Clinical Genomics

Key Takeaways:

• Regulatory elements in non-coding DNA regions play critical roles in controlling gene expression and biological processes.
• Misregulation of enhancers, promoters, and other non-coding elements contributes to the pathogenesis of various diseases, including rheumatoid arthritis, coronary artery disease, and melanoma.
• Integrating whole genome sequencing with clinical phenotypic information from electronic health records can uncover new insights into disease mechanisms driven by non-coding variants.
• Elucidating the roles of regulatory elements will facilitate development of more precise therapeutics and diagnostics tailored to individuals.

The quest to decipher the intricate underpinnings of human disease has long fixated on protein-coding genes. However, a rapidly burgeoning field is shining a spotlight on the proverbial dark matter of the genome – the vast, enigmatic expanse of non-coding DNA regions harboring regulatory elements that orchestrate gene expression. Enhancers, promoters, insulators, and other regulatory sequences exert exquisite control over when, where, and to what extent genes are expressed, shaping the symphony of cellular processes that sustain life. Yet, when these regulatory elements go awry, the consequences can be catastrophic, manifesting as devastating diseases that have long confounded researchers and clinicians alike.

Rheumatoid Arthritis and Non-Coding Dysregulation 

Rheumatoid arthritis (RA), characterized by excruciating joint inflammation and debilitating pain, exemplifies the profound impact of dysregulated non-coding elements. Enhancers and promoters governing the expression of pivotal inflammatory genes, such as those encoding the cytokines TNF-alpha and IL-6, have been implicated in the pathogenesis of RA. Mutations or epigenetic modifications in these regulatory regions can trigger aberrant cytokine production, fueling the vicious cycle of autoimmune attack and joint destruction that defines this debilitating condition.

Coronary Artery Disease and Lipid Metabolism 

Another striking example is coronary artery disease (CAD), a leading cause of mortality worldwide. The insidious buildup of atherosclerotic plaques within the coronary arteries, narrowing these vital conduits and heightening the risk of heart attacks, is intimately linked to disruptions in lipid metabolism and inflammation. Regulatory elements controlling the expression of genes such as PCSK9, a master regulator of cholesterol homeostasis, have been implicated in CAD etiology. Variants within these non-coding regions can dysregulate PCSK9, precipitating dyslipidemia and promoting the relentless progression of atherosclerosis.

Melanoma and Telomerase Regulation 

Melanoma, a deadly form of skin cancer arising from pigment-producing melanocytes, further underscores the profound influence of non-coding regulatory aberrations. Researchers have identified mutations in the TERT gene’s promoter region in a significant proportion of melanoma cases. These mutations lead to enhanced telomerase activity, conferring cellular immortality and fueling the unchecked proliferation that characterizes this aggressive malignancy.

The Tip of the Iceberg 

While these examples illuminate the pivotal roles of non-coding regulatory elements in disease pathogenesis, they represent merely the tip of the iceberg. The majority of disease-associated variants identified through genome-wide association studies reside within non-coding regions, and their functional implications are largely unexplored. However, interpreting the functional significance of these complex regions is challenging due to their diverse roles and interactions. This underscores the pressing need to delve deeper into the intricate regulatory landscapes governing gene expression and to elucidate how non-coding variants contribute to disease susceptibility, progression, and severity.

Harnessing Genomics and Clinical Data 

Fortunately, the advent of transformative technologies is ushering in a new era of scientific discovery. For example, integrating whole genome sequencing data with clinical phenotypic information from electronic health records (EHRs) provides the means to uncover insights into disease mechanisms driven by non-coding variants.

Envisioning a Future of Precision Medicine 

Imagine a future where an individual’s genomic information, meticulously annotated with regulatory element annotations, is seamlessly integrated with their EHR to capture a rich tapestry of clinical manifestations, treatment responses, and diagnostic imaging data. This convergence of multimodal data streams would empower researchers and clinicians to unravel the intricate interplay between non-coding regulatory variants and disease phenotypes, illuminating novel etiological factors that have long evaded detection.

