Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Stroke Genetics: Discovery, Insight Into Mechanisms, and Clinical Perspectives

Stroke is the second leading cause of death worldwide and a complex, heterogeneous condition. In this review, we provide an overview of the current knowledge on monogenic and multifactorial forms of stroke, highlighting recent insight into the continuum between these. We describe how, in recent years, large-scale genome-wide association studies have enabled major progress in deciphering the genetic basis for stroke and its subtypes, although more research is needed to interpret these findings. We cover the potential of stroke genetics to reveal novel pathophysiological processes underlying stroke, to accelerate the discovery of new therapeutic approaches, and to identify individuals in the population who are at high risk of stroke and could be targeted for tailored preventative interventions.

A Family and a Genome-Wide Polygenic Risk Score Are Independently Associated With Stroke in a Population-Based Study

BACKGROUND

Positive family history and genetic risk scores have been shown to independently capture those individuals with high risk for stroke. The aim of our study was to evaluate the amount of shared information between family history and genetic risk and to investigate their combined effect on the association with prevalent and incident stroke cases.

METHODS

We obtained a family risk score (FamRS), weighted for disease onset and family size as well as genome-wide polygenic risk score (PGS) including over 3.2 million single-nucleotide polymorphisms in the population-based prospective KORA F3 (Cooperative Health Research in the Region of Augsburg) study (n=3071) from Southern Germany. FamRS and PGS were evaluated separately and combined. The measures were once treated as continuous variables but also divided in the highest 20%, 10%, 5%, and 1% percentiles. Odds ratios via logistic regression and hazard ratios via Cox regression were estimated. A stroke event was defined as a hospitalization for stroke that was self-reported in a standardized interview by certified and supervised personnel.

RESULTS

The FamRS outperformed other simplified family measures such as affected parents or number of affected family members. FamRS and PGS were not correlated, and no individuals were observed with both very high FamRS and very high PGS (top 1% percentile). In a combined model, both FamRS and PGS were independently from each other associated with risk of stroke, also independent of other traditional risk factors (p [FamRS]=0.02, p [PGS]=0.005). Individuals in the top 1% of either FamRS or PGS were found to have >5-fold risk for stroke (odds ratios, 5.82 [95% CI, 2.08-14]; P=0.0002). The results for incident stroke events showed the same trend but were not significant.

CONCLUSIONS

Our study shows that a family risk score and PGS capture different information concerning individual stroke risk. Combining the risk measures FamRS and PGS increases predictive power, as demonstrated in a population-based study.

Evaluating risk for alcohol use disorder: Polygenic risk scores and family history

BACKGROUND

Early identification of individuals at high risk for alcohol use disorder (AUD) coupled with prompt interventions could reduce the incidence of AUD. In this study, we investigated whether Polygenic Risk Scores (PRS) can be used to evaluate the risk for AUD and AUD severity (as measured by the number of DSM-5 AUD diagnostic criteria met) and compared their performance with a measure of family history of AUD.

METHODS

We studied individuals of European ancestry from the Collaborative Study on the Genetics of Alcoholism (COGA). DSM-5 diagnostic criteria were available for 7203 individuals, of whom 3451 met criteria for DSM-IV alcohol dependence or DSM-5 AUD and 1616 were alcohol-exposed controls aged ≥21 years with no history of AUD or drug dependence. Further, 4842 individuals had a positive first-degree family history of AUD (FH+), 2722 had an unknown family history (FH?), and 336 had a negative family history (FH-). PRS were derived from a meta-analysis of a genome-wide association study of AUD from the Million Veteran Program and scores from the problem subscale of the Alcohol Use Disorders Identification Test in the UK Biobank. We used mixed models to test the association between PRS and risk for AUD and AUD severity.

RESULTS

AUD cases had higher PRS than controls with PRS increasing as the number of DSM-5 diagnostic criteria increased (p-values ≤ 1.85E-05 ) in the full COGA sample, the FH+ subsample, and the FH? subsample. Individuals in the top decile of PRS had odds ratios (OR) for developing AUD of 1.96 (95% CI: 1.54 to 2.51, p-value = 7.57E-08 ) and 1.86 (95% CI: 1.35 to 2.56, p-value = 1.32E-04 ) in the full sample and the FH+ subsample, respectively. These values are comparable to previously reported ORs for a first-degree family history (1.91 to 2.38) estimated from national surveys. PRS were also significantly associated with the DSM-5 AUD diagnostic criterion count in the full sample, the FH+ subsample, and the FH? subsample (p-values ≤6.7E-11 ). PRS remained significantly associated with AUD and AUD severity after accounting for a family history of AUD (p-values ≤6.8E-10 ).

CONCLUSIONS

Both PRS and family history were associated with AUD and AUD severity, indicating that these risk measures assess distinct aspects of liability to AUD traits.

