Application Of Polygenic Risk Score Research Articles

Insights into the Inherited Basis of Valvular Heart Disease.

PURPOSEOF REVIEW: Increases in the availability of genetic data and advances in the tools and methods for their analyses have enabled well-powered genetic association studies that have significantly enhanced our understanding of the genetic factors underlying both rare and common valve diseases. Valvular heart diseases, such as congenital valve malformations and degenerative valve lesions, increase the risk of heart failure, arrhythmias, and sudden death. In this review, we provide an updated overview of our current understanding of the genetic mechanisms underlying valvular heart diseases. With a focus on discoveries from the past 5years, we describe recent insights into genetic risk and underlying biological pathways. RECENT FINDINGS: Recently acquired knowledge around valvular heart disease genetics has provided important insights into novel mechanisms related to disease pathogenesis. Newly identified risk loci associated valvular heart disease mainly regulate the composition of the extracellular matrix, accelerate the endothelial-to-mesenchymal transition, contribute to cilia formation processes, and play roles in lipid metabolism. Large-scale genomic analyses have identified numerous risk loci, genes, and biological pathways associated with degenerative valve disease and congenital valve malformations. Shared risk genes suggest common mechanistic pathways for various valve pathologies. More recent studies have combined cardiac magnetic resonance imaging and machine learning to offer a novel approach for exploring genotype-phenotype relationships regarding valve disease. Progress in the field holds promise for targeted prevention, particularly through the application of polygenic risk scores, and innovative therapies based on the biological mechanisms for predominant forms of valvular heart diseases.

Clinical Application of Polygenic Risk Score in IgA Nephropathy

Clinical Application of Polygenic Risk Score in IgA Nephropathy

Precision medicine in cardiovascular therapeutics: Evaluating the role of pharmacogenetic analysis prior to drug treatment.

Pharmacogenomics is the examination of how genetic variation influences drug metabolism and response, in terms of both efficacy and safety. In cardiovascular disease, patient-specific diplotypes determine phenotypes, thereby influencing the efficacy and safety of drug treatments, including statins, antiarrhythmics, anticoagulants and antiplatelets. Notably, polymorphisms in key genes, such as CYP2C9, CYP2C19, VKORC1 and SLCO1B1, significantly impact the outcomes of treatment with clopidogrel, warfarin and simvastatin. Furthermore, the CYP2C19 polymorphism influences the pharmacokinetics and safety of the novel hypertrophic cardiomyopathy inhibitor, mavacamten. In this review, we critically assess the clinical application of pharmacogenomics in cardiovascular disease and delineate present and future utilization of pharmacogenomics. This includes insights into identifying missing heritability, the integration of whole genome sequencing and the application of polygenic risk scores to enhance the precision of personalized drug therapy. Our discussion encompasses health economic analyses that underscore the cost benefits associated with pre-emptive genotyping for warfarin and clopidogrel treatments, albeit acknowledging the need for further research in this area. In summary, we contend that cardiovascular pharmacogenomic analyses are underpinned by a wealth of evidence, and implementation is already occurring for some of these gene-drug pairs, but as with any area of medicine, we need to continually gather more information to optimize the use of pharmacogenomics in clinical practice.

Validation of a breast cancer polygenic risk score in 15,490 Brazilians using exome sequencing.

10631 Background: Brazil has a highly admixed population with significant Southern European, African and Native American genetic ancestries. Approximately 75% of the population has two or more continental ancestries. Therefore, breast cancer polygenic risk scores (PRS) developed in predominantly European populations requires validation in the Brazilian population. Compared with genotyping arrays and whole genome sequencing, affordable and scalable whole exome sequencing (WES) allows genotyping of both rare and common variants in the targeted regions and imputation of common variants not directly captured. In this study we validate a previously developed 3820-variant PRS using WES in 15,490 Brazilians. Methods: The cohort consisted of 6,362 women with a history of breast cancer (mean age 49.6, stddev 11.6) and 9,128 unphenotyped adults as controls (mean age 41.6, stddev 13.3). WES were performed on germline DNA and variants were called and curated. Imputation was done using the 1000 Genomes Phase 3 reference panel for variants falling outside the WES captured regions. After excluding subjects with known pathogenic or likely pathogenic variants in BRCA1, BRCA2, PALB2, PTEN, or TP53, and first or second-degree relatives of the probands, 14,577 individuals remained in the final analysis (5,730 cases and 8,847 controls). We restricted PRS calculation to 2,893 of the original 3,820 variants with an estimated global imputation accuracy of 96%. Odds ratios (OR) were adjusted using the top 10 principal components. Results: The breast cancer PRS showed a highly significant correlation with cancer risk (p-value = 7.1e-81, OR = 1.43, 95% confidence interval (CI) = 1.38-1.48 per PRS standard deviation). Compared with women in the middle deciles, those in the highest 10% had an OR of 1.93, while those in the lowest 10% had an OR of 0.52 (Table). Conclusions: In this study we show that WES can capture 76% of the variants included in a previously developed and validated breast cancer PRS, and the calculated PRS is correlated with breast cancer risk in the admixed Brazilian population. This opens the door for clinical studies into the application of PRS in Brazil, particularly in women without mutations in breast cancer high-penetrance genes. In addition, our WES-based PRS strategy can be quickly, affordably, and seamlessly adopted by labs that already offer Hereditary Breast Cancer panels that use WES as a backbone. [Table: see text]