Transformative Diagnostics and Therapeutics 

By decoding the enigmatic language of non-coding regulatory elements and their misregulation in disease, we stand to gain profound insights that will catalyze the development of groundbreaking diagnostics and therapeutics. For instance, personalized genetic screens could assess an individual’s risk of developing specific diseases based on their unique constellation of non-coding variants and enable early intervention. Similarly, tailored therapeutic interventions that precisely modulate the activity of dysregulated enhancers or promoters could restore homeostasis and alleviate disease burden, offering more effective and targeted treatments.

Interdisciplinary Collaboration 

As we embark on this exhilarating journey, fostering collaborations between geneticists, bioinformaticians, clinicians, and pharmaceutical partners will be paramount. By aligning interdisciplinary expertise and leveraging cutting-edge technologies, we can unlock the transformative potential of non-coding regulatory elements, ushering in a new era of precision medicine that promises to redefine our understanding and treatment of human disease.

Sources:

1. Hnisz, D., et al. (2013). Super-enhancers in the control of cell identity and disease. Cell, 155(4), 934-947.
2. Tak, P. P., & Firestein, G. S. (2001). NF-κB: a key role in inflammatory diseases. The Journal of clinical investigation, 107(1), 7-11.
3. Abifadel, M., et al. (2003). Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature genetics, 34(2), 154-156.
4. Huang, F. W., et al. (2013). Highly recurrent TERT promoter mutations in human melanoma. Science, 339(6122), 957-959.
5. Maurano, M. T., et al. (2012). Systematic localization of common disease-associated variation in regulatory DNA. Science, 337(6099), 1190-1195.

Key Takeaways:

• Human genetics research can elucidate mechanisms of disease and help identify new drug targets.
• Studying genetic variants linked to disease risk or drug response helps stratify patients and inform clinical trials.
• Genomic data enables the development of precision medicines targeted to patients’ genetic profiles.
• Pharmocogenomics and genetic screening guides optimal drug usage and minimizes adverse reactions.
• Advancements in genetic analysis technologies are enabling more rapid and expansive use of genomic data in drug R&D.

The Value of Human Genetics in Drug R&D

Developing new drugs is a lengthy and expensive process with a high failure rate. On average, it takes 10-15 years and over $1 billion to bring a new drug to market. The pharmaceutical industry is looking to human genetics research to improve R&D efficiency, success rates and the personalized utility of new medicines.

Understanding the genetic factors underlying diseases can point the way to new drug targets. Identifying genetic variants linked to disease risk helps elucidate biological pathways involved. Druggable targets can then be identified to modulate relevant pathways and processes. Genetics also helps establish causal mechanisms to avoid spurious associations.

Pharmacogenomics focuses on how genetic variability affects drug response. It enables matching patients to treatments according to genotype to maximize effectiveness and avoid adverse reactions. Testing for pharmacogenomic biomarkers can guide dosing, or indicate alternate treatments when genetics point to likely non-response.

Genetic screening also aids patient stratification and clinical trial optimization. Enriching trial participant selection for those most likely to respond or exhibit a clinical effect improves statistical power with smaller sample sizes. Genetic variables allow better control for confounding factors. Pharmacogenomic testing of participants also helps explain differential responses.

Studying rare genetic variants with large effects (“genetic supermodels”) provides another window into disease biology. The study of extreme genotypes helps unravel mechanisms and identify new targets.

Once a drug is developed, genetics continues to inform optimal use. Screening programs using pharmacogenomic biomarkers guide treatment choices and minimize risks. Genetics also aids mechanistic understanding of how therapies work, illuminating additional applications and opportunities.

The plummeting costs of genome sequencing and advances in big data analytics are enabling more extensive use of human genetic data. Pete Hulick, lead for molecular biology at Eli Lilly, described human genetics as “intersecting with everything that we do” in drug R&D.

Applications in Discovery Research

Early in the R&D process, human genetic insights can point the way to promising disease targets. Scientists look for associations between genetic variants, such as single nucleotide polymorphisms (SNPs), and disease risk. Genome-wide association studies (GWAS) uncover SNP differences between disease and control cohorts. Significant associations indicate genes and biological pathways involved in the disease that may be amenable to pharmacological intervention.

