Implementation and implications for polygenic risk scores in healthcare

Acute pancreatitis risk in multifactorial chylomicronemia syndrome depends on the molecular cause of severe hypertriglyceridemia

BACKGROUND AND AIMS

Multifactorial chylomicronemia syndrome (MCS) is a severe form of hypertriglyceridemia (hyperTG) associated with an increased risk of acute pancreatitis (AP). Severe hyperTG is mainly polygenic in nature, either caused by the presence of heterozygous pathogenic variants (PVs) in TG-related metabolism genes or by accumulation of common variants in hyperTG susceptibility genes. This study aims to determine if the risk of AP is similar amongst MCS patients with different molecular causes of severe hyperTG.

METHODS

This study included 114 MCS patients who underwent genetic testing for PVs in TG-related metabolism genes and 16 single nucleotide polymorphisms (SNPs) in hyperTG susceptibility genes. A weighted TG-polygenic risk score (TG-PRS) was calculated. A TG-PRS score ≥ 90th percentile was used to define a high TG-PRS.

RESULTS

Overall, 66.7% of patients had severe hyperTG of polygenic origin. MCS patients with only a PV and those with both a PV and high TG-PRS were more prone to have maximal TG concentration ≥ 40 mmol/L (OR 5.33 (1.55-18.36); p = 0.008 and OR 5.33 (1.28-22.25); p = 0.02), as well as higher prevalence of AP (OR 3.64 (0.89-14.92); p = 0.07 and OR 11.90 (2.54-55.85); p = 0.002) compared to MCS patients with high TG-PRS alone.

CONCLUSIONS

This is the first study to show that MCS caused by a high TG-PRS and a PV is associated with higher risk of AP, similar to what is seen in the monogenic form of severe hyperTG. This suggests that determining the molecular cause of severe hyperTG could be useful to stratify the risk of pancreatitis in MCS.

Breast Cancer Polygenic Risk Score Validation and Effects of Variable Imputation

Breast cancer (BC) is a complex disease affecting one in eight women in the USA. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to augment current risk models, but replication is often limited. We evaluated 2 robust PRSs with 313 and 3820 SNPs and the effects of multiple genotype imputation replications in BC cases and control populations. Biological samples from BC cases and cancer-free controls were drawn from three European ancestry cohorts. Genotyping on the Illumina Global Screening Array was followed by stringent quality control measures and 20 genotype imputation replications. A total of 468 unrelated cases and 4337 controls were scored, revealing significant differences in mean PRS percentiles between cases and controls (p < 0.001) for both SNP sets (313-SNP PRS: 52.81 and 48.07; 3820-SNP PRS: 55.45 and 49.81), with receiver operating characteristic curve analysis showing area under the curve values of 0.596 and 0.603 for the 313-SNP and 3820-SNP PRS, respectively. PRS fluctuations (from ~2-3% up to 9%) emerged across imputation iterations. Our study robustly reaffirms the predictive capacity of PRSs for BC by replicating their performance in an independent BC population and showcases the need to average imputed scores for reliable outcomes.

Return of polygenic risk scores in research: Stakeholders' views on the eMERGE-IV study

Research on polygenic risk scores (PRSs) for common, genetically complex chronic diseases aims to improve health-related predictions, tailor risk-reducing interventions, and improve health outcomes. Yet, the study and use of PRSs in clinical settings raise equity, clinical, and regulatory challenges that can be greater for individuals from historically marginalized racial, ethnic, and other minoritized communities. As part of the National Human Genome Research Institute-funded Electronic Medical Records and Genomics IV Network, we conducted online focus groups with patients/community members, clinicians, and members of institutional review boards to explore their views on key issues, including PRS research, return of PRS results, clinical translation, and barriers and facilitators to health behavioral changes in response to PRS results. Across stakeholder groups, our findings indicate support for PRS development and a strong interest in having PRS results returned to research participants. However, we also found multi-level barriers and significant differences in stakeholders' views about what is needed and possible for successful implementation. These include researcher-participant interaction formats, health and genomic literacy, and a range of structural barriers, such as financial instability, insurance coverage, and the absence of health-supporting infrastructure and affordable healthy food options in poorer neighborhoods. Our findings highlight the need to revisit and implement measures in PRS studies (e.g., incentives and resources for follow-up care), as well as system-level policies to promote equity in genomic research and health outcomes.

Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores

Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.