10 Years of GWAS discovery in endometrial cancer: Aetiology, function and translation

Endometrial cancer is a common gynaecological cancer with increasing incidence and mortality. In the last decade, endometrial cancer genome-wide association studies (GWAS) have provided a resource to explore aetiology and for functional interpretation of heritable risk variation, informing endometrial cancer biology. Indeed, GWAS data have been used to assess relationships with other traits through correlation and Mendelian randomisation analyses, establishing genetic relationships and potential risk factors. Cross-trait GWAS analyses have increased statistical power and identified novel endometrial cancer risk variation related to other traits. Functional analysis of risk loci has helped prioritise candidate susceptibility genes, revealing molecular mechanisms and networks. Lastly, risk scores generated using endometrial cancer GWAS data may allow for clinical translation through identification of patients at high risk of disease. In the next decade, this knowledge base should enable substantial progress in our understanding of endometrial cancer and, potentially, new approaches for its screening and treatment.

A polygenic risk score improves risk stratification of coronary artery disease: a large-scale prospective Chinese cohort study

Aims

To construct a polygenic risk score (PRS) for coronary artery disease (CAD) and comprehensively evaluate its potential in clinical utility for primary prevention in Chinese populations.

Methods and results

Using meta-analytic approach and large genome-wide association results for CAD and CAD-related traits in East Asians, a PRS comprising 540 genetic variants was developed in a training set of 2800 patients with CAD and 2055 controls, and was further assessed for risk stratification for CAD integrating with the guideline-recommended clinical risk score in large prospective cohorts comprising 41 271 individuals. During a mean follow-up of 13.0 years, 1303 incident CAD cases were identified. Individuals with high PRS (the highest 20%) had about three-fold higher risk of CAD than the lowest 20% (hazard ratio 2.91, 95% confidence interval 2.43–3.49), with the lifetime risk of 15.9 and 5.8%, respectively. The addition of PRS to the clinical risk score yielded a modest yet significant improvement in C-statistic (1%) and net reclassification improvement (3.5%). We observed significant gradients in both 10-year and lifetime risk of CAD according to the PRS within each clinical risk strata. Particularly, when integrating high PRS, intermediate clinical risk individuals with uncertain clinical decision for intervention would reach the risk levels (10-year of 4.6 vs. 4.8%, lifetime of 17.9 vs. 16.6%) of high clinical risk individuals with intermediate (20–80%) PRS.

Conclusion

The PRS could stratify individuals into different trajectories of CAD risk, and further refine risk stratification for CAD within each clinical risk strata, demonstrating a great potential to identify high-risk individuals for targeted intervention in clinical utility.

Neural network-based integration of polygenic and clinical information: development and validation of a prediction model for 10-year risk of major adverse cardiac events in the UK Biobank cohort

BACKGROUND

In primary cardiovascular disease prevention, early identification of high-risk individuals is crucial. Genetic information allows for the stratification of genetic predispositions and lifetime risk of cardiovascular disease. However, towards clinical application, the added value over clinical predictors later in life is crucial. Currently, this genotype-phenotype relationship and implications for overall cardiovascular risk are unclear.

METHODS

In this study, we developed and validated a neural network-based risk model (NeuralCVD) integrating polygenic and clinical predictors in 395 713 cardiovascular disease-free participants from the UK Biobank cohort. The primary outcome was the first record of a major adverse cardiac event (MACE) within 10 years. We compared the NeuralCVD model with both established clinical scores (SCORE, ASCVD, and QRISK3 recalibrated to the UK Biobank cohort) and a linear Cox-Model, assessing risk discrimination, net reclassification, and calibration over 22 spatially distinct recruitment centres.

FINDINGS

The NeuralCVD score was well calibrated and improved on the best clinical baseline, QRISK3 (ΔConcordance index [C-index] 0·01, 95% CI 0·009-0·011; net reclassification improvement (NRI) 0·0488, 95% CI 0·0442-0·0534) and a Cox model (ΔC-index 0·003, 95% CI 0·002-0·004; NRI 0·0469, 95% CI 0·0429-0·0511) in risk discrimination and net reclassification. After adding polygenic scores we found further improvements on population level (ΔC-index 0·006, 95% CI 0·005-0·007; NRI 0·0116, 95% CI 0·0066-0·0159). Additionally, we identified an interaction of genetic information with the pre-existing clinical phenotype, not captured by conventional models. Additional high polygenic risk increased overall risk most in individuals with low to intermediate clinical risk, and age younger than 50 years.