Once potential targets are identified, downstream lab research explores how to modulate them. Developing a drug is an iterative process, but human genetics provides clues on where to start.

Genetics also offers validation when biological hypotheses emerge from other experiments. Confirming that tweaking a gene or pathway affects disease risk strengthens the case for pursuing it as a drug target.

Patient Stratification & Clinical Trials

Patient heterogeneity is a major obstacle in clinical trials. Varied treatment responses lower statistical power and necessitate larger trial sizes. Genetic analysis enables better patient stratification to minimize heterogeneity and identify relevant subgroups.

For example, the cystic fibrosis drug Kalydeco works for patients with a particular CFTR gene mutation. Prescreening patients’ genetics enables targeted trial recruitment. Similar approaches minimize heterogeneity in cancer trials by selecting patients with tumors exhibiting specific mutations.

Genotyping trial participants helps explain differential responses and may uncover additional genotype-specific effects. Genetic associations can also point to new indications for the drug mechanism.

Precision Medicine

The emergence of targeted precision therapies relies directly on human genetics. Cancer treatments like Herceptin and Gleevec target tumors with specific genomic variants. HIV drugs are tailored to individual viral genotypes. Gene therapies introduce corrected genes to compensate for defective inherited genes.

This personalized approach promises greater efficacy for those most likely to respond. By targeting drugs based on genetic profiles, precision medicine seeks to maximize benefit while minimizing unnecessary treatment.

Pharmacogenomics for Safety & Optimization

Pharmacogenomic testing assesses how genetic variability affects reactions to drugs. It can identify patients likely to experience adverse events or suboptimal responses. This enables selecting safer treatments, dosage adjustment or more intense monitoring.

The blood thinner warfarin, for example, demonstrates significant pharmacogenomic effects. Genotyping helps guide ideal dosing to balance effectiveness and bleeding risks. The FDA added pharmacogenomic guidance on warfarin labeling in 2007.

Wider adoption of pharmacogenomic testing has the potential to reduce adverse drug events that represent a significant public health burden. More optimal treatment through genetic guidance also contributes to pharmacoeconomic goals.

Looking Ahead

The expanding use of human genetics is transforming every phase of drug R&D. While challenges remain in interpreting and applying genetic findings, the value in accelerating discovery, precision medicine and optimized therapeutics is evident. Advances in high-throughput genomics, big data analytics    and machine learning will further incorporate human genetics into tomorrow’s medicines.

Sources:

• Relling & Evans, Nature Reviews Drug Discovery 2015
• Roden & Denny, Annual Review of Medicine 2019
• Genomics England PanelApp Pharmacogenetics Gene Curation Group,NPJ Genomic Medicine 2020
• Li et al., Nature Reviews Genetics 2020
• Manolio et al., JAMA 2020
• Xu et al., Nature Reviews Drug Discovery 2022

Key Takeaways:

• Pharmaceutical development is high-risk and resource-intensive, with a 90% failure rate in clinical trials, often due to inadequate efficacy, toxicity, drug properties, or commercial viability.
• Incorporating human genetic evidence doubles drug approval rates, paving the way for innovative therapies and new molecular entities.
• Techniques like GWASs and PheWAS linking genetic data to phenotypic data enhance drug development by identifying associations between rare alleles and diseases.
• Published human genetic studies, primarily centered on individuals of European descent, hinder our understanding of genetic diversity and impede the development of new therapies suitable for diverse populations; therefore, establishing study cohorts with under-represented populations is crucial for promoting health equality and identifying novel drug targets based on diverse genetic variants.
• The Alliance for Genomic Discovery (AGD) aims to reshape drug development by sequencing 250,000 diverse samples, providing a powerful resource for pharmaceutical members to correlate genetic variations with clinical outcomes and, in turn, enabling these companies to better serve a global population.

