim of UK Biobank: "improving the prevention and treatment diagnosis of serious and life-threatening illnesses."1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11959 | A polygenic score method boosted by non-additive models | Rikifumi Ohta (+4) | 2024 | Nature Communications |
Enabling scientific discoveries that improve human health