Perceived benefits and barriers to implementing precision preventive care: Results of a national physician survey

Polygenic risk scores (PRS) may improve risk-stratification in preventive care. Their clinical implementation will depend on primary care physicians' (PCPs) uptake. We surveyed PCPs in a national physician database about the perceived clinical utility, benefits, and barriers to the use of PRS in preventive care. Among 367 respondents (participation rate 96.3%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p < 0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and insurance discrimination concerns (22%) as extreme barriers. Latent class analysis identified 3 subclasses of respondents. Skeptics (n = 83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n = 134, 36.5%) and enthusiasts (n = 150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS. Overall results suggest that PCPs generally endorse using PRS to guide medical decision-making about preventive care, and barriers identified suggest interventions to address their needs and concerns.

Disentangling the complexity of psoriasis in the post-genome-wide association era

In recent years, genome-wide association studies (GWAS) have been instrumental in unraveling the genetic architecture of complex diseases, including psoriasis. The application of large-scale GWA studies in psoriasis has illustrated several associated loci that participate in the cutaneous inflammation, however explaining a fraction of the disease heritability. With the advent of high-throughput sequencing technologies and functional genomics approaches, the post-GWAS era aims to unravel the functional mechanisms underlying the inter-individual variability in psoriasis patients. In this review, we present the key advances of psoriasis GWAS in under-represented populations, rare, non-coding and structural variants and epistatic phenomena that orchestrate the interplay between different cell types. We further review the gene-gene and gene-environment interactions contributing to the disease predisposition and development of comorbidities through Mendelian randomization studies and pleiotropic effects of psoriasis-associated loci. We finally examine the holistic approaches conducted in psoriasis through system genetics and state-of-the-art transcriptomic analyses, discussing their potential implication in the expanding field of precision medicine and characterization of comorbidities.

Clinical utility of polygenic risk scores: a critical 2023 appraisal

Since their first appearance in the context of schizophrenia and bipolar disorder in 2009, polygenic risk scores (PRSs) have been described for a large number of common complex diseases. However, the clinical utility of PRSs in disease risk assessment or therapeutic decision making is likely limited because PRSs usually only account for the heritable component of a trait and ignore the etiological role of environment and lifestyle. We surveyed the current state of PRSs for various diseases, including breast cancer, diabetes, prostate cancer, coronary artery disease, and Parkinson disease, with an extra focus upon the potential improvement of clinical scores by their combination with PRSs. We observed that the diagnostic and prognostic performance of PRSs alone is consistently low, as expected. Moreover, combining a PRS with a clinical score at best led to moderate improvement of the power of either risk marker. Despite the large number of PRSs reported in the scientific literature, prospective studies of their clinical utility, particularly of the PRS-associated improvement of standard screening or therapeutic procedures, are still rare. In conclusion, the benefit to individual patients or the health care system in general of PRS-based extensions of existing diagnostic or treatment regimens is still difficult to judge.

Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores

Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.

Gone Too Soon: priorities for action to prevent premature mortality associated with mental illness and mental distress

Globally, too many people die prematurely from suicide and the physical comorbidities associated with mental illness and mental distress. The purpose of this Review is to mobilise the translation of evidence into prioritised actions that reduce this inequity. The mental health research charity, MQ Mental Health Research, convened an international panel that used roadmapping methods and review evidence to identify key factors, mechanisms, and solutions for premature mortality across the social-ecological system. We identified 12 key overarching risk factors and mechanisms, with more commonalities than differences across the suicide and physical comorbidities domains. We also identified 18 actionable solutions across three organising principles: the integration of mental and physical health care; the prioritisation of prevention while strengthening treatment; and the optimisation of intervention synergies across social-ecological levels and the intervention cycle. These solutions included accessible, integrated high-quality primary care; early life, workplace, and community-based interventions co-designed by the people they should serve; decriminalisation of suicide and restriction of access to lethal means; stigma reduction; reduction of income, gender, and racial inequality; and increased investment. The time to act is now, to rebuild health-care systems, leverage changes in funding landscapes, and address the effects of stigma, discrimination, marginalisation, gender violence, and victimisation.

Primary care physician use of patient race and polygenic risk scores in medical decision-making

PURPOSE

The use of patient race in medicine is controversial for its potential either to exacerbate or address health disparities. Polygenic risk scores (PRSs) have emerged as a tool for risk stratification models used in preventive medicine. We examined whether PRS results affect primary care physician (PCP) medical decision-making and whether that effect varies by patient race.

METHODS

Using an online survey with a randomized experimental design among PCPs in a national database, we ascertained decision-making around atherosclerotic cardiovascular disease prevention and prostate cancer screening for case scenario patients who were clinically identical except for randomized reported race.