The Struggle to Discover New Therapies

Discovering and developing pharmaceuticals is a resource-intensive and high-risk endeavor, sometimes spanning 15 years with costs exceeding $2 billion for their approval (Hinkson et al., 2020). Shockingly, about nine out of ten potential therapies, upon progressing to clinical trials, fail before approval (Dowden & Munro, 2019; Sun et al., 2022). The four primary contributors to the staggering 90% failure rate in drug development are inadequate clinical efficacy, unmanageable toxicity, suboptimal drug-like properties and a lack of commercial viability (Dowden & Munro, 2019; Harrison, 2016; Sun et al., 2022). To increase the chances of a drug target passing these critical checkpoints, considerable endeavors can be directed towards incorporating human genetic evidence into drug development.

In the drug development pipeline, all compounds before entering clinical phases must undergo rigorous testing in animal models, providing significant evidence of their potential to treat diseases. However, despite promising results in preclinical studies, the translation of efficacy and safety from animal models to human clinical trials is often elusive. Integrating human genetic evidence into the drug development process has recently emerged as a crucial strategy to navigate this challenge. Drugs grounded in such evidence exhibit a twofold increase in approval rates (Nelson et al., 2015), contributing to a higher prevalence of first-in-class therapies and new molecular entities (NMEs) (King et al., 2019). This not only accelerates the approval process but also streamlines the discovery of more effective and targeted treatments. Leveraging human genetic data empowers researchers with valuable insights into the genetic basis of diseases, facilitating the identification of better drug targets. The substantial presence of genetic evidence in FDA-approved drugs in 2021 (Ochoa et al., 2022) underscores its instrumental role in advancing drug discovery and fostering the emergence of innovative pharmaceutical solutions.

Linking Genetics to Clinical Data for Drug Discovery

To incorporate genetics into therapeutic development, researchers can link the genetic code of an individual to their Electronic Health Records (EHRs). Researchers can use techniques like Genome-wide association studies (GWASs), Phenome-wide association studies (PheWAS), Mendelian Randomization or Loss/Gain-of-Function Variants to discover associations between rare alleles and human disease (Krebs & Milani, 2023). Using these techniques, drugs tailored for Mendelian disorders have achieved notable success in clinical trials and approvals (Heilbron et al., 2021). For instance, the genetic disease Autosomal dominant hypercholesterolemia (ADH) confers an increased risk of coronary artery disease (CAD) through elevated levels of plasmatic low-density lipoprotein (LDL). By linking phenotypic data with genetic data, researchers were able to identify the association of the PCSK9 gene with high LDL levels (Abifadel et al., 2003). This kickstarted a series of studies that culminated in the approval of two monoclonal antibodies that inhibit PCSK9, Repatha (Evolocumab) and Praluent (Alirocumab) (Krebs & Milani, 2023; Robinson et al., 2015) with their treatment reducing the rate of major adverse cardiovascular events by half (Kaddoura et al., 2020). Indeed, therapies derived from these kinds of impactful rare alleles exhibit a 6-7.2 times greater likelihood of receiving approval due to their substantial effect on symptoms (Nelson et al., 2015; King et al., 2019). However, for many prevalent diseases, heritable risk is predominantly associated with numerous common variants, each having smaller individual effect sizes. This intricate genetic landscape complicates the identification of therapeutic targets, making the discovery of new avenues for therapy challenging and necessitating new strategies.

So far, a disproportionate number of published human genetic studies have centered on individuals of European descent (Fatumo et al., 2022). However, this narrow focus restricts our understanding to a limited diversity of alleles and genetic disorders, hindering the development of new therapies. To promote health equality, it’s crucial to establish study cohorts that include under‐represented populations. After all, individuals of European descent represent only a fraction of the total human genetic variation (Heilbron et al., 2021). Diverse cohorts represent unique opportunities for identifying novel drug targets based on genetic variants that are less frequent or even absent in people of European ancestry. Genetic discoveries will have greater discovery power in populations where a disease is more prevalent and, hence, with larger disease cohorts; at the same time, these discoveries will be more relevant and beneficial for these populations.

