A Web Portal for Communicating Polygenic Risk Score Results for Health Care Use-The P5 Study

Genetics-based risk scores for prediction of premature coronary artery disease

Premature coronary artery disease (CAD) is endemic in India. Global Burden of Diseases study has reported that it led to 286,000 deaths in 2019 in India. Many of these patients have standard risk factors but a third have none. Clinical risk algorithms and imaging provide limited risk information in premature CAD. CAD is multifactorial and studies have now focused on the predictive capability of clusters of genes and single nucleotide polymorphisms (SNPs) using gene risk score (GRS). Older studies combined data from 10 to 12 genes and 100-500 SNPs to calculate GRS, however, following the advent of genome-wide association studies (GWAS), millions of SNPs have been incorporated. Studies have reported that GWAS-based GRS may be more discriminative than conventional tools. Recent studies, especially among South Asians, have reported that GRS improves net reclassification by 15% (12-19%) for younger individuals. Aggressive lifestyle interventions and lipid-lowering therapies can ameliorate risk in high-GRS individuals and potentially prevent premature CAD.

Genome screening, reporting, and genetic counseling for healthy populations

Rapid advancements of genome sequencing (GS) technologies have enhanced our understanding of the relationship between genes and human disease. To incorporate genomic information into the practice of medicine, new processes for the analysis, reporting, and communication of GS data are needed. Blood samples were collected from adults with a PCR-confirmed SARS-CoV-2 (COVID-19) diagnosis (target N = 1500). GS was performed. Data were filtered and analyzed using custom pipelines and gene panels. We developed unique patient-facing materials, including an online intake survey, group counseling presentation, and consultation letters in addition to a comprehensive GS report. The final report includes results generated from GS data: (1) monogenic disease risks; (2) carrier status; (3) pharmacogenomic variants; (4) polygenic risk scores for common conditions; (5) HLA genotype; (6) genetic ancestry; (7) blood group; and, (8) COVID-19 viral lineage. Participants complete pre-test genetic counseling and confirm preferences for secondary findings before receiving results. Counseling and referrals are initiated for clinically significant findings. We developed a genetic counseling, reporting, and return of results framework that integrates GS information across multiple areas of human health, presenting possibilities for the clinical application of comprehensive GS data in healthy individuals.

Economic evaluation of using polygenic risk score to guide risk screening and interventions for the prevention of type 2 diabetes in individuals with high overall baseline risk

Type 2 diabetes (T2D) with increasing prevalence is a significant global public health challenge. Obesity, unhealthy diet, and low physical activity are one of the major determinants of the rise in T2D prevalence. In addition, family history and genetic risk of diabetes also play a role in the process of developing T2D. Therefore, solutions for the early identification of individuals at high risk for T2D for early targeted detection of T2D, prevention, and intervention are highly preferred. Recently, novel genomic-based polygenic risk scores (PRSs) have been suggested to improve the accuracy of risk prediction supporting the targeting of preventive interventions to those at highest risk for T2D. Therefore, the aim of the present study was to assess the cost-utility of an additional PRS testing information (as a part of overall risk assessment) followed by a lifestyle intervention and an additional medical therapy when estimated 10-year overall risk for T2D exceeded 20% among Finnish individuals screened as at the high-risk category (i.e., 10%–20% 10-year overall risk of T2D) based on traditional risk factors only. For a cost-utility analysis, an individual-level state-transition model with probabilistic sensitivity analysis was constructed. A 1-year cycle length and a lifetime time horizon were applied in the base-case. A 3% discount rate was used for costs and QALYs. Cost-effectiveness acceptability curve (CEAC) and estimates for the expected value of perfect information (EVPI) were calculated to assist decision makers. The use of the targeted PRS strategy reclassified 12.4 percentage points of individuals to be very high-risk individuals who would have been originally classified as high risk using the usual strategy only. Over a lifetime horizon, the targeted PRS was a dominant strategy (i.e., less costly, more effective). One-way and scenario sensitivity analyses showed that results remained dominant in almost all simulations. However, there is uncertainty, since the probability (EVPI) of cost-effectiveness at a WTP of 0€/QALY was 63.0% (243€) indicating the probability that the PRS strategy is a dominant option. In conclusion, the results demonstrated that the PRS provides moderate additional value in Finnish population in risk screening leading to potential cost savings and better quality of life when compared with the current screening methods for T2D risk.

Psychosocial Effects of Receiving Genome-Wide Polygenic Risk Information Concerning Type 2 Diabetes and Coronary Heart Disease: A Randomized Controlled Trial

Receiving polygenic risk estimates of future disease through health care or direct-to-consumer companies is expected to become more common in the coming decades. However, only a limited number of studies have examined if such estimates might evoke an adverse psychosocial reaction in receivers. The present study utilized data from a sub-section of a personalized medicine project (the P5 study) that combines genomic and traditional health data to evaluate participants’ risk for certain common diseases. We investigated how communication of future disease risk estimates related to type 2 diabetes and coronary heart disease influenced respondents’ risk perception, self-efficacy, disease-related worry, and other emotions. A randomized controlled trial was conducted, where the experimental group (n = 714) received risk estimates based on traditional and polygenic risk factors and the control group (n = 649) based solely on traditional risk factors. On average, higher disease risk was associated with higher perceived risk (ps, <0.001, η_p² = 0.087–0.071), worry (ps <0.001, η_p² = 0.061–0.028), lower self-efficacy (p <0 .001, η_p² = 0.012), less positive emotions (ps <0.04, η_p² = 0.042–0.005), and more negative emotions (ps <0.048, η_p² = 0.062–0.006). However, we found no evidence that adding the polygenic risk to complement the more traditional risk factors would induce any substantive psychosocial harm to the recipients (ps >0.06).

Reaching for Precision Healthcare in Finland via Use of Genomic Data

Concerns over future healthcare capacity along with continuing demands for sustainability call for novel solutions to improve citizens' health and wellbeing through effective prevention and improved diagnosis and treatment. Part of the solution to tackle the challenge could be making the most of the exploitation of genomic data in personalized risk assessment, creating new opportunities for data-driven precision prevention and public health. Presently, the utilization of genomic data in the Finnish healthcare system is limited to a few medical specialty areas. To successfully extend the use of genomic information in everyday healthcare, evidence-based and feasible strategies are needed. The national actions that Finland is taking towards this goal are 1) providing scientific evidence for the utility of genomic information for healthcare purposes; 2) evaluating the potential health-economic impact of implementing precision healthcare in Finland; 3) developing a relevant legal framework and infrastructures for the utilization of genomic information; 4) building a national multidisciplinary expert network bringing together relevant professionals and initiatives to achieve consensus among the different stakeholders on specific issues vital for translating genomic data into precision healthcare; 5) building competence and genomic literacy skills among various target groups; and 6) public engagement (informing and educating the public). Taken together, these actions will enable building a roadmap towards the expedient application of genomic data in Finnish healthcare and promoting the health of our citizens.

Translating osteoarthritis genetics research: challenging times ahead

The ultimate goal of molecular genetic studies of human diseases is to translate the discoveries for patient benefit. For diseases that lack licensed disease-modifying therapeutics, such as osteoarthritis (OA), the need is acute. OA is polygenic and affects older individuals, with a recent genome-wide study of over 800 000 individuals adding 52 novel association signals to those already reported on for this common arthritis. Many of the predicted effector genes of these signals encode proteins that are targets of drugs for other indications, highlighting repurposing opportunities. Here, the potential for OA genetic data to translate is discussed, including whether the developmental origin of OA will limit the application of genetic risk data for disease-modification purposes.