RESULTS

Across 369 PCPs (email open rate = 10.8%, partial completion rate = 93.7%), recommendations varied with PRS results in expected directions (low-risk results, no available PRS results, and high-risk results). Still, physicians randomized to scenarios with Black patients were more likely to recommend statin therapy than those randomized to scenarios with White patients (odds ratio = 1.74, 95% CI = 1.16-2.59, P = .007) despite otherwise identical clinical profiles and independent of PRS results. Similarly, physicians were more likely to recommend prostate cancer screening for Black patients than for White patients (odds ratio = 1.58, 95% CI = 1.06-2.35, P = .025) despite otherwise identical clinical and genetic profiles.

CONCLUSION

Despite advances in precision risk stratification, physicians will likely continue to use patient race implicitly or explicitly in medical decision-making.

Validation of a Genetic-Enhanced Risk Prediction Model for Colorectal Cancer in a Large Community-Based Cohort

BACKGROUND

Polygenic risk scores (PRS) which summarize individuals' genetic risk profile may enhance targeted colorectal cancer screening. A critical step towards clinical implementation is rigorous external validations in large community-based cohorts. This study externally validated a PRS-enhanced colorectal cancer risk model comprising 140 known colorectal cancer loci to provide a comprehensive assessment on prediction performance.

METHODS

The model was developed using 20,338 individuals and externally validated in a community-based cohort (n = 85,221). We validated predicted 5-year absolute colorectal cancer risk, including calibration using expected-to-observed case ratios (E/O) and calibration plots, and discriminatory accuracy using time-dependent AUC. The PRS-related improvement in AUC, sensitivity and specificity were assessed in individuals of age 45 to 74 years (screening-eligible age group) and 40 to 49 years with no endoscopy history (younger-age group).

RESULTS

In European-ancestral individuals, the predicted 5-year risk calibrated well [E/O = 1.01; 95% confidence interval (CI), 0.91-1.13] and had high discriminatory accuracy (AUC = 0.73; 95% CI, 0.71-0.76). Adding the PRS to a model with age, sex, family and endoscopy history improved the 5-year AUC by 0.06 (P < 0.001) and 0.14 (P = 0.05) in the screening-eligible age and younger-age groups, respectively. Using a risk-threshold of 5-year SEER colorectal cancer incidence rate at age 50 years, adding the PRS had a similar sensitivity but improved the specificity by 11% (P < 0.001) in the screening-eligible age group. In the younger-age group it improved the sensitivity by 27% (P = 0.04) with similar specificity.

CONCLUSIONS

The proposed PRS-enhanced model provides a well-calibrated 5-year colorectal cancer risk prediction and improves discriminatory accuracy in the external cohort.

IMPACT

The proposed model has potential utility in risk-stratified colorectal cancer prevention.

Human Genetics Society of Australasia Position Statement: Use of Polygenic Scores in Clinical Practice and Population Health

Considerable progress continues to be made with regards to the value and use of disease associated polygenic scores (PGS). PGS aim to capture a person's genetic liability to a condition, disease, or a trait, combining information across many risk variants and incorporating their effect sizes. They are already available for clinicians and consumers to order in Australasia. However, debate is ongoing over the readiness of this information for integration into clinical practice and population health. This position statement provides the viewpoint of the Human Genetics Society of Australasia (HGSA) regarding the clinical application of disease-associated PGS in both individual patients and population health. The statement details how PGS are calculated, highlights their breadth of possible application, and examines their current challenges and limitations. We consider fundamental lessons from Mendelian genetics and their continuing relevance to PGS, while also acknowledging the distinct elements of PGS. Use of PGS in practice should be evidence based, and the evidence for the associated benefit, while rapidly emerging, remains limited. Given that clinicians and consumers can already order PGS, their current limitations and key issues warrant consideration. PGS can be developed for most complex conditions and traits and can be used across multiple clinical settings and for population health. The HGSA's view is that further evaluation, including regulatory, implementation and health system evaluation are required before PGS can be routinely implemented in the Australasian healthcare system.