INTERPRETATION

Our results demonstrated that the NeuralCVD score can estimate cardiovascular risk trajectories for primary prevention. NeuralCVD learns the transition of predictive information from genotype to phenotype and identifies individuals with high genetic predisposition before developing a severe clinical phenotype. This finding could improve the reprioritisation of otherwise low-risk individuals with a high genetic cardiovascular predisposition for preventive interventions.

FUNDING

Charité-Universitätsmedizin Berlin, Einstein Foundation Berlin, and the Medical Informatics Initiative.

Mendelian randomization study on the causal effects of tumor necrosis factor inhibition on coronary artery disease and ischemic stroke among the general population

Background

Tumor necrosis factor (TNF) is a potent inflammatory cytokine that has been causally associated with coronary artery disease (CAD) and ischemic stroke (IS), implying opportunities for disease prevention by anti-TNF therapeutics.

Methods

Leveraging summary statistics of several genome-wide association studies (GWAS), we assessed the repurposing potential of TNF inhibitors for CAD and IS using drug-target Mendelian randomization (MR) design. Pharmacologic blockade of the pro-inflammatory TNF signalling mediated by TNF receptor 1 (TNFR1) was instrumented by four validated variants. Causal effects of TNF/TNFR1 blockade on CAD (N_case/control upto 122,733/424,528) and IS (N_case/control upto 60,341/454,450) were then estimated via various MR estimators using circulating C-reactive protein (CRP; N_GWAS=204,402) as downstream biomarker to reflect treatment effect. Associations of a functional variant, rs1800693, with CRP, CAD and IS were also examined.

Findings

No protective effect of TNF/TNFR1 inhibition on CAD or IS was observed. For every 10% decrease of circulating CRP achieved by TNF/TNFR1 blockade, odds ratio was 0.98 (95% confidence interval [CI]: 0.60-1.60) for CAD and 0.77 (95% CI: 0.36-1.63) for IS. Findings remained null in all supplement analyses.

Interpretation

Our findings do not support TNFR1 as a promising target for CAD or IS prevention among the general population. Future research is warranted to investigate whether the detrimental effect of circulating TNF on CAD and IS might be counteracted by modulating other relevant drug targets.

Funding

No.

Genetic Contributors of Incident Stroke in 10,700 African Americans With Hypertension: A Meta-Analysis From the Genetics of Hypertension Associated Treatments and Reasons for Geographic and Racial Differences in Stroke Studies

Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals.

Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs aiming to evaluate cross-ethnic performance.

Results: We identified 10 statistically significant (p < 5.00E-08) and 90 additional suggestive (p < 1.00E-06) variants associated with incident stroke in the meta-analysis. Six of the top 10 variants were located in an intergenic region on chromosome 18 (LINC01443-LOC644669). Additional variants of interest were located in or near the COL12A1, SNTG1, PCDH7, TMTC1, and NTM genes. Replication was conducted in the Warfarin Pharmacogenomics Cohort (WPC), and while none of the variants were directly validated, seven intronic variants of NTM proximal to our target variants, had a p-value <5.00E-04 in the WPC. The inclusion of the PRS did not improve the prediction accuracy compared to a reference model adjusting for age, sex, and genetic ancestry in either study and had lower predictive accuracy compared to models accounting for established stroke risk factors. These results demonstrate the necessity for PRS derivation in AAs, particularly for diseases that affect AAs disproportionately.

Conclusion: This study highlights biologically plausible genetic determinants for incident stroke in hypertensive AAs. Ultimately, a better understanding of genetic risk factors for stroke in AAs may give new insight into stroke burden and potential clinical tools for those among the highest at risk.

Genetics of common cerebral small vessel disease

Cerebral small vessel disease (cSVD) is a leading cause of ischaemic and haemorrhagic stroke and a major contributor to dementia. Covert cSVD, which is detectable with brain MRI but does not manifest as clinical stroke, is highly prevalent in the general population, particularly with increasing age. Advances in technologies and collaborative work have led to substantial progress in the identification of common genetic variants that are associated with cSVD-related stroke (ischaemic and haemorrhagic) and MRI-defined covert cSVD. In this Review, we provide an overview of collaborative studies - mostly genome-wide association studies (GWAS) - that have identified >50 independent genetic loci associated with the risk of cSVD. We describe how these associations have provided novel insights into the biological mechanisms involved in cSVD, revealed patterns of shared genetic variation across cSVD traits, and shed new light on the continuum between rare, monogenic and common, multifactorial cSVD. We consider how GWAS summary statistics have been leveraged for Mendelian randomization studies to explore causal pathways in cSVD and provide genetic evidence for drug effects, and how the combination of findings from GWAS with gene expression resources and drug target databases has enabled identification of putative causal genes and provided proof-of-concept for drug repositioning potential. We also discuss opportunities for polygenic risk prediction, multi-ancestry approaches and integration with other omics data.

