J Korean Med Sci.  2023 Nov;38(46):e395. 10.3346/jkms.2023.38.e395.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

• Khanna NN
• Singh M
• Maindarkar M
• Kumar A
• Johri AM
• Mentella L
• Laird JR
• Paraskevas KI
• Ruzsa Z
• Singh N
• Kalra MK
• Fernandes JFE
• Chaturvedi S
• Nicolaides A
• Rathore V
• Singh I
• Teji JS
• Al-Maini M
• Isenovic ER
• Viswanathan V
• Khanna P
• Fouda MM
• Saba L
• Suri JS

• KMID: 2548544
• DOI: http://doi.org/10.3346/jkms.2023.38.e395

Abstract

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Keyword

Cardiovascular Disease; Genomics; Polygenic Risk Score; Artificial Intelligence; Precision Medicine

Figure

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  Fig. 1 CVD PRS system and treatment planning using aiP3 model.OBBM = office-based biomarker, LBBM = laboratory-based biomarker, RBBM = radiomics-based biomarker, GBBM = genomics-based biomarker, PBBM = proteomics-based biomarker, PRS = Polygenic Risk Score, aiP3 = AI for preventive, precise, and personalized system, CVD = cardiovascular disease.

• 

  Fig. 2 PRISMA model for study selection.AI = artificial intelligence.

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  Fig. 3 Statistical distribution of various studies involved in PRS.PRS = Polygenic Risk Score, AI = artificial intelligence, CVD = cardiovascular diseases, ML = machine learning, DL = deep learning, HDL = hybrid deep learning, ACC = accuracy, SEN = sensitivity, AUC = area under curve, MCC = Mathews orrelation coefficient, SPE = specificity, NPV = net present value.

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  Fig. 4 Elements for calculating Polygenic Risk Score for CVD.CVD = cardiovascular diseases.

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  Fig. 5 GWAS analysis for calculating and interpreting PRS. A simplified illustration of the related phases involved in GWAS analysis is provided by this flowchart. The first step in the procedure is to locate the SNPs associated with the desired phenotype (Box 1) and calculate their effect sizes (Box 2). The weighted PRS is then determined by identifying the risk allele (Box 3 and 4). To allow for inter-individual comparisons, the PRS is then normalized (Box 5). The trait under investigation is then taken into consideration when interpreting the PRS (Box 6). Please take note that although the steps are listed sequentially for clarity, they are actually interrelated and iterative in nature.SNP = single nucleotide polymorphism, GWAS = Gene Wide Association Studies, PRS = Polygenic Risk Score.

• 

  Fig. 6 AI-powered genomic risk assessment in personalized medicine.AI = artificial intelligence.

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