Polygenic risk scores: improving the prediction of future disease or added complexity?

Ayoub, Aya; McHugh, Jana; Hayward, Judith; Rafi, Imran; Qureshi, Nadeem

doi:10.3399/bjgp22x720437

Cited by 2 publications

(2 citation statements)

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“…This creates a barrier to clinical translation, as testing for hundreds or thousands of SNPs is not always practical or cost-effective in clinical settings. Moreover, incorporating too many weak predictors may lead to biased results and challenges in replicating findings across different cohorts [ 5 – 7 ]. While prior research has largely concentrated on association testing and polygenic risk scores, the application of Machine Learning (ML) to discern significant genotype–phenotype correlations has been marginally explored.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing prediction accuracy of coronary artery disease through machine learning-driven genomic variant selection

Alireza,

Maleeha,

Kaikkonen

et al. 2024

J Transl Med

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Machine learning (ML) methods are increasingly becoming crucial in genome-wide association studies for identifying key genetic variants or SNPs that statistical methods might overlook. Statistical methods predominantly identify SNPs with notable effect sizes by conducting association tests on individual genetic variants, one at a time, to determine their relationship with the target phenotype. These genetic variants are then used to create polygenic risk scores (PRSs), estimating an individual's genetic risk for complex diseases like cancer or cardiovascular disorders. Unlike traditional methods, ML algorithms can identify groups of low-risk genetic variants that improve prediction accuracy when combined in a mathematical model. However, the application of ML strategies requires addressing the feature selection challenge to prevent overfitting. Moreover, ensuring the ML model depends on a concise set of genomic variants enhances its clinical applicability, where testing is feasible for only a limited number of SNPs. In this study, we introduce a robust pipeline that applies ML algorithms in combination with feature selection (ML-FS algorithms), aimed at identifying the most significant genomic variants associated with the coronary artery disease (CAD) phenotype. The proposed computational approach was tested on individuals from the UK Biobank, differentiating between CAD and non-CAD individuals within this extensive cohort, and benchmarked against standard PRS-based methodologies like LDpred2 and Lassosum. Our strategy incorporates cross-validation to ensure a more robust evaluation of genomic variant-based prediction models. This method is commonly applied in machine learning strategies but has often been neglected in previous studies assessing the predictive performance of polygenic risk scores. Our results demonstrate that the ML-FS algorithm can identify panels with as few as 50 genetic markers that can achieve approximately 80% accuracy when used in combination with known risk factors. The modest increase in accuracy over PRS performances is noteworthy, especially considering that PRS models incorporate a substantially larger number of genetic variants. This extensive variant selection can pose practical challenges in clinical settings. Additionally, the proposed approach revealed novel CAD-genetic variant associations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%