Pathophysiology of Early Brain Injury and Its Association with Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage: A Review of Current Literature

Background: Delayed cerebral ischemia (DCI) is a common and serious complication of aneurysmal subarachnoid hemorrhage (aSAH). Though many clinical trials have looked at therapies for DCI and vasospasm in aSAH, along with reducing rebleeding risks, none have led to improving outcomes in this patient population. We present an up-to-date review of the pathophysiology of DCI and its association with early brain injury (EBI). Recent Findings: Recent studies have demonstrated that EBI, as opposed to delayed brain injury, is the main contributor to downstream pathophysiological mechanisms that play a role in the development of DCI. New predictive models, including advanced monitoring and neuroimaging techniques, can help detect EBI and improve the clinical management of aSAH patients. Summary: EBI, the severity of subarachnoid hemorrhage, and physiological/imaging markers can serve as indicators for potential early therapeutics in aSAH. The microcellular milieu and hemodynamic pathomechanisms should remain a focus of researchers and clinicians. With the advancement in understanding the pathophysiology of DCI, we are hopeful that we will make strides toward better outcomes for this unique patient population.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Downregulation of Circulating Hsa-miR-200c-3p Correlates with Dyslipidemia in Patients with Stable Coronary Artery Disease

Coronary heart disease (CHD), one of the leading causes of disability and death worldwide, is a multifactorial disease whose early diagnosis is demanding. Thus, biomarkers predicting the occurrence of this pathology are of great importance from a clinical and therapeutic standpoint. By means of a pilot study on peripheral blood cells (PBMCs) of subjects with no coronary lesions (CTR; n = 2) and patients with stable CAD (CAD; n = 2), we revealed 61 differentially methylated regions (DMRs) (18 promoter regions, 24 genes and 19 CpG islands) and 14.997 differentially methylated single CpG sites (DMCs) in CAD patients. MiRNA-seq results displayed a peculiar miRNAs profile in CAD patients with 18 upregulated and 32 downregulated miRNAs (FC ≥ ±1.5, p ≤ 0.05). An integrated analysis of genome-wide DNA methylation and miRNA-seq results indicated a significant downregulation of hsa-miR-200c-3p (FCCAD = −2.97, p ≤ 0.05) associated to the hypermethylation of two sites (genomic coordinates: chr12:7073122-7073122 and chr12:7072599-7072599) located intragenic to the miR-200c/141 genomic locus (encoding hsa-miR-200c-3p) (p-value = 0.009) in CAD patients. We extended the hsa-miR-200c-3p expression study in a larger cohort (CAD = 72, CTR = 24), confirming its reduced expression level in CAD patients (FCCAD = −2; p = 0.02). However, when we analyzed the methylation status of the two CpG sites in the same cohort, we failed to identify significant differences. A ROC curve analysis showed good performance of hsa-miR-200c-3p expression level (AUC = 0.65; p = 0.02) in distinguishing CAD from CTR. Moreover, we found a significant positive correlation between hsa-miR-200c-3p expression and creatinine clearance (R2 = 0.212, p < 0.005, Pearson r = 0.461) in CAD patients. Finally, a phenotypic correlation performed in the CAD group revealed lower hsa-miR-200c-3p expression levels in CAD patients affected by dyslipidemia (+DLP, n = 58) (p < 0.01). These results indicate hsa-miR-200c-3p as potential epi-biomarker for the diagnosis and clinical progression of CAD and highlight the importance of deeper studies on the expression of this miRNA to understand its functional role in coronary artery disease development.

Genetic Pathways Underlying Individual Differences in Regular Physical Activity

Twin and family studies show a strong contribution of genetic factors to physical activity (PA) assessed by either self-report or accelerometers. PA heritability is around 43% across the lifespan. Genome-wide association studies have implied biological pathways related to exercise ability and enjoyment. A polygenic score based on genetic variants influencing PA could help improve the success of intervention programs.

Association studies between chromosomal regions 1q21.3, 5q21.3, 14q21.2 and 17q21.31 and numbers of children in Poland

Number of children is an important human trait and studies have indicated associations with single-nucleotide polymorphisms (SNPs). Aim: to give further evidence for four associations using a large sample of Polish subjects. Data from the POPULOUS genetic database was provided from anonymous, healthy, unrelated, Polish volunteers of both sexes (N = 5760). SNPs (n = 173) studied: (a) 69 from the chromosome 17 H1/H2 inversion; (b) six from 1q21.3, 5q21.3 and 14q21.2; and (c) 98 random negative controls. Zero-inflated negative-binomial regression (z.i.) was performed (0-3 numbers of children per individual (NCI) set as non-events; adjustors: year of birth, sex). Significance level p = 0.05 with Bonferroni correction. Statistically-significant differences (with data from both sexes combined) were obtained from highly-linked inversion SNPs: representative rs12373123 gave means: homozygotes TT: 2.31 NCI (n = 1418); heterozygotes CT: 2.35 NCI (n = 554); homozygotes CC: 2.44 NCI (n = 43) (genotype p = 0.01; TTvs.CC p = 0.004; CTvs.CC p = 0.009). (Male data alone gave similar results.) Recessive modeling indicated that H2-homozygotes had 0.118 more children than H1-homozygotes + heterozygotes (z.i.-count estimates ± standard errors: CT, - 0.508 ± 0.194; TT, - 0.557 ± 0.191). The non-over-dispersed count model detected no interactions: of importance there was no significant interaction with age. No positive results were obtained from negative-control SNPs or (b). Conclusions: association between the H1/H2 inversion and numbers of children (previously reported in Iceland) has been confirmed, albeit using a different statistical model. One limitation is the small amount of data, despite initially ~ 6000 subjects. Causal studies require further investigation.