Polygenic risk scores for cardiovascular diseases and type 2 diabetes

Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual's risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.

Polygenic Risk Score to Identify Subclinical Coronary Heart Disease Risk in Young Adults

BACKGROUND

Polygenic risk scores (PRS) may enhance risk stratification for coronary heart disease among young adults. Whether a coronary heart disease PRS improves prediction beyond modifiable risk factors in this population is not known.

METHODS

Genotyped adults aged 18 to 35 years were selected from the CARDIA study (Coronary Artery Risk Development in Young Adults; n=1132) and FOS (Framingham Offspring Study; n=663). Systolic blood pressure, total and HDL (high-density lipoprotein) cholesterol, triglycerides, smoking, and waist circumference or body mass index were measured at the visit 1 exam of each study, and coronary artery calcium, a measure of coronary atherosclerosis, was assessed at year 15 (CARDIA) or year 30 (FOS). A previously validated PRS for coronary heart disease was computed for each subject. The C statistic and integrated discrimination improvement were used to compare improvements in prediction of elevated coronary artery calcium between models containing the PRS, risk factors, or both.

RESULTS

There were 62 (5%) and 93 (14%) participants with a coronary artery calcium score >20 (CARDIA) and >300 (FOS), respectively. At these thresholds, the C statistic changes of adding the PRS to a risk factor-based model were 0.015 (0.004-0.028) and 0.020 (0.001-0.039) in CARDIA and FOS, respectively. When adding risk factors to a PRS-based model, the respective changes were 0.070 (0.033-0.109) and 0.051 (0.017-0.079). The integrated discrimination improvement, when adding the PRS to a risk factor model, was 0.027 (-0.006 to 0.054) in CARDIA and 0.039 (0.0005-0.072) in FOS.

CONCLUSIONS

Among young adults, a PRS improved model discrimination for coronary atherosclerosis, but improvements were smaller than those associated with modifiable risk factors.

Obesity-related genetic determinants of stroke

As obesity, circulating lipids and other vascular/metabolic factors influence the risk of stroke, we examined if genetic variants associated with these conditions are related to risk of stroke using a case-control study in Galicia, Spain. A selection of 200 single-nucleotide polymorphisms previously found to be related to obesity, body mass index, circulating lipids, type 2 diabetes, heart failure, obesity-related cancer and cerebral infarction were genotyped in 465 patients diagnosed with stroke and 480 population-based controls. An unsupervised Lasso regression procedure was carried out for single-nucleotide polymorphism selection based on their potential effect on stroke according to obesity. Selected genotypes were further analysed through multivariate logistic regression to study their association with risk of stroke. Using unsupervised selection procedures, nine single-nucleotide polymorphisms were found to be related to risk of stroke overall and after stratification by obesity. From these, rs10761731, rs2479409 and rs6511720 in obese subjects [odds ratio (95% confidence interval) = 0.61 (0.39-0.95) (P = 0.027); 0.54 (0.35-0.84) (P = 0.006) and 0.42 (0.22-0.80) (P = 0.0075), respectively], and rs865686 in non-obese subjects [odds ratio (95% confidence interval) = 0.67 (0.48-0.94) (P = 0.019)], were independently associated with risk of stroke after multivariate logistic regression procedures. The associations between the three single-nucleotide polymorphisms found to be associated with stroke risk in obese subjects were more pronounced among females; for rs10761731, odds ratios among obese males and females were 1.07 (0.58-1.97) (P = 0.84), and 0.31 (0.14-0.69) (P = 0.0018), respectively; for rs2479409, odd ratios were 0.66 (0.34-1.27) (P = 0.21), and 0.49 (0.24-0.99) (P = 0.04), for obese males and females, respectively; the stroke-rs6511720 association was also slightly more pronounced among obese females, odds ratios were 0.33 (0.13-0.87) (P = 0.022), and 0.28 (0.09-0.85) (P = 0.02) for obese males and females, respectively. The rs865686-stroke association was more pronounced among non-obese males [odds ratios = 0.61 (0.39-0.96) (P = 0.029) and 0.72 (0.42-1.22) (P = 0.21), for non-obese males and females, respectively]. A combined genetic score of variants rs10761731, rs2479409 and rs6511720 was highly predictive of stroke risk among obese subjects (P = 2.04 × 10^-5), particularly among females (P = 4.28 × 10^-6). In summary, single-nucleotide polymorphisms rs1076173, rs2479409 and rs6511720 were found to independently increase the risk of stroke in obese subjects after adjustment for established risk factors. A combined score with the three genomic variants was an independent predictor of risk of stroke among obese subjects in our population.

A genetic risk score using human chromosomal-scale length variation can predict schizophrenia

Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539-0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.

