The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation

Significant sparse polygenic risk scores across 813 traits in UK Biobank

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Local genetic variation of inflammatory bowel disease in Basque population and its effect in risk prediction

Inflammatory bowel disease (IBD) is characterised by chronic inflammation of the gastrointestinal tract. Although its aetiology remains unknown, environmental and genetic factors are involved in its development. Regarding genetics, more than 200 loci have been associated with IBD but the transferability of those signals to the Basque population living in Northern Spain, a population with distinctive genetic background, remains unknown. We have analysed 5,411,568 SNPs in 498 IBD cases and 935 controls from the Basque population. We found 33 suggestive loci (p < 5 × 10⁻⁶) in IBD and its subtypes, namely Crohn’s Disease (CD) and Ulcerative Colitis (UC), detecting a genome-wide significant locus located in HLA region in patients with UC. Those loci contain previously associated genes with IBD (IL23R, JAK2 or HLA genes) and new genes that could be involved in its development (AGT, BZW2 or FSTL1). The overall genetic correlation between European populations and Basque population was high in IBD and CD, while in UC was lower. Finally, the use of genetic risk scores based on previous GWAS findings reached area under the curves > 0.68. In conclusion, we report on the genetic architecture of IBD in the Basque population, and explore the performance of European-descent genetic risk scores in this population.

A tool for translating polygenic scores onto the absolute scale using summary statistics

There is growing interest in the clinical application of polygenic scores as their predictive utility increases for a range of health-related phenotypes. However, providing polygenic score predictions on the absolute scale is an important step for their safe interpretation. We have developed a method to convert polygenic scores to the absolute scale for binary and normally distributed phenotypes. This method uses summary statistics, requiring only the area-under-the-ROC curve (AUC) or variance explained (R2) by the polygenic score, and the prevalence of binary phenotypes, or mean and standard deviation of normally distributed phenotypes. Polygenic scores are converted using normal distribution theory. We also evaluate methods for estimating polygenic score AUC/R2 from genome-wide association study (GWAS) summary statistics alone. We validate the absolute risk conversion and AUC/R2 estimation using data for eight binary and three continuous phenotypes in the UK Biobank sample. When the AUC/R2 of the polygenic score is known, the observed and estimated absolute values were highly concordant. Estimates of AUC/R2 from the lassosum pseudovalidation method were most similar to the observed AUC/R2 values, though estimated values deviated substantially from the observed for autoimmune disorders. This study enables accurate interpretation of polygenic scores using only summary statistics, providing a useful tool for educational and clinical purposes. Furthermore, we have created interactive webtools implementing the conversion to the absolute ( https://opain.github.io/GenoPred/PRS_to_Abs_tool.html ). Several further barriers must be addressed before clinical implementation of polygenic scores, such as ensuring target individuals are well represented by the GWAS sample.

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8–4.6] up to 6.2 [4.6–7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.

Max Tamlander et al. combine polygenic risk scores and clinical assessments to improve prediction of coronary artery disease and type 2 diabetes in European cohorts. Taken together, their results provide a useful method for preliminary cardiometabolic risk assessment in patients.

Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists

A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.

Somatic mutational profiles and germline polygenic risk scores in human cancer

BACKGROUND

The mutational profile of cancer reflects the activity of the mutagenic processes which have been operative throughout the lineage of the cancer cell. These processes leave characteristic profiles of somatic mutations called mutational signatures. Mutational signatures, including single-base substitution (SBS) signatures, may reflect the effects of exogenous or endogenous exposures.

METHODS

We used polygenic risk scores (PRS) to summarize common germline variation associated with cancer risk and other cancer-related traits and examined the association between somatic mutational profiles and germline PRS in 12 cancer types from The Cancer Genome Atlas. Somatic mutational profiles were constructed from whole-exome sequencing data of primary tumors. PRS were calculated for the 12 selected cancer types and 9 non-cancer traits, including cancer risk determinants, hormonal factors, and immune-mediated inflammatory diseases, using germline genetic data and published summary statistics from genome-wide association studies.

