Genetic risk prediction platform for common non-communicable diseases
2
Issued Date
2024
Copyright Date
2019
Resource Type
Language
eng
File Type
application/pdf
No. of Pages/File Size
x, 130 leaves : ill.
Access Rights
open access
Rights
ผลงานนี้เป็นลิขสิทธิ์ของมหาวิทยาลัยมหิดล ขอสงวนไว้สำหรับเพื่อการศึกษาเท่านั้น ต้องอ้างอิงแหล่งที่มา ห้ามดัดแปลงเนื้อหา และห้ามนำไปใช้เพื่อการค้า
Rights Holder(s)
Mahidol University
Bibliographic Citation
Thesis (M.Sc. (Medical Bioinformatics))--Mahidol University, 2019
Suggested Citation
Kittisak Taoma Genetic risk prediction platform for common non-communicable diseases. Thesis (M.Sc. (Medical Bioinformatics))--Mahidol University, 2019. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/92204
Title
Genetic risk prediction platform for common non-communicable diseases
Author(s)
Abstract
Genetic risk score (GRS) is one of the important aspects in precision medicine for early detection of the non - communicable diseases (NCDs). However, the genome wide association studies (GWAS) have been conducted mainly in the Europeans, and the resulting summary statistics are effectively generalized to the Europeans themselves. Thus, the unpredictable bias or the low predictive ability will be observed in GRS study of the non - European population. To tackle these issues, we aimed to develop the online genetic risk prediction platform (MGRS https://hub.docker.com/repository/docker/kittisak1803/mgrs) to predict the genetic risk in the five NCDs including coronary artery disease, hypertension, type 2 diabetes, stroke, and chronic obstructive pulmonary disease. Also, the five-NCD GRS distribution with the multi-ethnic summary statistics will be observed in the Thai population when compared with the reference population from 1000 genome project phase 1 (1KGp1) as well as evaluated the weighting strategies in these five NCDs. Firstly, the MGRS platform will facilitate the researcher to calculate the GRS to predict the genetic risk with the multi-ethnic summary statistics in five NCDs. This application was written in R with the shiny package and dockerized into container technology. With the variant call format (VCF) or PLINK flat file format (PED/MAP), the user can calculate the GRS and compare with the general population in 1KGp1.With this convenience, this application will help the researcher or the epidemiologist without a computer background to facilitate the GRS calculation for continuing the GRS downstream analysis. Secondly, we explored five-NCD GRS distribution of the 1,806-individual Thai population against 1KGp1. Overall, the five-NCD GRS distribution were different across ethnicities (one - way ANOVA, p < 0.0001). In the Thai population, the GRS distributions were closely related to the East Asians. These similarities were strongly supported by the high correlation of risk allele frequency with r2 > 0.90 as well as the population structure. However, the unpredictable GRS distribution between the Thais and the East Asians (Turkey's test < 0.05) were also observed. These two possibilities might imply that the GRS cut off from the genetically related population should be empirically evaluated before applying. Besides, when comparing globally, the Thai population tended to have higher risk than the global average, especially CAD (GRSThai = 3.46 vs GRSGlobal = 3.42). This might suggest that we should give a priority to the CAD in the GRS study in the Thai population, resulting in increasing the awareness of CAD, thus leading to disease prevention. However, the validation of the GRS in the Thai population in five NCDs is required before we can use the GRS in the clinical setting in the future. Also, with the high correlation between weighted GRS (wGRS) and unweighted GRS (uGRS), the predictive ability of both wGRS and uGRS should be empirically evaluated in the Thai population in order to yield the optimal model.
Description
Medical Bioinformatics (Mahidol University 2019)
Degree Name
Master of Science
Degree Level
Master's degree
Degree Department
Faculty of Medicine Siriraj Hospital
Degree Discipline
Medical Bioinformatics
Degree Grantor(s)
Mahidol University