Modern biomedical technologies initiated a rapid growth of knowledge about the human genome and its variability. Associations between the genome of an individual and his predisposition to various diseases are emerging. However, the evaluation of such genomic information is problematic for clinical geneticists without knowledge of data analysis.
Student will collect publicly available data sources about causative genetic variations, their impact, severity, and representation in selected populations (Slovak, European, African, etc.). Then, he will compare several published approaches to combining individual risk scores into an overall score. Finally, he will prepare an application for clinical geneticists and verify its accuracy on clinical data.
doc. Mgr. Tomáš Vinař, PhD.
Mgr. Jaroslav Budiš, PhD.
- (22.2. - 28.2.) Setting up server for web application and installation of required modules and tools
- (1.3. - 7.3.) Extraction of the single nucleotide variants by disease
- (8.3. - 14.3.) Extraction of the genotype variants from samples
- (15.3. - 21.3.) Extraction of all more specific diseases or terms of selected disease
- (22.3. - 28.3.) Improved design of the web application
- (5.4. - 11.4.) Research from some scientific articles
- (12.4. - 18.4.) Calculation of risk scores
- (19.4. - 25.4.) Implemented calculation into web application
- (26.4. - 2.5.) Impoved visualisation of risk scores
- (3.5. - 9.5.) Verification of the implemented calculation methods
- (10.5. - 16.5.) Some final web application adjustments
- (17.5. - 23.5.) Writing thesis
- Visscher (2017) 10 Years of GWAS Discovery_Biology, Function, and Translation
- Chatterjee (2016) Developing and evaluating polygenic risk prediction models for stratified disease prevention.
- Norrgard (2008) Calculation of Complex Disease Risk _ Learn Science at Scitable
- Warren (2018) The approach to predictive medicine that is taking genomics research by storm.
Mathematics for predicting risk scores
- Lloyed-Jones et al (2019) Improved polygenic prediction by Bayesian multiple regression on summary statistics.
- Mak et al (2017) Polygenic scores via penalized regression on summary statistics.
- Ge et al (2019) Polygenic prediction via Baysian regression and continuous shrinkage priors.
- Newcombe (2019) A flexible and paralizable approach to genome-wide PRS.
- Horne et al (2005) Generating genetic risk scores from intermediate phenotypes for use in association studies of clinically significant endpoints.
- Marees et al (2018) A tutorial on conducting genome-wide association studies _ Quality control and statistical analysis.
- Clarke et al (2011) Basic statistical analysis in genetic case-control strudies.
- Bailey et al (2019) Genetic risk scores.
- Janssens (2019) Validity of polygenic risk scores.
- Choi et al (2020) Tutorial _ a guide to performing polygenic risk score analyses