How do experts in psychiatric genetics view the clinical utility of polygenic risk scores for schizophrenia?

Polygenic risk scores (PRS) are promising for identifying common variant-related inheritance for psychiatric conditions but their integration into clinical practice depends on their clinical utility and psychiatrists' understanding of PRS. Our online survey explored these issues with 276 professionals working in psychiatric genetics (RR: 19%). Overall, participants demonstrated knowledge of how to interpret PRS results. Their performance on knowledge-based questions was positively correlated with participants' self-reported familiarity with PRS (r = 0.21, p = 0.0006) although differences were not statistically significant (Wald Chi-square = 3.29, df = 1, p = 0.07). However, only 48.9% of all participants answered all knowledge questions correctly. Many participants (56.5%), especially researchers (42%), indicated having at least occasional conversations about the role of genetics in psychiatric conditions with patients and/or family members. Most participants (62.7%) indicated that PRS are not yet sufficiently robust for assessment of susceptibility to schizophrenia; most significant obstacles were low predictive power and lack of population diversity in available PRS (selected, respectively, by 53.6% and 29.3% of participants). Nevertheless, 89.8% of participants were optimistic about the use of PRS in the next 10 years, suggesting a belief that current shortcomings could be addressed. Our findings inform about the perceptions of psychiatric professionals regarding PRS and the application of PRS in psychiatry.

Novel Application of Polygenic Risk Scores Identifies Biomarkers for Early Detection of Glioma

Abstract AIMS In the UK, brain, other CNS and intracranial tumours incidence rates are increasing with over 12,200 new cases diagnosed each year. The overall survival rate over 5 years in these tumours is approximately 12%. There is a need for predictive biomarkers to detect the presence of gliomas to improve prognosis for such patients. A novel modified Mendelian randomization (MR)-based technique has been proposed to discover predictive biomarkers for diagnostic applications. The aim of this project is to identify circulating blood biomarkers that are genetically liable to all gliomas, glioblastoma (GB), or non-GBs. METHOD We generated polygenic scores for genetic liability to glioma and a separate GWAS of plasma protein levels from the INTERVAL study. We used sensitivity analyses methods to further assess our findings. The results were then validated by the plasma pQTL GWAS data from the Fenland cohort. RESULTS We identified a potential blood biomarker called erythroblast membrane associated protein (ERMAP) which showed consistent results across all sensitivity analyses in our discovery cohort [β: -0.0778, P-value: 2.1E-04]. We identified that decreased levels of ERMAP [β: -0.078 SD of ERMAP abundance per SD increase in glioma risk, 95% confidence interval (CI) 0.037 to 0.119, P-value: 2.1E-04] in the blood are associated with increased risk of glioma. CONCLUSION ERMAP protein is found to have a potential genetic liability for all gliomas. Predictive biomarkers are novel to glioma research and could be important for diagnostic applications and population screening in early detection. However, further clinical research is required to validate these findings.

Application of European-specific polygenic risk scores for predicting prostate cancer risk in different ancestry populations.