RESULTS

We found 17 statistically significant associations between somatic mutational profiles and germline PRS after Bonferroni correction (p < 3.15 × 10^-5), including positive associations between germline inflammatory bowel disease PRS and number of somatic mutations attributed to signature SBS1 in prostate cancer and APOBEC-related signatures in breast cancer. Positive associations were also found between age at menarche PRS and mutation counts of SBS1 in overall and estrogen receptor-positive breast cancer. Consistent with prior studies that found an inverse association between the pubertal development PRS and risk of prostate cancer, likely reflecting hormone-related mechanisms, we found an inverse association between age at menarche PRS and mutation counts of SBS1 in prostate cancer. Inverse associations were also found between several cancer PRS and tumor mutation counts.

CONCLUSIONS

Our analysis suggests that there are robust associations between tumor somatic mutational profiles and germline PRS. These may reflect the mechanisms through hormone regulation and immune responses that contribute to cancer etiology and drive cancer progression.

Polygenic score development in the era of large-scale biobanks

In this issue of Cell Genomics, Xu et al. report a comprehensive analysis of the genetics of 26 blood cell traits, leveraging data from two large biobanks to construct and make available machine-learning optimized polygenic scores (PGSs). In addition to delivering insights into the biology and clinical associations of these traits, the authors evaluate and provide recommendations on methods for PGS construction.

Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores

Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.

The European Bioinformatics Institute (EMBL-EBI) in 2021

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI's core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI's online training offering.

Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort

The low portability of polygenic scores (PGSs) across global populations is a major concern that must be addressed before PGSs can be used for everyone in the clinic. Indeed, prediction accuracy has been shown to decay as a function of the genetic distance between the training and test cohorts. However, such cohorts differ not only in their genetic distance but also in their geographical distance and their data collection and assaying, conflating multiple factors. In this study, we examine the extent to which PGSs are transferable between ancestries by deriving polygenic scores for 245 curated traits from the UK Biobank data and applying them in nine ancestry groups from the same cohort. By restricting both training and testing to the UK Biobank data, we reduce the risk of environmental and genotyping confounding from using different cohorts. We define the nine ancestry groups at a sub-continental level, based on a simple, robust, and effective method that we introduce here. We then apply two different predictive methods to derive polygenic scores for all 245 phenotypes and show a systematic and dramatic reduction in portability of PGSs trained using Northwestern European individuals and applied to nine ancestry groups. These analyses demonstrate that prediction already drops off within European ancestries and reduces globally in proportion to genetic distance. Altogether, our study provides unique and robust insights into the PGS portability problem.

Data Mining, Quality and Management in the Life Sciences

With the evermore emphasis put on open science and its invaluable benefits to the scientific community, it is no longer the case where a research project simply ends with a scientific publication. The benefits of data sharing and reproducibility of results have taken the centerpiece within the life science research supported by FAIR principles that firmly underline the importance of open data. The current data-intensive multidisciplinary research has also highlighted the significance of how data is mined and managed. Here we describe some of the features adopted by EMBL-EBI data resources to support data mining, data quality, and data management. We also highlight how EMBL-EBI has responded to the current pandemic through its data resources.

From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics

Autoimmune diseases share a general mechanism of auto-antigens harming tissues. Still. they are phenotypically diverse, with genetic as well as environmental factors contributing to their etiology at varying degrees. Associated genomic loci and variants have been identified in numerous genome-wide association studies (GWAS), whose results are increasingly used for polygenic scores (PGS) that are used to predict disease risk. At the same time, a technological shift from genotyping arrays to next generation sequencing (NGS) is ongoing. NGS allows the identification of virtually all - including rare - genetic variants, which in combination with methodological developments promises to improve the prediction of disease risk and elucidate molecular mechanisms underlying disease. Here we review current, publicly available autoimmune disease GWAS and PGS data based on information from the GWAS and PGS catalog, respectively. We summarize autoimmune diseases investigated, respective studies conducted and their results. Further, we review genetic data and autoimmune disease patients in the UK Biobank (UKB), the largest resource for genetic and phenotypic data available for academic research. We find that only comparably prevalent autoimmune diseases are covered by the UKB and at the same time assessed by both GWAS and PGS catalogs. These are systemic (systemic lupus erythematosus) as well as organ-specific, affecting the gastrointestinal tract (inflammatory bowel disease as well as specifically Crohn's disease and ulcerative colitis), joints (juvenile ideopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis), glands (Sjögren syndrome), the nervous system (multiple sclerosis), and the skin (vitiligo).

