A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits

As increasing sample sizes from genome-wide association studies (GWASs), polygenic risk scores (PRSs) have shown great potential in personalized medicine with disease risk prediction, prevention and treatment. However, the PRS constructed using European samples becomes less accurate when it is applied to individuals from non-European populations. It is an urgent task to improve the accuracy of PRSs in under-represented populations, such as African populations and East Asian populations. In this article, we propose a cross-population and cross-phenotype (XPXP) method for construction of PRSs in under-represented populations. XPXP can construct accurate PRSs by leveraging biobank-scale datasets in European populations and multiple GWASs of genetically correlated phenotypes. XPXP also allows to incorporate population-specific and phenotype-specific effects, and thus further improves the accuracy of PRS. Through comprehensive simulation studies and real data analysis, we demonstrated that our XPXP outperformed existing PRS approaches. We showed that the height PRSs constructed by XPXP achieved 9% and 18% improvement over the runner-up method in terms of predicted R2 in East Asian and African populations, respectively. We also showed that XPXP substantially improved the stratification ability in identifying individuals at high genetic risk of type 2 diabetes. The XPXP software and all analysis code are available at github.com/YangLabHKUST/XPXP. Supplementary data are available at Bioinformatics online.

Genetic Risk Prediction for CKD: A Journey of a Thousand Miles

INTRODUCTION:Genome-wide association studies (GWAS) have identified 80+ genetic loci associated with Alzheimer’s disease (AD), enabling the development of polygenic risk scores (PRS). However, the predictive accuracy of PRS in diverse populations remains low. Here, we evaluated the predictive accuracy of single-, multi-, and cross-ancestry AD-PRS models across multi-ancestral populations.METHODS:We used AD GWAS summary statistics from European, African, Amerindian, and East Asian populations to construct AD-PRS for each target population. Model performance was assessed by estimating odds ratios, R2, and AUC.RESULTS:The cross-ancestry Bayesian PRS model demonstrated the highest predictive performance in non-European populations. It was significantly associated with poorer cognitive function, lower Aβ42 CSF levels, and the most severe category of Aβ and tau neuropathological burden, as well as a clinical AD latent variable in a multi-ancestral validation cohort.DISCUSSION:Inclusive genetic datasets and cross-ancestry PRS models are needed to enhance the transportability of AD-PRS across multi-ancestral populations.

BackgroundType 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations.MethodsWe integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts.ResultsThe trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5–4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance.ConclusionsBy integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

The aim of this systematic review is to identify all genome-wide association study (GWAS)-based polygenic risk score (PRS) studies (with different PRS approaches) reported in African ancestry populations diagnosed with any type of cancer. Additionally, this review assessed the role of PRS in advancing precision medicine through its clinical utility across different cancer types in African populations. We searched PubMed from January 2009 to April 2023 and included GWAS-based PRS studies for cancer patients of African genetic ancestry. Among the 33 eligible studies, prostate cancer and breast cancer were the most common types in adults, whereas only one publication reported the risk association of neuroblastoma (a pediatric cancer). The most common PRS approach used was ancestry-specific PRS. Clinical utility of the calculated PRS varies across cancer types, with inconsistent results. Our systematic review found a limited number of PRS studies on cancer patients (adult and pediatric) of African ancestry, and these studies showed less clinical utility compared to those conducted in European ancestry populations. To make PRS clinically actionable for African ancestry populations, it is crucial to increase the number of large-scale, population-specific GWAS, improve the representation of African-ancestry cohorts, and refine PRS models to better reflect the genetic diversity within African populations.