Polygenic risk score (PRS) has shown promise in predicting prostate cancer (PCa) risk. However, the application of PRS in non-European ancestry was poorly studied. We constructed PRS using 68, 86, or 128 PCa-associated single-nucleotide polymorphisms (SNPs) identified through a large-scale Genome-wide association study (GWAS) in the European ancestry population. A calibration approach was performed to adjust the PRS exact value for each ancestry. The study was conducted in East Asian (ChinaPCa Consortium, n = 2379), European (UK Biobank, n = 209,172), and African American (African Ancestry Prostate Cancer Consortium, n = 6016). Individuals with the highest PRS (in >97.5th percentile) had over 2.5-fold increased risk of PCa than those with average PRS (in 40th-60th percentile) in both European (odds ratio [OR] = 3.79, 95% confidence interval [CI] = 3.46-4.16, p < 0.001) and Chinese (OR = 2.87, 95% CI = 1.29-6.40, p = 0.010), while slightly lower in African American (OR = 1.77, 95% CI = 1.22-2.58, p = 0.008). Compared with the lowest PRS (in <2.5th percentile), increased PRS was also associated with the earlier onset of PCa (All log-rank p < 0.05). The highest PRS contributed to having about 5- to 12-fold higher lifetime risk and 5-10 years earlier at disease onset than the lowest category across different ancestry populations. We demonstrated that European-GWAS-based PRS could also significantly predict PCa risk in Asian ancestry and African ancestry populations.

Testing the application of polygenic risk scores in the transplant setting - Relevance for precision medicine.

Testing the application of polygenic risk scores in the transplant setting - Relevance for precision medicine.

Improving the computation efficiency of polygenic risk score modeling: faster in Julia.

To enable large-scale application of polygenic risk scores (PRSs) in a computationally efficient manner, we translate a widely used PRS construction method, PRS-continuous shrinkage, to the Julia programming language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average runtime by 5.5×. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes usage of PRSs by lowering the computational burden of the PRS-continuous shrinkage method.

Construction and Application of Polygenic Risk Scores in Autoimmune Diseases.

Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.

Combining Polygenic Risk Score and Voice Features to Detect Major Depressive Disorders

Background: The application of polygenic risk scores (PRSs) in major depressive disorder (MDD) detection is constrained by its simplicity and uncertainty. One promising way to further extend its usability is fusion with other biomarkers. This study constructed an MDD biomarker by combining the PRS and voice features and evaluated their ability based on large clinical samples. Methods: We collected genome-wide sequences and utterances edited from clinical interview speech records from 3,580 women with recurrent MDD and 4,016 healthy people. Then, we constructed PRS as a gene biomarker by p value-based clumping and thresholding and extracted voice features using the i-vector method. Using logistic regression, we compared the ability of gene or voice biomarkers with the ability of both in combination for MDD detection. We also tested more machine learning models to further improve the detection capability. Results: With a p-value threshold of 0.005, the combined biomarker improved the area under the receiver operating characteristic curve (AUC) by 9.09% compared to that of genes only and 6.73% compared to that of voice only. Multilayer perceptron can further heighten the AUC by 3.6% compared to logistic regression, while support vector machine and random forests showed no better performance. Conclusion: The addition of voice biomarkers to genes can effectively improve the ability to detect MDD. The combination of PRS and voice biomarkers in MDD detection is feasible. This study provides a foundation for exploring the clinical application of genetic and voice biomarkers in the diagnosis of MDD.

Clinical experience with polygenic risk score in subjects with early cognitive concerns

AbstractBackgroundThere is mounting evidence that lifestyle interventions and behavioural changes play a significant role in maintaining cognition and function, as well as preventing dementia. Consequently, it is important that clinicians confronted with subjects with early cognitive concerns, have appropriate tools available to assist in diagnosis and to facilitate risks management appropriately. The application of polygenic risk score (PRS) tests has the potential to contribute towards management planning and to reduce the burden of testing in subjects with low overall risk.MethodsThis retrospective analysis considered the application of genoSCORETM in a small cohort of patients seen over a six month period in a London Memory Clinic. The test was offered to selected patients in the clinic with MCI not clinically attributable to dementia, or cognitively normal individuals concerned about their risks of dementia. The impact upon clinical management and lifestyle modification was reviewed. genoSCORE, a polygenic risk score algorithm, was developed by Cytox to assess genetic risk for the future development of Late‐Onset Alzheimer’s disease (LOAD).ResultsPatients receiving the genoSCORE test included those with early MCI, subjective memory complaints and a small number concerned about their risk of dementia. In each case, a medical history was taken and individuals assessed using the Addenbrooke’s Cognitive Examination, conducted either in clinic or remotely. genoSCORE polygenic risk score was easy to conduct and well received. The results stimulated individuals at risk of developing LOAD to make lifestyle adjustments and thereby potentially modifying their dementia risk.ConclusionsIn this study, the genoSCORE PRS test provided a valuable assessment of genetic risk of individuals most likely to decline cognitively decline towards AD and as such, contributed significantly to clinical management decisions. The ease and effectiveness of home sampling of saliva as source DNA for the PRS test was a major factor and well aligned with the continuing need for remote consultations in the light of COVID‐19 concerns. Further larger‐scale studies to determine the full clinical and associated economic impact of the genoSCORE PRS test are required.