Reproducibility in the UK biobank of genome-wide significant signals discovered in earlier genome-wide association studies

With the establishment of large biobanks, discovery of single nucleotide variants (SNVs, also known as single nucleotide polymorphisms (SNVs)) associated with various phenotypes has accelerated. An open question is whether genome-wide significant SNVs identified in earlier genome-wide association studies (GWAS) are replicated in later GWAS conducted in biobanks. To address this, we examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, “replication” GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNVs (of which 6289 reached P < 5e−8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0%; although lower for binary than quantitative phenotypes (58.1% versus 94.8% respectively). There was a 18.0% decrease in SNV effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNV effect size, phenotype trait (binary or quantitative), and discovery P value, we built and validated a model that predicted SNV replication with area under the Receiver Operator Curve = 0.90. While non-replication may reflect lack of power rather than genuine false-positives, these results provide insights about which discovered associations are likely to be replicated across subsequent GWAS.

Technological readiness and implementation of genomic-driven precision medicine for complex diseases

The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.

Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control

AIMS/HYPOTHESIS

Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk.

METHODS

We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study.

RESULTS

The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10^-21 and p = 9.6 × 10^-31, respectively) and a 4.4-fold (p = 6.8 × 10^-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years.

CONCLUSIONS/INTERPRETATION

This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.

Genetics of Intracranial Aneurysms

Rupture of an intracranial aneurysm leads to aneurysmal subarachnoid hemorrhage, a severe type of stroke which is, in part, driven by genetic variation. In the past 10 years, genetic studies of IA have boosted the number of known genetic risk factors and improved our understanding of the disease. In this review, we provide an overview of the current status of the field and highlight the latest findings of family based, sequencing, and genome-wide association studies. We further describe opportunities of genetic analyses for understanding, prevention, and treatment of the disease.

Using Human Genetics to Understand Mechanisms in Ischemic Stroke Outcome: From Early Brain Injury to Long-Term Recovery

There is a critical need to elucidate molecular mechanisms underlying brain injury, repair, and recovery following ischemic stroke-a global health problem with major social and economic impact. Despite 5 decades of intensive research, there are no widely accepted neuroprotective drugs that mitigate ischemic brain injury, or neuroreparative drugs, or personalized approaches that guide therapies to enhance recovery. We here explore novel reverse translational approaches that will complement traditional forward translational methods in identifying mechanisms relevant to human stroke outcome. Although genome-wide association studies have yielded over 30 genetic loci that influence ischemic stroke risk, only a few genome-wide association studies have been performed for stroke outcome. We discuss important considerations for genetic studies of ischemic stroke outcome-including carefully designed phenotypes that capture injury/recovery mechanisms, anchored in time to stroke onset. We also address recent genome-wide association studies that provide insight into mechanisms underlying brain injury and repair. There are several ongoing initiatives exploring genomic associations with novel phenotypes related to stroke outcome. To improve the understanding of the genetic architecture of ischemic stroke outcome, larger studies using standardized phenotypes, preferably embedded in standard-of-care measures, are needed. Novel techniques beyond genome-wide association studies-including exploiting informatics, multi-omics, and novel analytics-promise to uncover genetic and molecular pathways from which drug targets and other new interventions may be identified.

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.

Stroke Genetics: Turning Discoveries into Clinical Applications

The field of medical and population genetics in stroke is moving at a rapid pace and has led to unanticipated opportunities for discovery and clinical applications. Genome-wide association studies have highlighted the role of specific pathways relevant to etiologically defined subtypes of stroke and to stroke as a whole. They have further offered starting points for the exploration of novel pathways and pharmacological strategies in experimental systems. Mendelian randomization studies continue to provide insights in the causal relationships between exposures and outcomes and have become a useful tool for predicting the efficacy and side effects of drugs. Additional applications that have emerged from recent discoveries include risk prediction based on polygenic risk scores and pharmacogenomics. Among the topics currently moving into focus is the genetics of stroke outcome. While still at its infancy, this field is expected to boost the development of neuroprotective agents. We provide a brief overview on recent progress in these areas.

Advancing drug discovery using the power of the human genome

Human genetics plays an increasingly important role in drug development and population health. Here we review the history of human genetics in the context of accelerating the discovery of therapies, present examples of how human genetics evidence supports successful drug targets, and discuss how polygenic risk scores could be beneficial in various clinical settings. We highlight the value of direct-to-consumer platforms in the era of fast-paced big data biotechnology, and how diverse genetic and health data can benefit society. © 2021 23andMe, Inc. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

NOTCH3 variants are more common than expected in the general population and associated with stroke and vascular dementia: an analysis of 200 000 participants

BACKGROUND

Cysteine-altering NOTCH3 variants identical to those causing the rare monogenic form of stroke, CADASIL (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy), have been reported more common than expected in the general population, but their clinical significance and contribution to stroke and dementia risk in the community remain unclear.

