Developing Computational Solutions for Personalized Medicine
| Main Author: | Reisberg Sulev |
|---|---|
| Format: | info publication-thesis Journal |
| Terbitan: |
, 2019
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/4265226 |
Daftar Isi:
- (PhD Thesis abstract) The general idea of personalized medicine is to provide more effective clinicalcare and prevention of diseases by utilizing individual differences mostly in ge-netics, but also in detailed electronic health records (EHR) and other data. I pro-vide an overview of the definition and its elements of personalized medicine, alsothe current state of the field. To date, personalized medicine is used in oncologyand for testing developmental diseases in children. For more broader use, severalchallenges need to be dealt with. Some of these are addressed in this thesis. Weshow, by using genetic data from Estonian Biobank and 1000 Genomes Project,that polygenic risk score models are biased towards Europeans and should notbe used for people from other populations. Similarly, frequencies of single nu-cleotide variants associated with asthma and liver diseases among Estonians areclose to Europeans but different from the others. To bring personalized medicineinto state-level clinical use, one has to integrate them to the workflows of theEHR systems. By combining genetic data and EHR of the participants of Es-tonian Biobank, we conducted a phenome-wide association study, by utilizinggenetic variants related to asthma and liver diseases in order to find new gene-disease associations. Although we did not identify novel associations, we showedthat this data could be effectively used for validation studies. Finally, we describea pharmacogenomics recommendation pipeline for producing individual pharma-cogenomic recommendations for 44,000 gene donors. We show that genotypingarrays with imputation can be used as cost-effective alternatives for whole genomesequencing in pharmacogenomic testing.