A systematic review and analysis of the use of polygenic scores in pharmacogenomics

Polygenic scores (PGS) have emerged as promising tools for complex trait risk prediction. The application of these scores to pharmacogenomics provides new opportunities to improve the prediction of treatment outcomes. To gain insight into this area of research, we conducted a systematic review and accompanying analysis. This review uncovered 51 papers examining the use of PGS for drug-related outcomes, with the majority of these papers focusing on the treatment of psychiatric disorders (n=30). Due to difficulties in collecting large cohorts of uniformly treated patients, the majority of pharmacogenomic PGS were derived from large-scale genome-wide association studies of disease phenotypes that were related to the pharmacogenomic phenotypes under investigation (e.g. schizophrenia-derived PGS for antipsychotic response prediction). Examination of the research participants included in these studies revealed that the majority of cohort participants were of European descent (78.4%). These biases were also reflected in research affiliations, which were heavily weighted towards institutions located in Europe and North America, with no first or last authors originating from institutions in Africa or South Asia. There was also substantial variability in the methods used to develop PGS, with between 3 and 6.6 million variants included in the PGS. Finally, we observed significant inconsistencies in the reporting of PGS analyses and results, particularly in terms of risk model development and application, coupled with a lack of data transparency and availability, with only three pharmacogenomics PGS deposited on the PGS Catalog. These findings highlight current gaps and key areas for future pharmacogenomic PGS research.

Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants

Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.

Resource profile and user guide of the Polygenic Index Repository

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Clinical utility of polygenic risk scores for coronary artery disease

Over the past decade, substantial progress has been made in the discovery of alleles contributing to the risk of coronary artery disease. In addition to providing causal insights into disease, these endeavours have yielded and enabled the refinement of polygenic risk scores. These scores can be used to predict incident coronary artery disease in multiple cohorts and indicate the clinical response to some preventive therapies in post hoc analyses of clinical trials. These observations and the widespread ability to calculate polygenic risk scores from direct-to-consumer and health-care-associated biobanks have raised many questions about responsible clinical adoption. In this Review, we describe technical and downstream considerations for the derivation and validation of polygenic risk scores and current evidence for their efficacy and safety. We discuss the implementation of these scores in clinical medicine for uses including risk prediction and screening algorithms for coronary artery disease, prioritization of patient subgroups that are likely to derive benefit from treatment, and efficient prospective clinical trial designs.

A Polygenic Risk Score Predicts Incident Prostate Cancer Risk in Older Men but Does Not Select for Clinically Significant Disease

Despite the high prevalence of prostate cancer in older men, the predictive value of a polygenic risk score (PRS) remains uncertain in men aged ≥70 years. We used a 6.6 million-variant PRS to predict the risk of incident prostate cancer in a prospective study of 5701 men of European descent aged ≥70 years (mean age 75 years) enrolled in the ASPirin in Reducing Events in the Elderly (ASPREE) clinical trial. The study endpoint was prostate cancer, including metastatic or non-metastatic disease, confirmed by an expert panel. After excluding participants with a history of prostate cancer at enrolment, we used a multivariable Cox proportional hazards model to assess the association between the PRS and incident prostate cancer risk, adjusting for covariates. Additionally, we examined the distribution of Gleason grade groups by PRS group to determine if a higher PRS was associated with higher grade disease. We tested for interaction between the PRS and aspirin treatment. Logistic regression was used to independently assess the association of the PRS with prevalent (pre-trial) prostate cancer, reported in medical histories. During a median follow-up time of 4.6 years, 218 of the 5701 participants (3.8%) were diagnosed with prostate cancer. The PRS predicted incident risk with a hazard ratio (HR) of 1.52 per standard deviation (SD) (95% confidence interval (CI) 1.33-1.74, p < 0.001). Men in the top quintile of the PRS distribution had an almost three times higher risk of prostate cancer than men in the lowest quintile (HR = 2.99 (95% CI 1.90-4.27), p < 0.001). However, a higher PRS was not associated with a higher Gleason grade groups. We found no interaction between aspirin treatment and the PRS for prostate cancer risk. The PRS was also associated with prevalent prostate cancer (odds ratio = 1.80 per SD (95% CI 1.65-1.96), p < 0.001).While a PRS for prostate cancer is strongly associated with incident risk in men aged ≥70 years, the clinical utility of the PRS as a biomarker is currently limited by its inability to select for clinically significant disease.

Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps

Polygenic risk scores (PRSs) aggregate the many small effects of alleles across the human genome to estimate the risk of a disease or disease-related trait for an individual. The potential benefits of PRSs include cost-effective enhancement of primary disease prevention, more refined diagnoses and improved precision when prescribing medicines. However, these must be weighed against the potential risks, such as uncertainties and biases in PRS performance, as well as potential misunderstanding and misuse of these within medical practice and in wider society. By addressing key issues including gaps in best practices, risk communication and regulatory frameworks, PRSs can be used responsibly to improve human health. Here, the International Common Disease Alliance's PRS Task Force, a multidisciplinary group comprising expertise in genetics, law, ethics, behavioral science and more, highlights recent research to provide a comprehensive summary of the state of polygenic score research, as well as the needs and challenges as PRSs move closer to widespread use in the clinic.

Heterogeneity in how women value risk-stratified breast screening

PURPOSE

Risk-stratified screening has potential to improve the cost effectiveness of national breast cancer screening programs. This study aimed to inform a socially acceptable and equitable implementation framework by determining what influences a woman's decision to accept a personalized breast cancer risk assessment and what the relative impact of these key determinants is.

METHODS

Multicriteria decision analysis was used to elicit the relative weights for 8 criteria that women reported influenced their decision. Preference heterogeneity was explored through cluster analysis.

RESULTS

The 2 criteria valued most by the 347 participants related to program access, "Mode of invitation" and "Testing process". Both criteria significantly influenced participation (P < .001). A total of 73% preferred communication by letter/online. Almost all women preferred a multidisease risk assessment with potential for a familial high-risk result. Four preference-based subgroups were identified. Membership to the largest subgroup was predicted by lower educational attainment, and women in this subgroup were concerned with program access. Higher relative perceived breast cancer risk predicted membership to the smallest subgroup that was focused on test parameters, namely "Scope of test" and "Test specificity".

CONCLUSION

Overall, Australian women would accept a personalized multidisease risk assessment, but when aligning with their preferences, it will necessitate a focus on program access and the development of online communication frameworks.

GA4GH: International policies and standards for data sharing across genomic research and healthcare

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits.

Workshop proceedings: GWAS summary statistics standards and sharing

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.

Utility of polygenic embryo screening for disease depends on the selection strategy

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. A strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. When only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. We systematically examine the impact of several factors on the utility of screening, including: variance explained by the PRS, number of embryos, disease prevalence, parental PRSs, and parental disease status. We consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions, and also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating 'virtual' couples and offspring based on real genomes from schizophrenia and Crohn's disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

The Propagation of Racial Disparities in Cardiovascular Genomics Research

Genomics research has improved our understanding of the genetic basis for human traits and diseases. This progress is now being translated into clinical care as we move toward a future of precision medicine. Many hope that expanded use of genomic testing will improve disease screening, diagnosis, risk stratification, and treatment. In many respects, cardiovascular medicine is leading this charge. However, most cardiovascular genomics research has been conducted in populations of primarily European ancestry. This bias has critical downstream effects. Here, we review the current disparities in cardiovascular genomics research, and we outline how these disparities propagate forward through all phases of the translational pipeline. If not adequately addressed, biases in genomics research will further compound the existing health disparities that face underrepresented and marginalized populations.