METHODS

Cysteine-altering NOTCH3 variants were identified in UK Biobank whole-exome sequencing data (N=200 632). Frequency of stroke, vascular dementia and other clinical features of CADASIL, and MRI white matter hyperintensity volume were compared between variant carriers and non-carriers. MRIs from those with variants were visually rated, each matched with three controls.

RESULTS

Of 200 632 participants with exome sequencing data available, 443 (~1 in 450) carried 67 different cysteine-altering NOTCH3 variants. After adjustment for various covariates, NOTCH3 variant carriers had increased risk of stroke (OR: 2.33, p=0.0004) and vascular dementia (OR: 5.00, p=0.007), and increased white matter hyperintensity volume (standardised difference: 0.52, p<0.001) and white matter ultrastructural damage on diffusion MRI (standardised difference: 0.72, p<0.001). On visual analysis of MRIs from 47 carriers and 148 matched controls, variants were associated with presence of lacunes (OR: 5.97, p<0.001) and cerebral microbleeds (OR: 4.38, p<0.001). White matter hyperintensity prevalence was most increased in the anterior temporal lobes (OR: 7.65, p<0.001) and external capsule (OR: 13.32, p<0.001).

CONCLUSIONS

Cysteine-changing NOTCH3 variants are more common in the general population than expected from CADASIL prevalence and are risk factors for apparently 'sporadic' stroke and vascular dementia. They are associated with MRI changes of small vessel disease, in a distribution similar to that seen in CADASIL.

Modifiable and Non-Modifiable Risk Factors for Atherothrombotic Ischemic Stroke among Subjects in the Malmö Diet and Cancer Study

Risk factors for ischemic stroke is suggested to differ by etiologic subtypes. The purpose of this study was to examine the associations between modifiable and non-modifiable risk factors and atherothrombotic stroke (i.e., excluding cardioembolic stroke), and to examine if the potential benefit of modifiable lifestyle factors differs among subjects with and without predisposing comorbidities. After a median follow-up of 21.2 years, 2339 individuals were diagnosed with atherothrombotic stroke out of 26,547 study participants from the Malmö Diet and Cancer study. Using multivariable Cox regression, we examined non-modifiable (demographics and family history of stroke), semi-modifiable comorbidities (hypertension, dyslipidemia, diabetes mellitus and atherosclerotic disease), and modifiable (smoking, body mass index, diet quality, physical activity, and alcohol intake) risk factors in relation to atherothrombotic stroke. Higher age, male gender, family history of stroke, and low educational level increased the risk of atherothrombotic stroke as did predisposing comorbidities. Non-smoking (hazard ratio (HR) = 0.62, 95% confidence interval (CI) 0.56–0.68), high diet quality (HR = 0.83, 95% CI 0.72–0.97) and high leisure-time physical activity (HR = 0.89, 95% CI 0.80–0.98) decreased the risk of atherothrombotic ischemic stroke independent of established risk factors, with non-significant associations with body mass index and alcohol intake. The effect of the lifestyle factors was independent of predisposing comorbidities at baseline. The adverse effects of several cardiovascular risk factors were confirmed in this study of atherothrombotic stroke. Smoking cessation, improving diet quality and increasing physical activity level is likely to lower risk of atherothrombotic stroke in the general population as well as in patient groups at high risk.

Combining Clinical and Polygenic Risk Improves Stroke Prediction Among Individuals With Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is associated with a five-fold increased risk of ischemic stroke. A portion of this risk is heritable; however, current risk stratification tools (CHA2DS2-VASc) do not include family history or genetic risk. We hypothesized that we could improve ischemic stroke prediction in patients with AF by incorporating polygenic risk scores (PRS).

METHODS

Using data from the largest available genome-wide association study in Europeans, we combined over half a million genetic variants to construct a PRS to predict ischemic stroke in patients with AF. We externally validated this PRS in independent data from the UK Biobank, both independently and integrated with clinical risk factors. The integrated PRS and clinical risk factors risk tool had the greatest predictive ability.

RESULTS

Compared with the currently recommended risk tool (CHA2DS2-VASc), the integrated tool significantly improved Net Reclassification Index (2.3% [95% CI, 1.3%-3.0%]) and fit (χ2P=0.002). Using this improved tool, >115 000 people with AF would have improved risk classification in the United States. Independently, PRS was a significant predictor of ischemic stroke in patients with AF prospectively (hazard ratio, 1.13 per 1 SD [95% CI, 1.06-1.23]). Lastly, polygenic risk scores were uncorrelated with clinical risk factors (Pearson correlation coefficient, -0.018).