Founding the Alliance for Genomic Discovery

This need to identify rare genetic variants in diverse patient cohorts has driven the collaboration of NashBio and Illumina Inc. to establish AGD. AGD, comprising eight member organizations—AbbVie, Amgen, AstraZeneca, Bayer, Merck, Bristol Myers Squibb (BMS), GlaxoSmithKline Pharmaceuticals (GSK), and Novo Nordisk (Novo)—aims to expedite therapeutic development through whole-genome sequencing (WGS) 250,000 samples from Vanderbilt University Medical Center’s (VUMC) biobank repository, BioVU®. As the first phase in AGD, deCODE genetics performed WGS on the first 35,000 VUMC samples, primarily made up of DNA from individuals of African ancestry. Moving forward, deCODE/Amgen will sequence the remaining samples for the Alliance members to have access to the resulting data for drug discovery and therapeutic development. The WGS data will then be linked with structured EHR data from NashBio and VUMC, creating a valuable resource for pharmaceutical members to correlate genetic variations with clinical outcomes. To learn more about how AGD aims to accelerate drug discovery and to hear directly from the alliance members, click here.

Summary

AGD marks a pivotal step in reshaping drug development, offering a solution to the challenges plaguing the pharmaceutical industry. With a staggering 90% failure rate in clinical trials, the incorporation of human genetic evidence into drug development by AGD aims to increase the approval likelihood of drug targets, fostering the discovery of more effective and targeted treatments. AGD also aims to address the limitations of existing genetic resources and studies. The WGS of 250,000 samples, encompassing diverse populations and linked with structured EHR data, provides pharmaceutical members with a powerful resource. This not only accelerates drug discovery but also facilitates the development of tailored therapies. AGD represents a significant step toward healthcare equality, highlighting the importance of diverse genetic studies in progressing drug discovery for the benefit of all people.

References

Abifadel, M., Varret, M., Rabès, J.-P., Allard, D., Ouguerram, K., Devillers, M., Cruaud, C., Benjannet, S., Wickham, L., Erlich, D., Derré, A., Villéger, L., Farnier, M., Beucler, I., Bruckert, E., Chambaz, J., Chanu, B., Lecerf, J.-M., Luc, G., … Boileau, C. (2003). Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genetics, 34(2), 154–156. https://doi.org/10.1038/ng1161

Dowden, H., & Munro, J. (2019). Trends in clinical success rates and therapeutic focus. Nature Reviews. Drug Discovery, 18(7), 495–496. https://doi.org/10.1038/d41573-019-00074-z

Fatumo, S., Chikowore, T., Choudhury, A., Ayub, M., Martin, A. R., & Kuchenbaecker, K. (2022). A roadmap to increase diversity in genomic studies. Nature Medicine, 28(2), 243–250. https://doi.org/10.1038/s41591-021-01672-4

Harrison, R. K. (2016). Phase II and phase III failures: 2013-2015. Nature Reviews. Drug Discovery, 15(12), 817–818. https://doi.org/10.1038/nrd.2016.184

Heilbron, K., Mozaffari, S. V, Vacic, V., Yue, P., Wang, W., Shi, J., Jubb, A. M., Pitts, S. J., & Wang, X. (2021). Advancing drug discovery using the power of the human genome. The Journal of Pathology, 254(4), 418–429. https://doi.org/10.1002/path.5664

Hinkson, I. V., Madej, B., & Stahlberg, E. A. (2020). Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery. Frontiers in Pharmacology, 11. https://doi.org/10.3389/fphar.2020.00770

Kaddoura, R., Orabi, B., & Salam, A. M. (2020). PCSK9 Monoclonal Antibodies: An Overview. Heart Views : The Official Journal of the Gulf Heart Association, 21(2), 97–103. https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_20_20

King, E. A., Davis, J. W., & Degner, J. F. (2019). Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLOS Genetics, 15(12), e1008489. https://doi.org/10.1371/journal.pgen.1008489

Krebs, K., & Milani, L. (2023). Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery. Annual Review of Pharmacology and Toxicology, 63(1), 65–76. https://doi.org/10.1146/annurev-pharmtox-051421-111324