Design and user experience testing of a polygenic score report: a qualitative study of prospective users

Background

Polygenic scores—which quantify inherited risk by integrating information from many common sites of DNA variation—may enable a tailored approach to clinical medicine. However, alongside considerable enthusiasm, we and others have highlighted a lack of standardized approaches for score disclosure. Here, we review the landscape of polygenic score reporting and describe a generalizable approach for development of a polygenic score disclosure tool for coronary artery disease.

Methods

We assembled a working group of clinicians, geneticists, data visualization specialists, and software developers. The group reviewed existing polygenic score reports and then designed a two-page mock report for coronary artery disease. We then conducted a qualitative user-experience study with this report using an interview guide focused on comprehension, experience, and attitudes. Interviews were transcribed and analyzed for themes identification to inform report revision.

Results

Review of nine existing polygenic score reports from commercial and academic groups demonstrated significant heterogeneity, reinforcing the need for additional efforts to study and standardize score disclosure. Using a newly developed mock score report, we conducted interviews with ten adult individuals (50% females, 70% without prior genetic testing experience, age range 20–70 years) recruited via an online platform. We identified three themes from interviews: (1) visual elements, such as color and simple graphics, enable participants to interpret, relate to, and contextualize their polygenic score, (2) word-based descriptions of risk and polygenic scores presented as percentiles were the best recognized and understood, (3) participants had varying levels of interest in understanding complex genomic information and therefore would benefit from additional resources that can adapt to their individual needs in real time. In response to user feedback, colors used for communicating risk were modified to minimize unintended color associations and odds ratios were removed. All 10 participants expressed interest in receiving a polygenic score report based on their personal genomic information.

Conclusions

Our findings describe a generalizable approach to develop a polygenic score report understandable by potential patients. Although additional studies are needed across a wider spectrum of patient populations, these results are likely to inform ongoing efforts related to polygenic score disclosure within clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-021-01056-0.

Genomic risk prediction of coronary artery disease in women with breast cancer: a prospective cohort study

BACKGROUND

Advancements in cancer therapeutics have resulted in increases in cancer-related survival; however, there is a growing clinical dilemma. The current balancing of survival benefits and future cardiotoxic harms of oncotherapies has resulted in an increased burden of cardiovascular disease in breast cancer survivors. Risk stratification may help address this clinical dilemma. This study is the first to assess the association between a coronary artery disease-specific polygenic risk score and incident coronary artery events in female breast cancer survivors.

METHODS

We utilized the Studies in Epidemiology and Research in Cancer Heredity prospective cohort involving 12,413 women with breast cancer with genotype information and without a baseline history of cardiovascular disease. Cause-specific hazard ratios for association of the polygenic risk score and incident coronary artery disease (CAD) were obtained using left-truncated Cox regression adjusting for age, genotype array, conventional risk factors such as smoking and body mass index, as well as other sociodemographic, lifestyle, and medical variables.

RESULTS

Over a median follow-up of 10.3 years (IQR: 16.8) years, 750 incident fatal or non-fatal coronary artery events were recorded. A 1 standard deviation higher polygenic risk score was associated with an adjusted hazard ratio of 1.33 (95% CI 1.20, 1.47) for incident CAD.

CONCLUSIONS

This study provides evidence that a coronary artery disease-specific polygenic risk score can risk-stratify breast cancer survivors independently of other established cardiovascular risk factors.

Discovery and implications of polygenicity of common diseases

The sequencing of the human genome has allowed the study of the genetic architecture of common diseases: the number of genomic variants that contribute to risk of disease and their joint frequency and effect size distribution. Common diseases are polygenic, with many loci contributing to phenotype, and the cumulative burden of risk alleles determines individual risk in conjunction with environmental factors. Most risk loci occur in noncoding regions of the genome regulating cell- and context-specific gene expression. Although the effect sizes of most risk alleles are small, their cumulative effects in individuals, quantified as a polygenic (risk) score, can identify people at increased risk of disease, thereby facilitating prevention or early intervention.

