Genetic disorders are caused due to alterations in the DNA sequence. The changes in the DNA sequence can take place in a single gene or multiple genes. Monogenic disorders are a result of changes in a single gene occurring in all cells. Monogenic disorders are rare and their inheritance can be dominant or recessive. Few examples of monogenic disorders are sickle cell disease, cystic fibrosis, thalassaemia, Tay-Sachs disease etc.

Disease-associated variants have been identified through GWAS. However, for most of these the contribution to disease was found to be negligible. Several years on, we now have tools to accurately determine the cumulative effect of these small effect genetic variations to calculate a PRS, that measures the susceptibility of an individual to a particular polygenic disease². Common diseases, such as type 2 diabetes, coronary artery disease, atrial fibrillation, inflammatory bowel disease, breast cancer and many neurodegenerative diseases disorders such as schizophrenia, bipolar disorder, etc. tend to be polygenic, in other words, influenced by a large number of genetic variants scattered throughout the genome, as well as environmental and lifestyle factors. The genetic susceptibility of an individual for such diseases can be estimated through PRS/GPS.

The PRS is calculated from independent risk variants, associated with the disease, as evidenced from GWAS data. It is a summation of the effects of all component variants. Distribution of PRS in a cohort is plotted bell curve distribution with both ends of the curves representing extremes of the outcomes, low and high risk, while the middle/peak represents moderate risk. High risk individuals, shown in the high risk end of the curve benefit by getting a chance to take adequate precautionary measures⁵.

With the increasing focus on personalized medicine, modeling of PRS for complex diseases becomes more relevant, especially for diseases that have been long known as “lifestyle diseases”, like coronary artery disease, diabetes, etc. A single blood/saliva test can estimate one’s polygenic risk and identify measures for corrective action. When combined with other data, it might be possible to predict risk and prevent the harmful outcome, years or decades in advance.

High polygenic risk scores, for common polygenic or multifactorial diseases have been proven to have the comparable risk to rare monogenic forms. Also, the polygenic and monogenic risks in an individual are additive¹¹. If clinicians had access to this information, it would enable them to offer the same counseling and interventions that they do for patients meeting other high-risk criteria. For example, a breast cancer PRS can stratify women based on 10-year and lifetime risk. Based on United Kingdom screening guidelines, if implemented in clinical care ubiquitously, the PRS alone could identify women likely to account for 17% of total breast cancer cases in the population. This could allow the system to optimize screening strategies (e.g., via mammogram timing and frequency) to better align with individuals’ risk⁶. The stage is set for getting PRS to the clinical/diagnostic field although this still requires validating the findings of such studies to the target population. Ethnicity plays a major role in interpreting PRS. There are significant differences in genetic variation frequencies and linkage disequilibrium patterns between various ethnic groups. Therefore, the need for systematic evaluation of polygenic score performance in the population for which is to be applied is imperative¹².

Coronary artery disease (CAD) is a complex disease, the onset of which is regulated by the complex interplay for multiple genetic and environmental factors. It is a condition in which the blood vessels that supply the heart with blood, oxygen, and nutrients (called coronary arteries) get blocked due to deposition of a waxy material called plaque, which can lead to a heart attack. As of date the focus has been on the environmental and lifestyle factors. Now we also know the polygenic risk that regulates this disease and with a PRS scoring model this polygenic risk can be quantified¹⁵.

CAD remains one of the main causes of morbidity and mortality worldwide. Existing risk prediction models incorporate information about age, sex, and clinical history (hypertension, blood cholesterol, smoking, and diabetes) to calculate the probability of a CAD event. Certain models have proven to underestimate the risk of CAD in genetically predisposed Indian population. Therefore, it is more relevant to develop risk predictive scores for application to the Indian population⁹.

Genome wide association studies have identified >100 loci associated with CAD, mostly in populations of European ancestry. CAD has a high heritability (50-60%), however, apart from testing for familial hypercholesterolemia, which leads to CAD, other genetic testing is not routinely used for CAD. Genome-wide PRS for CAD is now available to be applied in the clinical setting and is being validated across populations^8,11,13.

Polygenic risk scores have gained attention in predicting complex disorders such as breast cancer, Alzheimer's, Atrial fibrillation among others ^11,14. Acute coronary incidents have become very common lately with an increasing number of young onset cases below 40-45 years complaining of chest pain/angina and heart attack. Detection of underlying polygenic risk combined with prevention and planned informed disease management, such as active lifestyle modifications, use of statins, or intensified mammography screening for breast cancer, can together help reduce the disease burden in the population.

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• Lewis, Cathryn M., and Evangelos Vassos. "Prospects for using risk scores in polygenic medicine." Genome medicine 9.1 (2017): 1-3.
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• Homburger, Julian R., et al. "Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores."  Genome medicine 11.1 (2019): 1-12.
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• Wünnemann, Florian, et al. "Validation of genome-wide polygenic risk scores for coronary artery disease in French Canadians." Circulation: Genomic and Precision Medicine 12.6 (2019): e002481.
• Kanjilal, S., et al. "Application of cardiovascular disease risk prediction models and the relevance of novel biomarkers to risk stratification in Asian Indians." Vascular health and risk management 4.1 (2008): 199
• Roberts, Robert. "Genetics of coronary artery disease." Circulation research 114.12 (2014): 1890-1903.
• Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. Genome-wide polygenic scores for common diseases identify inliiduals with risk equivalent to monogenic mutations. Nat Genet. 2018 Sep;50(9):1219-1224. doi: 10.1038/s41588-018-0183-z. Epub 2018 Aug 13. PMID: 30104762; PMCID: PMC6128408.
• Duncan L, Shen H, Gelaye B, Meijsen J, Ressler K, Feldman M, Peterson R, Domingue B. Analysis of polygenic risk score usage and performance in lierse human populations. Nat Commun. 2019 Jul 25;10(1):3328. doi: 10.1038/s41467-019-11112-0. PMID: 31346163; PMCID: PMC6658471.
• Nikpay M, Goel A, etal.,. A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet. 2015 Oct;47(10):1121-1130. doi: 10.1038/ng.3396. Epub 2015 Sep 7. PMID: 26343387; PMCID: PMC4589895.
• Tripathi P. Steps in Genetic Research of Complex Disease. Glob J Res Rev. 2017, 4:1.
• “American Heart Association.” Www.heart.org, www.heart.org/.
• Muse ED, Wineinger NE, Spencer EG, Peters M, Henderson R, Zhang Y, et al. Validation of a genetic risk score for atrial fibrillation: A prospective multicenter cohort study. PLOS Medicine. 2018;15(3).