Nelson, M. R., Tipney, H., Painter, J. L., Shen, J., Nicoletti, P., Shen, Y., Floratos, A., Sham, P. C., Li, M. J., Wang, J., Cardon, L. R., Whittaker, J. C., & Sanseau, P. (2015). The support of human genetic evidence for approved drug indications. Nature Genetics, 47(8), 856–860. https://doi.org/10.1038/ng.3314

Ochoa, D., Karim, M., Ghoussaini, M., Hulcoop, D. G., McDonagh, E. M., & Dunham, I. (2022). Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nature Reviews. Drug Discovery, 21(8), 551. https://doi.org/10.1038/d41573-022-00120-3

Robinson, J. G., Farnier, M., Krempf, M., Bergeron, J., Luc, G., Averna, M., Stroes, E. S., Langslet, G., Raal, F. J., El Shahawy, M., Koren, M. J., Lepor, N. E., Lorenzato, C., Pordy, R., Chaudhari, U., & Kastelein, J. J. P. (2015). Efficacy and Safety of Alirocumab in Reducing Lipids and Cardiovascular Events. New England Journal of Medicine, 372(16), 1489–1499. https://doi.org/10.1056/NEJMoa1501031

Sun, D., Gao, W., Hu, H., & Zhou, S. (2022). Why 90% of clinical drug development fails and how to improve it? Acta Pharmaceutica Sinica. B, 12(7), 3049–3062. https://doi.org/10.1016/j.apsb.2022.02.002

Introduction

We were promised the end to genetic diseases. All we needed to do was unlock the human genome. Unfortunately, life has a way of being more complicated than we expect. It turned out that many genetic disorders are the result of the interplay between multiple genetic factors. This set off the need for improved analytical tools to analyze human genetics that could interrogate the associations of many genetic backgrounds and link them to various diseases. One such technique, the Polygenic Risk Score (PRS), emerged as a powerful tool to quantify the cumulative effects of multiple genetic variants on an individual’s predisposition to a specific disease.

The Evolution of Polygenic Risk Scores

The genesis of PRS can be traced back to the early 2000s when researchers sought to comprehend the collective impact of multiple genetic variants on disease susceptibility. Initially viewed through a biological lens, the focus was on enhancing the prediction of diseases by analyzing subtle genomic variations. Studies concentrated on prevalent yet complex diseases such as diabetes, cardiovascular diseases, and cancer, laying the groundwork for a comprehensive understanding of their genetic architecture.

That was until Dr. Sekar Kathiresan showed that the prediction from a PRS was just as clinically useful as a single variant (Khera et al., 2018). Instead of looking at the percent of people with a PRS in each group (with or without a disease), his group could show a much more obvious effect – the difference in risk for people in the groups with the highest and lowest scores. Then, they could say that there was a huge difference in risk for these two edges of the population.

In the initial stages, PRSs consisted of only the most statistically significant variants from genome-wide association studies. Geneticists often added up the quantity of risk variants without giving them a weight for how much of an impact they had on whether someone would get a disease. Refining these scores led scientists to challenge arbitrary risk cutoffs and advocate for the inclusion of all variants to maximize statistical power (based on the assumption that, on average, variants that have no effect are evenly distributed to appear positively or negatively correlated to the trait). However, proximity of variants on a chromosome presented another challenge. If variants were closer together on a chromosome, they would be less likely to be separated during recombination (Linkage Disequilibrium). This would result in them carrying the signal of something that had a true effect, potentially leading to an overcounting of that signal.

To deal with this, geneticists used tools to remove signals within a specified block unless their correlation with the strongest signal fell below a threshold. One of the first packages, PRSice (Choi & O’Reilly, 2019), used an approach called Pruning and Thresholding. Scientists would choose a block size, say, 200,000 base pairs. A program would go through and slide that block along the genome. If there was more than a single signal in that block, the program would remove (or “prune”) all but the strongest signal unless the variant had a smaller correlation with the strongest signal than the “threshold”. The result was that in a region with many different variants that affected the risk of a disease, but which were still a bit correlated, signal could be lost.

