Multifactorial conditions are complex health problems that have genetic and environmental influences. They are not dependent upon a variant in a single gene like cystic fibrosis or sickle cell anemia. A Polygenic Risk Score (PRS) aims to capture this cumulative impact from variants in many genes to estimate a risk of developing a specific health condition. Examples where PRS is being studied include cancer, diabetes, heart disease, neurodegenerative disorders, and psychiatric conditions, among others.

PRS is made possible by Genome Wide Association Studies (GWAS). GWAS analyzes the genomes of a large population of people to identify variants that occur more often in those with a specific disease compared to those who are unaffected. Once the genomic variants are identified, they are typically used to search for nearby variants that contribute directly to the disease or trait. The data is utilized to estimate the weighted sums of the genetic variants to determine the PRS. GWAS has become more accurate in the last decade, which adds more clinical utility to PRS.

Type 2 diabetes is a multifactorial chronic disease that is an excellent candidate for GWAS studies resulting in PRS. One study validated a PRS which could identify individuals with an approximate 2.5-4.5-fold risk increase for type 2 diabetes. The algorithm for the study analyzed genetic data from over a million people in three different ancestral populations. Theoretically, if PRS becomes clinically available, patients who know their increased diabetes risk based on PRS could potentially take risk-reducing measures such as diet or lifestyle modification.

The National Comprehensive Cancer Network (NCCN) publishes guidelines on cancer risk assessment based on family history, which is used by clinicians to care for patients. Family history is an integral part of the risk assessment. Those without a family history of specific types of cancer (breast, ovarian, colon cancer, etc.) are typically not flagged as increased risk. One study was able to estimate a colon cancer PRS in people without a family history that provided a similar cancer risk to those with a positive family history of colon cancer. Without the PRS, many people in the study population would have been classified as ‚Äúaverage risk‚ÄĚ even though they may benefit from additional cancer screening.

These two studies demonstrate the potential of PRS, and there is medical research exploring PRS for many other chronic and multifactorial health conditions. Therefore, the American College of Medical Genetics and Genomics (ACMG) has several educational resources to provide clinical and scientific guidance for PRS studies, laboratory methods, reporting, and their clinical application. Some clinical considerations from ACMG include:

  • PRS is a prediction of increased risk, not a diagnosis.
  • A low PRS score for a particular health condition does not reduce the risk to zero, and there may still be significant risk for disease.
  • PRS testing alone is not indicated if a monogenic condition is on the differential diagnosis.
  • Patients and healthcare providers should work collaboratively to determine if PRS testing should be ordered and how the results will be used for medical management.
  • Any PRS medical management should be based on medical evidence, which may be challenging given the limited data of this topic.
  • Clinicians should utilize professional medical guidelines when implementing PRS into clinical care.
  • Based on the lack of scientific data, PRS testing for embryos should not be offered at this time.

While there is evidence for PRS clinical utility, there are barriers to overcome before it is universally adopted. Results can be inconsistent among different studies and publications. This may be due to study design, various mathematical algorithms, or reporting methods. Furthermore, genetic ancestry can significantly impact the predictive value of PRS. PRS derived for a specific genetic ancestry may have a lower predictive value if applied to other populations.

To overcome some of these challenges, the Polygenic Score Catalog was created to be an opensource database (www.PGSCatalog.org). Genes, variants, traits, specific PRS, and publications can be accessed by clinicians and researchers. Additionally, there is a PRS workflow, which may be downloaded and used without restriction. As more researchers use this tool, it may reduce the heterogeneity and increase reproducibility of PRS studies.

PRS is a promising tool for healthcare providers to assess the risk for complex multifactorial health conditions. However, many different health conditions have been studied in various populations with mixed results that can be difficult to replicate at times. Fortunately, professional medical societies including ACMG and others are publishing resources to provide some standardization for the study design and implementation of PRS. The Polygenic Score Catalog further advances collaborative efforts among the medical research community. The next several years will likely see an increase in the clinical utilization of PRS in routine clinical care.

 

References:

Abu-El-Haija A, Reddi HV, Wand H, Rose NC, Mori M, Qian E, Murray MF; ACMG Professional Practice and Guidelines Committee. Electronic address: documents@acmg.net. The clinical application of polygenic risk scores: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2023 May;25(5):100803. PMID: 36920474.

https://pubmed.ncbi.nlm.nih.gov/36920474/

Ge T, Irvin MR, Patki A, Srinivasasainagendra V, Lin YF, Tiwari HK, Armstrong ND, Benoit B, Chen CY, Choi KW, Cimino JJ, Davis BH, Dikilitas O, Etheridge B, Feng YA, Gainer V, Huang H, Jarvik GP, Kachulis C, Kenny EE, Khan A, Kiryluk K, Kottyan L, Kullo IJ, Lange C, Lennon N, Leong A, Malolepsza E, Miles AD, Murphy S, Namjou B, Narayan R, O’Connor MJ, Pacheco JA, Perez E, Rasmussen-Torvik LJ, Rosenthal EA, Schaid D, Stamou M, Udler MS, Wei WQ, Weiss ST, Ng MCY, Smoller JW, Lebo MS, Meigs JB, Limdi NA, Karlson EW. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med. 2022 Jun 29;14(1):70. doi: 10.1186/s13073-022-01074-2. PMID: 35765100; PMCID: PMC9241245.