Abstract 11949: Disease-Specific and Comorbidity-Related Polygenic Risk in Spontaneous Coronary Artery Dissection

Introduction: Spontaneous coronary artery dissection (SCAD) is an increasingly recognised cause of acute coronary syndrome that typically affects younger women (24-52 years). Several comorbidities have now been reported, including migraine, fibromuscular dysplasia (FMD), connective tissue disorders, anxiety, and depression. Associations between SCAD and other conditions, such as autoimmune disorders, have been reported but remain contentious. SCAD is known to have a genetic element: both common and rare variants have been associated with the disease and familial clustering has been identified. However, the genetic etiology is not well resolved although common variants have been identified between SCAD, FMD, and migraine. Aims: We sought to determine if there is a polygenic disease burden of common genetic variation in a cohort of sporadic SCAD cases, and shared genetics between SCAD and reported comorbidities via the application of polygenic risk scores (PRSs) for these comorbidities. Methods: A cohort of 173 sporadic European SCAD cases and 1314 European controls were genotyped (88 cases, 1127 healthy elderly controls and 76 non-SCAD cardiomyopathy-diseased patients by whole genome sequencing; 85 cases and 111 controls by Axiom UK Biobank and PMDA array, respectively). Differences in genetic risk was assessed for cases and controls using established PRS, specific to either SCAD or conditions reported to be comorbid. Results: SCAD cases showed significantly higher SCAD-PRS than did controls (WGS p = 1.372e-09; array p = 5.264e-05). In contrast, there were no differences in SCAD-PRS between non-SCAD patients and controls (WGS only: p-value = 0.291) and non-SCAD patients also had lower risk than SCAD cases (WGS only: p-value = 2.08e-07). Multiple PRS derived from comorbid disorders are currently being investigated in this cohort. Conclusions: We have shown that there is a common genetic risk element to SCAD, while genetic risk for, and, therefore, genetic overlap with, reported comorbidities in SCAD will be further discussed. These findings illustrate the role of common variants in SCAD etiology and may further contribute to our understanding of the mechanisms leading to SCAD formation.

Polygenic Risk Scores Contribute to Personalized Medicine of Parkinson's Disease.

Parkinson’s disease (PD) is the second most common neurodegenerative disorder characterized by the loss of dopaminergic neurons. The vast majority of PD patients develop the disease sporadically and it is assumed that the cause lies in polygenic and environmental components. The overall polygenic risk is the result of a large number of common low-risk variants discovered by large genome-wide association studies (GWAS). Polygenic risk scores (PRS), generated by compiling genome-wide significant variants, are a useful prognostic tool that quantifies the cumulative effect of genetic risk in a patient and in this way helps to identify high-risk patients. Although there are limitations to the construction and application of PRS, such as considerations of limited genetic underpinning of diseases explained by SNPs and generalizability of PRS to other populations, this personalized risk prediction could make a promising contribution to stratified medicine and tailored therapeutic interventions in the future.

Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction

A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual’s disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level. Here, we introduce a machine-learning technique, Mondrian Cross-Conformal Prediction (MCCP), to estimate the confidence bounds of PRS-to-disease-risk prediction. MCCP can report disease status conditional probability value for each individual and give a prediction at a desired error level. Moreover, with a user-defined prediction error rate, MCCP can estimate the proportion of sample (coverage) with a correct prediction.