Combination of a 15-SNP Polygenic Risk Score and Classical Risk Factors for the Prediction of Breast Cancer Risk in Cypriot Women

Simple Summary

Breast cancer is the most commonly diagnosed type of cancer in women worldwide. Stratification of women based on their individual breast cancer risk could guide targeted preventative strategies and population screening. Integrated models that combine the effects of a polygenic risk score (PRS) with classical breast cancer risk factors could provide an individualized breast-cancer risk estimation. Although various studies have extensively evaluated the performance of such integrated models in populations of European ancestry, no previous studies have included individuals of Greek-Cypriot origin. To this end, we have assessed the predictive performance of a 15-SNP PRS (PRS₁₅), in combination with classical breast-cancer risk factors, in women of Greek-Cypriot origin. This proof-of-concept study suggests that models combining genetic data with classical risk factors may be used in the future for the prediction of breast-cancer risk and, therefore, supports their potential clinical utility for targeted preventative strategies in Cypriot women.

Abstract

The PRS combines multiplicatively the effects of common low-risk single nucleotide polymorphisms (SNPs) and has the potential to be used for the estimation of an individual’s risk for a trait or disease. PRS has been successfully implemented for the prediction of breast cancer risk. The combination of PRS with classical breast cancer risk factors provides a more comprehensive risk estimation and could, thus, improve risk stratification and personalized preventative strategies. In this study, we assessed the predictive performance of the combined effect of PRS₁₅ with classical breast-cancer risk factors in Cypriot women using 1109 cases and 1177 controls from the MASTOS study. The PRS₁₅ was significantly associated with an increased breast cancer risk in Cypriot women OR (95% CI) 1.66 (1.25–2.19). The integrated risk model obtained an AUC (95% CI) 0.70 (0.67–0.72) and had the ability to stratify women according to their disease status at the extreme deciles. These results provide evidence that the combination of PRS with classical risk factors may be used in the future for the stratification of Cypriot women based on their disease risk, and support its potential clinical utility for targeted preventative actions and population screening.

A polygenic-score-based approach for identification of gene-drug interactions stratifying breast cancer risk

An individual's genetics can dramatically influence breast cancer (BC) risk. Although clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly used medications are a promising set of modifiable factors, but no previous study has explored whether a range of widely taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGSs) that summarize BC genetic risk and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with an HR = 3.41 per-allele within corticosteroid users). In comparison, there are no differences in BC risk within the reference allele homozygotes. Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.

Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases

Early prediction of risk of cardiovascular disease (CVD), including stroke, is a cornerstone of disease prevention. Clinical risk scores have been widely used for predicting CVD risk from known risk factors. Most CVDs have a substantial genetic component, which also has been confirmed for stroke in recent gene discovery efforts. However, the role of genetics in prediction of risk of CVD, including stroke, has been limited to testing for highly penetrant monogenic disorders. In contrast, the importance of polygenic variation, the aggregated effect of many common genetic variants across the genome with individually small effects, has become more apparent in the last 5 to 10 years, and powerful polygenic risk scores for CVD have been developed. Here we review the current state of the field of polygenic risk scores for CVD including stroke, and their potential to improve CVD risk prediction. We present findings and lessons from diseases such as coronary artery disease as these will likely be useful to inform future research in stroke polygenic risk prediction.

Embryo Screening for Polygenic Disease Risk: Recent Advances and Ethical Considerations

Machine learning methods applied to large genomic datasets (such as those used in GWAS) have led to the creation of polygenic risk scores (PRSs) that can be used identify individuals who are at highly elevated risk for important disease conditions, such as coronary artery disease (CAD), diabetes, hypertension, breast cancer, and many more. PRSs have been validated in large population groups across multiple continents and are under evaluation for widespread clinical use in adult health. It has been shown that PRSs can be used to identify which of two individuals is at a lower disease risk, even when these two individuals are siblings from a shared family environment. The relative risk reduction (RRR) from choosing an embryo with a lower PRS (with respect to one chosen at random) can be quantified by using these sibling results. New technology for precise embryo genotyping allows more sophisticated preimplantation ranking with better results than the current method of selection that is based on morphology. We review the advances described above and discuss related ethical considerations.