https://pubmed.ncbi.nlm.nih.gov/35765100/

Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, Buniello A, McMahon A, Abraham G, Chapman M, Parkinson H, Danesh J, MacArthur JAL, Inouye M. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat Genet. 2021 Apr;53(4):420-425. doi: 10.1038/s41588-021-00783-5. PMID: 33692568.

https://pubmed.ncbi.nlm.nih.gov/33692568/

Reddi HV, Wand H, Funke B, Zimmermann MT, Lebo MS, Qian E, Shirts BH, Zou YS, Zhang BM, Rose NC, Abu-El-Haija A; ACMG Laboratory Quality Assurance Committee. Electronic address: documents@acmg.net. Laboratory perspectives in the development of polygenic risk scores for disease: A points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med. 2023 May;25(5):100804. PMID: 36971772.

https://pubmed.ncbi.nlm.nih.gov/36971772/

Thomas M, Sakoda LC, Hoffmeister M, Rosenthal EA, Lee JK, van Duijnhoven FJB, Platz EA, Wu AH, Dampier CH, de la Chapelle A, Wolk A, Joshi AD, Burnett-Hartman A, Gsur A, Lindblom A, Castells A, Win AK, Namjou B, Van Guelpen B, Tangen CM, He Q, Li CI, Schafmayer C, Joshu CE, Ulrich CM, Bishop DT, Buchanan DD, Schaid D, Drew DA, Muller DC, Duggan D, Crosslin DR, Albanes D, Giovannucci EL, Larson E, Qu F, Mentch F, Giles GG, Hakonarson H, Hampel H, Stanaway IB, Figueiredo JC, Huyghe JR, Minnier J, Chang-Claude J, Hampe J, Harley JB, Visvanathan K, Curtis KR, Offit K, Li L, Le Marchand L, Vodickova L, Gunter MJ, Jenkins MA, Slattery ML, Lemire M, Woods MO, Song M, Murphy N, Lindor NM, Dikilitas O, Pharoah PDP, Campbell PT, Newcomb PA, Milne RL, MacInnis RJ, Castellví-Bel S, Ogino S, Berndt SI, Bézieau S, Thibodeau SN, Gallinger SJ, Zaidi SH, Harrison TA, Keku TO, Hudson TJ, Vymetalkova V, Moreno V, Martín V, Arndt V, Wei WQ, Chung W, Su YR, Hayes RB, White E, Vodicka P, Casey G, Gruber SB, Schoen RE, Chan AT, Potter JD, Brenner H, Jarvik GP, Corley DA, Peters U, Hsu L. Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. Am J Hum Genet. 2020 Sep 3;107(3):432-444. PMID: 32758450.

https://pubmed.ncbi.nlm.nih.gov/32758450/

Wand H, Lambert SA, Tamburro C, Iacocca MA, O’Sullivan JW, Sillari C, Kullo IJ, Rowley R, Dron JS, Brockman D, Venner E, McCarthy MI, Antoniou AC, Easton DF, Hegele RA, Khera AV, Chatterjee N, Kooperberg C, Edwards K, Vlessis K, Kinnear K, Danesh JN, Parkinson H, Ramos EM, Roberts MC, Ormond KE, Khoury MJ, Janssens ACJW, Goddard KAB, Kraft P, MacArthur JAL, Inouye M, Wojcik GL. Improving reporting standards for polygenic scores in risk prediction studies. Nature. 2021 Mar;591(7849):211-219. PMID: 33692554.

https://pubmed.ncbi.nlm.nih.gov/33692554/

Zhou X, Chen Y, Ip FCF, Jiang Y, Cao H, Lv G, Zhong H, Chen J, Ye T, Chen Y, Zha¬†¬†¬†¬†¬†¬†¬†¬† ng Y, Ma S, Lo RMN, Tong EPS; Alzheimer‚Äôs Disease Neuroimaging Initiative; Mok VCT, Kwok TCY, Guo Q, Mok KY, Shoai M, Hardy J, Chen L, Fu AKY, Ip NY. Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction. Commun Med (Lond). 2023 Apr 6;3(1):49. PMID: 37024668.

https://pubmed.ncbi.nlm.nih.gov/37024668/

Centers for Disease Control and Prevention: Polygenic Risk Scores

https://www.cdc.gov/genomics/disease/polygenic.htm

National Institutes of Health: NIH researchers develop guidelines for reporting polygenic risk scores

https://www.nih.gov/news-events/news-releases/nih-researchers-develop-guidelines-reporting-polygenic-risk-scores

National Genome Research Institute

https://www.genome.gov/genetics-glossary/Genome-Wide-Association-Studies

National Comprehensive Cancer Network (NCCN)

https://www.nccn.org/patients/guidelines/content/PDF/colon-patient.pdf

https://www.nccn.org/patients/guidelines/content/PDF/breastcancerscreening-patient.pdf

https://www.nccn.org/patients/guidelines/content/PDF/ovarian-patient.pdf

The Polygenic Score (PGS) Catalog

http://www.pgscatalog.org/