Insights into the genomics of affective disorders

Abstract Affective disorders, or mood disorders, are a group of neuropsychiatric illnesses that are characterized by a disturbance of mood or affect. Most genetic research in this field to date has focused on bipolar disorder and major depression. Symptoms of major depression include a depressed mood, reduced energy, and a loss of interest and enjoyment. Bipolar disorder is characterized by the occurrence of (hypo)manic episodes, which generally alternate with periods of depression. Formal and molecular genetic studies have demonstrated that affective disorders are multifactorial diseases, in which both genetic and environmental factors contribute to disease development. Twin and family studies have generated heritability estimates of 58–85 % for bipolar disorder and 40 % for major depression. Large genome-wide association studies have provided important insights into the genetics of affective disorders via the identification of a number of common genetic risk factors. Based on these studies, the estimated overall contribution of common variants to the phenotypic variability (single-nucleotide polymorphism [SNP]-based heritability) is 17–23 % for bipolar disorder and 9 % for major depression. Bioinformatic analyses suggest that the associated loci and implicated genes converge into specific pathways, including calcium signaling. Research suggests that rare copy number variants make a lower contribution to the development of affective disorders than to other psychiatric diseases, such as schizophrenia or the autism spectrum disorders, which would be compatible with their less pronounced negative impact on reproduction. However, the identification of rare sequence variants remains in its infancy, as available next-generation sequencing studies have been conducted in limited samples. Future research strategies will include the enlargement of genomic data sets via innovative recruitment strategies; functional analyses of known associated loci; and the development of new, etiologically based disease models. Researchers hope that deeper insights into the biological causes of affective disorders will eventually lead to improved diagnostics and disease prediction, as well as to the development of new preventative, diagnostic, and therapeutic strategies. Pharmacogenetics and the application of polygenic risk scores represent promising initial approaches to the future translation of genomic findings into psychiatric clinical practice.

[Application of polygenic risk scores in risk prediction and precision prevention of complex diseases: opportunities and challenges].

Along with the rapid progress in the field of human genomics, genome-wide association studies have successfully identified numerous risk loci for complex diseases. Polygenic risk scores can predict disease risk by integrating the effects of multiple susceptibility loci, and begin to show good performance for improving risk prediction, screening strategy and precision prevention. This paper briefly reviews the recent progress of polygenic risk scores in disease prevention, and summarizes the opportunities and challenges of its application.

SU18 - INVESTIGATING SHRINKAGE METHODS TO IMPROVE ACCURACY OF GWAS AND PRS EFFECT SIZE ESTIMATES

Background As the scale and power of GWAS have increased to detect the small genetic effect sizes involved in complex polygenic traits, the results of GWAS, especially SNP effect size estimates, are increasingly utilised for prediction. A popular approach is the application of Polygenic Risk Scores (PRS). However, the effect size estimates are inherently prone to overfit the specific samples on which the GWAS were performed (e.g. “the Winner's Curse”). The inflation of effect size estimation reduces the out-of-sample accuracy of prediction based on GWAS results. Many shrinkage methods have been developed to correct for such inflation. As GWAS results and large individual genotype data sets become widely available, there is greater opportunity to accurately evaluate effect size inflation and compare the performance of different shrinkage methods. Methods We develop a novel permutation-based shrinkage method and compare its performance with previously developed methods based on local false discovery rate and the LASSO. We evaluate the performance of the methods across a wide range of phenotypes and investigate different factors (e.g. phenotype heritability) that influence their impact on the predictive power of polygenic risk scores. Results Using large-scale real genotype data, we show that SNP effect sizes are markedly overfit even in relatively large samples, but that our permutation-based shrinkage approach can improve PRS prediction dramatically. Discussion Our results suggest that GWAS results can be adjusted using an efficient empirical approach to provide more accurate effect size estimates and thus greater downstream predictive power. This approach could be applied to a wide variety of big data settings.

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n ~ 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders.

Recent Progress in Pharmacogenomics of Antipsychotic Drug Response.

Pharmacogenomics (PGx) of antipsychotic drug response is an active area of research in the past few years. We reviewed recent PGx studies with an emphasis of development of new methodologies and new research directions. Traditional candidate gene approach continues to generate evidence to support the associations of antipsychotic response with genes coding for drug targets such as DRD2. Genome-wide association studies have found a few novel genes that may be associated with drug efficacy and adverse events. Recent application of polygenic risk score makes it possible to combine many genetic variants to predict clinical response. Finally, epigenetic research including DNA methylation is emerging and promises new findings that potentially can be applied in clinical practice. New methodologies may advance PGx closer to clinical application. Multiple genes and epigenomic markers can be used in prediction of clinical phenotypes.