CONCLUSIONS

In patients with AF, there appears to be a significant association between PRS and risk of ischemic stroke. The greatest predictive ability was found with the integration of PRS and clinical risk factors; however, the prediction of stroke remains challenging.

Predictive Performance of a Polygenic Risk Score for Incident Ischemic Stroke in a Healthy Older Population

[Figure: see text].

Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

OBJECTIVE

To construct a polygenic risk score (PRS) for stroke and evaluate its utility in risk stratification and primary prevention for stroke.

METHODS

Using a meta-analytic approach and large genome-wide association results for stroke and stroke-related traits in East Asians, we generated a combined PRS (metaPRS) by incorporating 534 genetic variants in a training set of 2,872 patients with stroke and 2,494 controls. We then validated its association with incident stroke using Cox regression models in large Chinese population-based prospective cohorts comprising 41,006 individuals.

RESULTS

During a total of 367,750 person-years (mean follow-up 9.0 years), 1,227 participants developed stroke before age 80 years. Individuals with high polygenic risk had an about 2-fold higher risk of incident stroke compared with those with low polygenic risk (hazard ratio [HR] 1.99, 95% confidence interval [CI] 1.66-2.38), with the lifetime risk of stroke being 25.2% (95% CI 22.5%-27.7%) and 13.6% (95% CI 11.6%-15.5%), respectively. Individuals with both high polygenic risk and family history displayed lifetime risk as high as 41.1% (95% CI 31.4%-49.5%). Individuals with high polygenic risk achieved greater benefits in terms of absolute risk reductions from adherence to ideal fasting blood glucose and total cholesterol than those with low polygenic risk. Maintaining favorable cardiovascular health (CVH) profile could substantially mitigate the increased risk conferred by high polygenic risk to the level of low polygenic risk (from 34.6% to 13.2%).

CONCLUSIONS

Our metaPRS has great potential for risk stratification of stroke and identification of individuals who may benefit more from maintaining ideal CVH.

CLASSIFICATION OF EVIDENCE

This study provides Class I evidence that metaPRS is predictive of stroke risk.

Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

Stroke is an example of a complex and multi-factorial disease involving multiple organs, timescales, and disease mechanisms. To deal with this complexity, and to realize Precision Medicine of stroke, mathematical models are needed. Such approaches include: 1) machine learning, 2) bioinformatic network models, and 3) mechanistic models. Since these three approaches have complementary strengths and weaknesses, a hybrid modelling approach combining them would be the most beneficial. However, no concrete approach ready to be implemented for a specific disease has been presented to date. In this paper, we both review the strengths and weaknesses of the three approaches, and propose a roadmap for hybrid modelling in the case of stroke care. We focus on two main tasks needed for the clinical setting: a) For stroke risk calculation, we propose a new two-step approach, where non-linear mixed effects models and bioinformatic network models yield biomarkers which are used as input to a machine learning model and b) For simulation of care scenarios, we propose a new four-step approach, which revolves around iterations between simulations of the mechanistic models and imputations of non-modelled or non-measured variables. We illustrate and discuss the different approaches in the context of Precision Medicine for stroke.

Genomic risk score provides predictive performance for type 2 diabetes in the UK biobank

AIMS

Type 2 diabetes (T2D) is affected by a combination of genetic and environmental factors. However, the comprehensive genomic risk scores (GRSs) for T2D prediction have not been evaluated.

METHODS

Using a meta-scoring approach, we developed a metaGRS for T2D; T2D-related traits consist of 1,692 genetic variants in the UK Biobank training set (n = 40,423 + 7,558 events) and evaluate this score in the validation set (n = 303,053).

RESULTS

The hazard ratio (HR) for T2D was 1.32 (95% confidence interval [CI]: 1.29-1.35) per standard deviation of metaGRS and was larger than previously published T2D-GRS. Individuals, in the top 25% of metaGRS, have an HR of 2.08 (95%CI: 1.93-2.23) compared with those in the bottom 25%. The addition of metaGRS to all conventional risk factors significantly increased the AUC (P < 0.001). Adding metaGRS to all conventional risk factors significantly improved the reclassification accuracy (continuous net reclassification improvement = 11.8%, 95%CI: 9.2%-14.2%). All analyses adjusted for age, sex, and 10PCs.

CONCLUSIONS

The metaGRS significantly improves T2D prediction ability.

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

We present the Polygenic Score (PGS) Catalog (https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

Polygenic Risk Scores Augment Stroke Subtyping

Objective

To determine whether the polygenic risk score (PRS) derived from MEGASTROKE is associated with ischemic stroke (IS) and its subtypes in an independent tertiary health care system and to identify the PRS derived from gene sets of known biological pathways associated with IS.