Criticism from biostatisticians prompted a shift towards a Bayesian approach, reducing over-counting while better accounting for partially independent signals. Implementation was challenged by the extensive computational resources needed to update the signal at each genetic location based on linkage disequilibrium of the surrounding SNPs. One program, called PRS-CS (Ge et al., 2019), implemented a method that could apply changes to a whole linkage block at once, addressing both the geneticist demand for a good system that can provide results using the computation tools we have and the biostatistician demand for accuracy and retained information.

Despite these advancements, accuracy challenges persisted, particularly when applying scoring systems across populations with different genetic ancestries. It turned out Linkage Disequilibrium was a pervasive problem. The patterns of Linkage Disequilibrium are different in people with different genetic ancestries. In fact, even statistics about the patterns themselves, like how big an average block size is, are different. Recognizing the need for improvement, ongoing efforts in refining PRSs aim to address these challenges, paving the way for more accurate and reliable applications. As researchers delve deeper into these complexities, the evolving landscape of PRSs continues to shape the future of clinical research.

Polygenic Risk Scores in Clinical Research Settings

To harness the full potential of PRS in clinical practice, a crucial shift is needed—from population-level insights to personalized predictions for individual patients. This transformation involves converting relative risks, which compare individuals across the PRS spectrum with a baseline group, into absolute risks for the specific disease (Lewis & Vassos, 2020). The current emphasis is on identifying individuals with a high genetic predisposition to disease, forming the foundation for effective risk stratification. This information guides decisions related to participation in screening programs, lifestyle modifications, or preventive treatments when deemed suitable.

In practical applications, PRS demonstrates promise in patient populations with a high likelihood of disease. Consider a recent study in an East Asian population, where researchers developed a PRS for Coronary Artery Disease (CAD) using 540 genetic variants (Lu et al., 2022). Tested on 41,271 individuals, the top 20% had a three-fold higher risk of CAD compared to the bottom 20%, with lifetime risks of 15.9% and 5.8%, respectively. Adding PRS to clinical risk assessment slightly improved accuracy. Notably, individuals with intermediate clinical risk and high PRS reached risk levels similar to high clinical risk individuals with intermediate PRS, indicating the potential of PRS to refine risk assessment and identify those requiring targeted interventions for CAD.

Another application of PRS lies in improving screening for individuals with major disease risk alleles (Roberts et al., 2023). A recent breast cancer risk assessment study explored pathogenic variants in high and moderate-risk genes (Gao et al., 2021). Over 95% of BRCA1, BRCA2, and PALB2 carriers had a lifetime breast cancer risk exceeding 20%. Conversely, integrating PRS identified over 30% of CHEK2 and almost half of ATM carriers below the 20% threshold. Indeed, a similar result was found in a separate study when researchers investigated men with high blood levels of prostate-specific antigen (PSA). 

This trend extends to other diseases, such as prostate cancer, where a separate investigation focused on men with elevated levels of prostate-specific antigen (PSA) (Shi et al., 2023). Through the application of PRS, researchers pinpointed over 100 genetic variations linked to increased PSA levels. Ordinarily, such elevated PSA levels would prompt prostate biopsies to assess potential prostate cancer. By incorporating PRS into the screening process, doctors could have accounted for the natural variation in PSA level and prevent unnecessary escalation of clinical care. These two studies suggest that PRS integration into health screening enhances accuracy, preventing unnecessary tests and enabling more personalized risk management.

In the realm of pharmacogenetics, efforts to optimize treatment responses continue. While progress has been made in identifying rare high-risk variants linked to adverse drug events, predicting treatment effectiveness remains challenging. The evolving role of PRS in treatment response is particularly evident in statin use for reducing initial coronary events. In a real-world cohort without prior myocardial infarction, an investigation revealed that statin effectiveness varied based on CHD PRSs, with the highest impact in the high-risk group, intermediate in the intermediate-risk group, and the smallest effect in the low-risk group (Oni-Orisan et al., 2022). Post-hoc analyses like this for therapeutics could potentially allow for more targeted enrollment for clinical trial design, substantially reducing the number of participants needed to demonstrate trial efficacy (Fahed et al., 2022).

