Publication: ASSESSING THE PERFORMANCE OF A POLYGENIC RISK SCORES (PRS) MODEL FOR TYPE 2 DIABETES MELLITUS IN A MALAYSIAN POPULATION
| dc.contributor.author | CHEW XIN YI | |
| dc.date.accessioned | 2025-11-12T04:01:37Z | |
| dc.date.available | 2025-11-12T04:01:37Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Type 2 diabetes (T2DM) represents a major global public health challenge. Despite being a common chronic illness, its underlying disease mechanism remains highly complex, driven by a multifactorial interplay between genetic predisposition and lifestyle exposures. Numerous genetic variants associated with the susceptibility of T2DM have been identified, allowing the development of polygenic risk score (PRS) to predict an individual’s risk in disease development, yet to which extend such PRS is applicable in Malaysian cohort has not been assessed. This study evaluates a polygenic risk score (PRS) model for T2DM within a Malaysian cohort. Genetic variants associated to T2DM were curated using proprietary methods informed by prior research on the UK Biobank dataset. For the current evaluation study, anonymized data from 613 Malaysian individuals was used. PRS calculations and model assessments were conducted using customised R scripts. A total of 1,983 single nucleotide polymorphisms (SNPs) were included in the final dataset. Logistic regression was employed to test the association between PRS and T2DM status. The model demonstrated strong discriminative performance, with an area under the curve (AUC) of 0.96 (CI: 0.95-0.97). Additional evaluation metrics, including positive predictive value (PPV), negative predictive value (NPV), Sensitivity and Specificity, all ranged between 0.8 to 0.9, showing high predictive robustness. Calibration analysis further supported the model’s reliability, with a Brier score of 0.077. Beyond the primary analysis, a comparative evaluation was conducted to refine the model. Fisher’s exact test was applied to filter SNPs with less discriminatory power (p < 0.01), resulting in a reduced variant panel and a subsequent drop in performance metrics. This highlights the trade-off between statistical stringency and predictive coverage. Overall, these findings validate the potential of SNPs for prediction in a multi-ethnic Malaysian population. While current model shows strong predictive capability, future work is needed to enhance its generalizability and clinical utility. This study validates a UK Biobank derived PRS for T2DM in a Malaysian cohort, demonstrating its practicability within an industry-led dataset. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14377/37248 | |
| dc.language.iso | en | |
| dc.publisher | IMU University | |
| dc.subject | Genetic Risk Score | |
| dc.subject | Genetic Predisposition to Disease | |
| dc.subject | Diabetes Mellitus, Type 2 | |
| dc.subject | Risk Assessment | |
| dc.subject | Models, Genetic | |
| dc.subject | Malaysia | |
| dc.title | ASSESSING THE PERFORMANCE OF A POLYGENIC RISK SCORES (PRS) MODEL FOR TYPE 2 DIABETES MELLITUS IN A MALAYSIAN POPULATION | |
| dc.type | Thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# |