Although many polygenic risk scores (PRS) for cardiovascular traits have been developed in European populations, it is an urgent task to construct a PRS and to evaluate its ability in non-European populations. We developed a genome-wide PRS for blood pressure in a Japanese population and examined the associations between this PRS and hypertension prevalence. We performed a cross-sectional study in 11 252 Japanese individuals who participated in the J-MICC (Japan Multi-Institutional Collaborative Cohort) study. Using publicly available GWAS summary statistics from Biobank Japan, we developed the PRS in the target data (n=7876). With >30 000 single nucleotide polymorphisms, we evaluated PRS performance in the test data (n=3376). Hypertension was defined as systolic blood pressure of 130 mm Hg or more, or diastolic blood pressure of 85 mm Hg or more, or taking an antihypertensive drug. Compared with the middle PRS quintile, the prevalence of hypertension at the top PRS quintile was higher independently from traditional risk factors (odds ratio, 1.73 [95% CI, 1.32-2.27]). The difference of mean systolic blood pressure and diastolic blood pressure between the middle and the top PRS quintile was 4.55 (95% CI, 2.26-6.85) and 2.32 (95% CI, 0.86-3.78) mm Hg, respectively. Subgroups reflecting combinations of Japanese PRS and modifiable lifestyles and factors (smoking, alcohol intake, sedentary time, and obesity) were associated with the prevalence of hypertension. A European-derived PRS was not associated with hypertension in our participants. A PRS for blood pressure was significantly associated with hypertension and BP traits in a general Japanese population. Our findings also highlighted the importance of a combination of PRS and risk factors for identifying high-risk subgroups.

Association of asthma and its genetic predisposition with the risk of severe COVID-19

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

Decision letter: Polygenic risk scores for the prediction of common cancers in East Asians: A population-based prospective cohort study

Funding Acknowledgements Type of funding sources: None. Background A polygenic risk score (PRS) is derived from a genome-wide association study (GWAS) and represents an aggregate of thousands of single-nucleotide polymorphisms (SNPs) that provide a baseline estimate of an individual’s genetic risk for a specific disease or trait at birth. Cardiometabolic disease represents a set of disease processes that historically have disproportionally affected underrepresented racial and minority groups. Furthermore, these groups represent a population generally not well captured by traditional risk scores compared to European cohorts. Since the first GWAS studying myocardial infarction was published, PRSs have increasingly been seen as a promising tool to improve risk stratification of non-European populations. However, how PRSs can be best used in clinical practice remains unclear. Purpose To provide an overview of the PRSs related to cardiometabolic disease, analyze the ancestral diversity of GWAS cohorts, and discuss the evidence supporting their clinical applications. Methods The Preferred Reporting Items For Systematic Reviews and Meta-analysis extension for Scoping Reviews protocol was used to conduct a scoping review of the MEDLINE, EMBASE, and CENTRAL databases. English studies that published a PRS related to atrial fibrillation (AF), cerebrovascular disease (CVD), coronary artery disease (CAD), dyslipidemia, heart failure, heritable cardiomyopathy, hypertension, and type 2 diabetes were reviewed. Results Across the 4,863 studies screened, 82 articles met the inclusion criteria. The most common PRS related to CAD, followed by hypertension and CVD. Limited ancestral diversity was observed as most studies (56) included only individuals of European ancestry. A smaller proportion of studies (16) published PRSs derived in multi-ancestry cohorts. Only ten studies published a PRS derived solely from a sample population of non-European ancestry (Chinese, East Asian, Japanese, and Korean). The predictive performance of most PRSs was similar to or superior to traditional risk factors. More than half of the included studies (42) reported an integrated risk model combining a PRS with traditional risk factors or a clinical risk tool (FRS, PCE, CHADS2). The integrated risk model consistently improved predictive accuracy, but few studies investigated the performance in a non-European population. Conclusion In conclusion, this scoping review is the first of its kind and reports strong evidence for the clinical use of PRSs in AF, CAD, CVD, and hypertension. However, most PRSs are derived in cohorts of European ancestry, which contributes to a lack of PRS transferability across different ancestral groups, likely exacerbating health inequities. Future prospective studies should focus on further establishing the clinical utility of PRSs. Additionally, diversity in future GWAS cohorts is essential to ensure that PRSs reflect the multi-ancestry society at large.

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 (release v7) 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 evaluated effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. 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 particularly 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, and larger heritability estimates in African ancestry populations, such as neutrophil count (R2: 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.

Decision letter: Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome

Editor's evaluation: Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome

The First Baby Born After Polygenic Embryo Screening

Polygenic score: An anchor holding the whole life course.