Conclusion

As the field of genetics continues to advance, PRSs emerge as a potent tool with the potential to aid clinical research. Validated PRSs show promise in enhancing the design and execution of clinical trials, refining disease screening, and developing personalized treatment strategies to improve the overall health and well-being of patients. However, it’s crucial to acknowledge that the majority of PRS studies heavily rely on biased datasets of European ancestry. To refine and improve PRS, a comprehensive understanding of population genetic traits for people of all backgrounds, such as linkage disequilibrium, is essential. Moving forward, the integration of PRS into clinical applications must prioritize datasets with diverse ancestry to ensure equitable and effective utilization across all patient backgrounds. As research in this field progresses, the incorporation of PRS is poised to become an indispensable tool for expediting the development of safer and more efficacious therapeutics.

References

Choi, S. W., & O’Reilly, P. F. (2019). PRSice-2: Polygenic Risk Score software for biobank-scale data. GigaScience, 8(7). https://doi.org/10.1093/gigascience/giz082

Fahed, A. C., Philippakis, A. A., & Khera, A. V. (2022). The potential of polygenic scores to improve cost and efficiency of clinical trials. Nature Communications, 13(1), 2922. https://doi.org/10.1038/s41467-022-30675-z

Gao, C., Polley, E. C., Hart, S. N., Huang, H., Hu, C., Gnanaolivu, R., Lilyquist, J., Boddicker, N. J., Na, J., Ambrosone, C. B., Auer, P. L., Bernstein, L., Burnside, E. S., Eliassen, A. H., Gaudet, M. M., Haiman, C., Hunter, D. J., Jacobs, E. J., John, E. M., … Kraft, P. (2021). Risk of Breast Cancer Among Carriers of Pathogenic Variants in Breast Cancer Predisposition Genes Varies by Polygenic Risk Score. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, 39(23), 2564–2573. https://doi.org/10.1200/JCO.20.01992

Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A., & Smoller, J. W. (2019). Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nature Communications, 10(1), 1776. https://doi.org/10.1038/s41467-019-09718-5

Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., & Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics, 50(9), 1219–1224. https://doi.org/10.1038/s41588-018-0183-z

Lewis, C. M., & Vassos, E. (2020). Polygenic risk scores: from research tools to clinical instruments. Genome Medicine, 12(1), 44. https://doi.org/10.1186/s13073-020-00742-5

Lu, X., Liu, Z., Cui, Q., Liu, F., Li, J., Niu, X., Shen, C., Hu, D., Huang, K., Chen, J., Xing, X., Zhao, Y., Lu, F., Liu, X., Cao, J., Chen, S., Ma, H., Yu, L., Wu, X., … Gu, D. (2022). A polygenic risk score improves risk stratification of coronary artery disease: a large-scale prospective Chinese cohort study. European Heart Journal, 43(18), 1702–1711. https://doi.org/10.1093/eurheartj/ehac093

Oni-Orisan, A., Haldar, T., Cayabyab, M. A. S., Ranatunga, D. K., Hoffmann, T. J., Iribarren, C., Krauss, R. M., & Risch, N. (2022). Polygenic Risk Score and Statin Relative Risk Reduction for Primary Prevention of Myocardial Infarction in a Real-World Population. Clinical Pharmacology and Therapeutics, 112(5), 1070–1078. https://doi.org/10.1002/cpt.2715

Roberts, E., Howell, S., & Evans, D. G. (2023). Polygenic risk scores and breast cancer risk prediction. Breast (Edinburgh, Scotland), 67, 71–77. https://doi.org/10.1016/j.breast.2023.01.003

Shi, M., Shelley, J. P., Schaffer, K. R., Tosoian, J. J., Bagheri, M., Witte, J. S., Kachuri, L., & Mosley, J. D. (2023). Clinical consequences of a genetic predisposition toward higher benign prostate-specific antigen levels. EBioMedicine, 97, 104838. https://doi.org/10.1016/j.ebiom.2023.104838