Implementation and implications for polygenic risk scores in healthcare

Increasing amounts of genetic data have led to the development of polygenic risk scores (PRSs) for a variety of diseases. These scores, built from the summary statistics of genome-wide association studies (GWASs), are able to stratify individuals based on their genetic risk of developing various common diseases and could potentially be used to optimize the use of screening and preventative treatments and improve personalized care for patients. Many challenges are yet to be overcome, including PRS validation, healthcare professional and patient education, and healthcare systems integration. Ethical challenges are also present in how this information is used and the current lack of diverse populations with PRSs available. In this review, we discuss the topics above and cover the nature of PRSs, visualization schemes, and how PRSs can be improved. With these tools on the horizon for multiple diseases, scientists, clinicians, health systems, regulatory bodies, and the public should discuss the uses, benefits, and potential risks of PRSs.

Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

Statistical genetics and polygenic risk score for precision medicine

The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future.

Polygenic Risk Scores

As genome-wide association studies have continued to identify loci associated with complex traits, the implications of and necessity for proper use of these findings, including prediction of disease risk, have become apparent. Many complex diseases have numerous associated loci with detectable effects implicating risk for or protection from disease. A common contemporary approach to using this information for disease prediction is through the application of genetic risk scores. These scores estimate an individual's liability for a specific outcome by aggregating the effects of associated loci into a single measure as described in the previous version of this article. Although genetic risk scores have traditionally included variants that meet criteria for genome-wide significance, an extension known as the polygenic risk score has been developed to include the effects of more variants across the entire genome. Here, we describe common methods and software packages for calculating and interpreting polygenic risk scores. In this revised version of the article, we detail information that is needed to perform a polygenic risk score analysis, considerations for planning the analysis and interpreting results, as well as discussion of the limitations based on the choices made. We also provide simulated sample data and a walkthrough for four different polygenic risk score software. © 2021 Wiley Periodicals LLC.

Coronary Artery Disease-associated and Longevity-associated Polygenic Risk Scores for Prediction of Coronary Artery Disease Events in Persons Living with HIV: The Swiss HIV Cohort Study

BACKGROUND

Coronary artery disease (CAD) is in part genetically determined. Aging is accentuated in people with HIV (PLWH). It is unknown whether genetic CAD event prediction in PLWH is improved by applying individual polygenic risk scores (PRS) and by considering genetic variants associated with successful aging and longevity.

METHODS

In Swiss HIV Cohort Study participants of self-reported European descent, we determined univariable and multivariable odds ratios (OR) for CAD events, based on traditional CAD risk factors, adverse antiretroviral exposures, and different validated genome-wide PRS. PRS were built from CAD-associated single nucleotide polymorphisms (SNPs), longevity-associated SNPs, or both.

RESULTS

We included 269 cases with CAD events between 2000-2017 (Median age 54 years, 87% male, 82% with suppressed HIV RNA) and 567 event-free controls. Clinical (i.e. traditional and HIV-related) risk factors, and PRS built from CAD-associated SNPs, longevity-associated SNPs, or both, each contributed independently to CAD events (p<0.001). Participants with the most unfavorable clinical risk factor profile (top quintile) had adjusted CAD-OR=17.82 (8.19-38.76), compared to participants in the bottom quintile. Participants with the most unfavorable CAD-PRS (top quintile) had adjusted CAD-OR=3.17 (1.74-5.79), compared to the bottom quintile. After adding longevity-associated SNPs to the CAD-PRS, participants with the most unfavorable genetic background (top quintile) had adjusted CAD-OR=3.67 (2.00-6.73), compared to the bottom quintile.

CONCLUSIONS

In Swiss PLWH, CAD prediction based on traditional and HIV-related risk factors was superior to genetic CAD prediction based on longevity- and CAD-associated PRS. Combining traditional, HIV-related and genetic risk factors provided the most powerful CAD prediction.

Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction

The accuracy of polygenic risk scores (PRSs) to predict complex diseases increases with the training sample size. PRSs are generally derived based on summary statistics from large meta-analyses of multiple genome-wide association studies (GWASs). However, it is now common for researchers to have access to large individual-level data as well, such as the UK Biobank data. To the best of our knowledge, it has not yet been explored how best to combine both types of data (summary statistics and individual-level data) to optimize polygenic prediction. The most widely used approach to combine data is the meta-analysis of GWAS summary statistics (meta-GWAS), but we show that it does not always provide the most accurate PRS. Through simulations and using 12 real case-control and quantitative traits from both iPSYCH and UK Biobank along with external GWAS summary statistics, we compare meta-GWAS with two alternative data-combining approaches, stacked clumping and thresholding (SCT) and meta-PRS. We find that, when large individual-level data are available, the linear combination of PRSs (meta-PRS) is both a simple alternative to meta-GWAS and often more accurate.

Genetic sensitivity analysis: Adjusting for genetic confounding in epidemiological associations

Associations between exposures and outcomes reported in epidemiological studies are typically unadjusted for genetic confounding. We propose a two-stage approach for estimating the degree to which such observed associations can be explained by genetic confounding. First, we assess attenuation of exposure effects in regressions controlling for increasingly powerful polygenic scores. Second, we use structural equation models to estimate genetic confounding using heritability estimates derived from both SNP-based and twin-based studies. We examine associations between maternal education and three developmental outcomes - child educational achievement, Body Mass Index, and Attention Deficit Hyperactivity Disorder. Polygenic scores explain between 14.3% and 23.0% of the original associations, while analyses under SNP- and twin-based heritability scenarios indicate that observed associations could be almost entirely explained by genetic confounding. Thus, caution is needed when interpreting associations from non-genetically informed epidemiology studies. Our approach, akin to a genetically informed sensitivity analysis can be applied widely.

Improving reporting standards for polygenic scores in risk prediction studies

Polygenic risk scores (PRSs), which often aggregate results from genome-wide association studies, can bridge the gap between initial discovery efforts and clinical applications for the estimation of disease risk using genetics. However, there is notable heterogeneity in the application and reporting of these risk scores, which hinders the translation of PRSs into clinical care. Here, in a collaboration between the Clinical Genome Resource (ClinGen) Complex Disease Working Group and the Polygenic Score (PGS) Catalog, we present the Polygenic Risk Score Reporting Standards (PRS-RS), in which we update the Genetic Risk Prediction Studies (GRIPS) Statement to reflect the present state of the field. Drawing on the input of experts in epidemiology, statistics, disease-specific applications, implementation and policy, this comprehensive reporting framework defines the minimal information that is needed to interpret and evaluate PRSs, especially with respect to downstream clinical applications. Items span detailed descriptions of study populations, statistical methods for the development and validation of PRSs and considerations for the potential limitations of these scores. In addition, we emphasize the need for data availability and transparency, and we encourage researchers to deposit and share PRSs through the PGS Catalog to facilitate reproducibility and comparative benchmarking. By providing these criteria in a structured format that builds on existing standards and ontologies, the use of this framework in publishing PRSs will facilitate translation into clinical care and progress towards defining best practice.

Precision Medicine and Public Health: New Challenges for Effective and Sustainable Health

The development of high-throughput omics technologies represents an unmissable opportunity for evidence-based prevention of adverse effects on human health. However, the applicability and access to multi-omics tests are limited. In Italy, this is due to the rapid increase of knowledge and the high levels of skill and economic investment initially necessary. The fields of human genetics and public health have highlighted the relevance of an implementation strategy at a national level in Italy, including integration in sanitary regulations and governance instruments. In this review, the emerging field of public health genomics is discussed, including the polygenic scores approach, epigenetic modulation, nutrigenomics, and microbiomes implications. Moreover, the Italian state of implementation is presented. The omics sciences have important implications for the prevention of both communicable and noncommunicable diseases, especially because they can be used to assess the health status during the whole course of life. An effective population health gain is possible if omics tools are implemented for each person after a preliminary assessment of effectiveness in the medium to long term.