The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care

All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations

Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

The acceptability and clinical impact of using polygenic scores for risk-estimation of common cancers in primary care: a systematic review

BACKGROUND

Polygenic scores (PGS) have been developed for cancer risk-estimation and show potential as tools to prompt earlier referral for high-risk individuals and aid risk-stratification within cancer screening programmes. This review explores the potential for using PGS to identify individuals at risk of the most common cancers seen in primary care.

METHODS

Two electronic databases were searched up until November 2023 to identify quantitative, qualitative, and mixed methods studies that reported on the acceptability and clinical impact of using PGS to identify individuals at highest risk of breast, prostate, colorectal and lung cancer in primary care. The Mixed Methods Appraisal Tool (MMAT) was used to assess the quality of included studies and a narrative synthesis was used to analyse data.

RESULTS

A total of 190 papers were identified, 18 of which were eligible for inclusion. A cancer risk-assessment tool incorporating PGS was acceptable to the general practice population and their healthcare providers but major challenges to implementation were identified, including lack of evidence for PGS in non-European ancestry and a need for healthcare provider education in genomic medicine. A PGS cancer risk-assessment had relatively limited impact on psychosocial outcomes and health behaviours. However, for prostate cancer, potential applications for its use in primary care were shown.

CONCLUSIONS

Cancer risk assessment incorporating PGS in primary care is acceptable to patients and healthcare providers but there is a paucity of research exploring clinical impact. Few studies were identified, and more research is required before clinical implementation of PGS can be recommended.