Communicating Personal Melanoma Polygenic Risk Information: Participants’ Experiences of Genetic Counseling in a Community-Based Study

Personalized polygenic risk information may be used to guide risk-based melanoma prevention and early detection at a population scale, but research on communicating this information is limited. This mixed-methods study aimed to assess the acceptability of a genetic counselor (GC) phone call in communicating polygenic risk information in the Melanoma Genomics Managing Your Risk randomized controlled trial. Participants (n = 509) received personalized melanoma polygenic risk information, an educational booklet on melanoma prevention, and a GC phone call, which was audio-recorded. Participants completed the Genetic Counseling Satisfaction Survey 1-month after receiving their risk information (n = 346). A subgroup took part in a qualitative interview post-study completion (n = 20). Survey data were analyzed descriptively using SPSS, and thematic analysis of the qualitative data was conducted using NVivo 12.0 software. The survey showed a high level of acceptability for the GC phone call (mean satisfaction score overall: 4.3 out of 5, standard deviation (SD): 0.6) with differences according to gender (mean score for women: 4.4, SD: 0.6 vs. men: 4.2, SD: 0.7; p = 0.005), health literacy (lower literacy: 4.1, SD: 0.8; average: 4.3, SD: 0.6; higher: 4.4, SD: 0.6: p = 0.02) and polygenic risk group (low risk: 4.5, SD: 0.5, SD: average: 4.3, SD: 0.7, high: 4.3, SD: 0.7; p = 0.03). During the GC phone calls, the discussion predominately related to the impact of past sun exposure on personal melanoma risk. Together our findings point to the importance of further exploring educational and support needs and preferences for communicating personalized melanoma risk among population subgroups, including diverse literacy levels.

A Systematic Review of Polygenic Models for Predicting Drug Outcomes

Polygenic models have emerged as promising prediction tools for the prediction of complex traits. Currently, the majority of polygenic models are developed in the context of predicting disease risk, but polygenic models may also prove useful in predicting drug outcomes. This study sought to understand how polygenic models incorporating pharmacogenetic variants are being used in the prediction of drug outcomes. A systematic review was conducted with the aim of gaining insights into the methods used to construct polygenic models, as well as their performance in drug outcome prediction. The search uncovered 89 papers that incorporated pharmacogenetic variants in the development of polygenic models. It was found that the most common polygenic models were constructed for drug dosing predictions in anticoagulant therapies (n = 27). While nearly all studies found a significant association with their polygenic model and the investigated drug outcome (93.3%), less than half (47.2%) compared the performance of the polygenic model against clinical predictors, and even fewer (40.4%) sought to validate model predictions in an independent cohort. Additionally, the heterogeneity of reported performance measures makes the comparison of models across studies challenging. These findings highlight key considerations for future work in developing polygenic models in pharmacogenomic research.

Translational impact of omics studies in alopecia areata: recent advances and future perspectives

INTRODUCTION

Alopecia areata (AA) is a non-scarring, hair loss disorder and a common autoimmune-mediated disease with an estimated lifetime risk of about 2%. To date, the treatment of AA is mainly based on suppression or stimulation of the immune response. Genomics and transcriptomics studies generated important insights into the underlying pathophysiology, enabled discovery of molecular disease signatures, which were used in some of the recent clinical trials to monitor drug response and substantiated the consideration of new therapeutic modalities for the treatment of AA such as abatacept, dupilumab, ustekinumab and Janus Kinase (JAK) inhibitors.

AREAS COVERED

In this review, genomics and transcriptomics studies in AA are discussed in detail with particular emphasis on their past and prospective translational impacts. Microbiome studies are also briefly introduced.

EXPERT OPINION

The generation of large datasets using the new high-throughput technologies has revolutionized medical research and AA has also benefited from the wave of omics studies. However, the limitations associated with JAK inhibitors and clinical heterogeneity in AA patients underscore the necessity for continuing omics research in AA for discovery of novel therapeutic modalities and development of clinical tools for precision medicine.

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.

Stability of polygenic scores across discovery genome-wide association studies

Polygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.