Methods

Controls (n = 19,806/7,484, age ≥69/79 years) and cases (n = 1,184/951 for discovery/replication) of acute IS with European ancestry and clinical risk factors were identified by leveraging the Geisinger Electronic Health Record and chart review confirmation. All Geisinger MyCode patients with age ≥69/79 years and without any stroke-related diagnostic codes were included as low risk control. Genetic heritability and genetic correlation between Geisinger and MEGASTROKE (EUR) were calculated using the summary statistics of the genome-wide association study by linkage disequilibrium score regression. All PRS for any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS) were constructed by PRSice-2.

Results

A moderate heritability (10%–20%) for Geisinger sample as well as the genetic correlation between MEGASTROKE and the Geisinger cohort was identified. Variation of all 5 PRS significantly explained some of the phenotypic variations of Geisinger IS, and the R² increased by raising the cutoff for the age of controls. PRSLAS, PRSCES, and PRSSVS derived from low-frequency common variants provided the best fit for modeling (R² = 0.015 for PRSLAS). Gene sets analyses highlighted the association of PRS with Gene Ontology terms (vascular endothelial growth factor, amyloid precursor protein, and atherosclerosis). The PRSLAS, PRSCES, and PRSSVS explained the most variance of the corresponding subtypes of Geisinger IS suggesting shared etiologies and corroborated Geisinger TOAST subtyping.

Conclusions

We provide the first evidence that PRSs derived from MEGASTROKE have value in identifying shared etiologies and determining stroke subtypes.

Polygenic risk scores in cardiovascular risk prediction: A cohort study and modelling analyses

BACKGROUND

Polygenic risk scores (PRSs) can stratify populations into cardiovascular disease (CVD) risk groups. We aimed to quantify the potential advantage of adding information on PRSs to conventional risk factors in the primary prevention of CVD.

METHODS AND FINDINGS

Using data from UK Biobank on 306,654 individuals without a history of CVD and not on lipid-lowering treatments (mean age [SD]: 56.0 [8.0] years; females: 57%; median follow-up: 8.1 years), we calculated measures of risk discrimination and reclassification upon addition of PRSs to risk factors in a conventional risk prediction model (i.e., age, sex, systolic blood pressure, smoking status, history of diabetes, and total and high-density lipoprotein cholesterol). We then modelled the implications of initiating guideline-recommended statin therapy in a primary care setting using incidence rates from 2.1 million individuals from the Clinical Practice Research Datalink. The C-index, a measure of risk discrimination, was 0.710 (95% CI 0.703-0.717) for a CVD prediction model containing conventional risk predictors alone. Addition of information on PRSs increased the C-index by 0.012 (95% CI 0.009-0.015), and resulted in continuous net reclassification improvements of about 10% and 12% in cases and non-cases, respectively. If a PRS were assessed in the entire UK primary care population aged 40-75 years, assuming that statin therapy would be initiated in accordance with the UK National Institute for Health and Care Excellence guidelines (i.e., for persons with a predicted risk of ≥10% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), then it could help prevent 1 additional CVD event for approximately every 5,750 individuals screened. By contrast, targeted assessment only among people at intermediate (i.e., 5% to <10%) 10-year CVD risk could help prevent 1 additional CVD event for approximately every 340 individuals screened. Such a targeted strategy could help prevent 7% more CVD events than conventional risk prediction alone. Potential gains afforded by assessment of PRSs on top of conventional risk factors would be about 1.5-fold greater than those provided by assessment of C-reactive protein, a plasma biomarker included in some risk prediction guidelines. Potential limitations of this study include its restriction to European ancestry participants and a lack of health economic evaluation.

CONCLUSIONS

Our results suggest that addition of PRSs to conventional risk factors can modestly enhance prediction of first-onset CVD and could translate into population health benefits if used at scale.

LDpred2: better, faster, stronger

Motivation

Polygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance.

Results

Here, we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a ‘sparse’ option that can learn effects that are exactly 0, and an ‘auto’ option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that LDpred2 provides more accurate polygenic scores when run genome-wide, instead of per chromosome.

Availability and implementation

LDpred2 is implemented in R package bigsnpr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction

Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores.

Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients With Cardiometabolic Disease

BACKGROUND

Genome-wide association studies have identified single-nucleotide polymorphisms that are associated with an increased risk of stroke. We sought to determine whether a genetic risk score (GRS) could identify subjects at higher risk for ischemic stroke after accounting for traditional clinical risk factors in 5 trials across the spectrum of cardiometabolic disease.

METHODS

Subjects who had consented for genetic testing and who were of European ancestry from the ENGAGE AF-TIMI 48 (Effective Anticoagulation with Factor Xa Next Generation in Atrial Fibrillation), SOLID-TIMI 52 (Stabilization of Plaques Using Darapladib), SAVOR-TIMI 53 (Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus), PEGASUS-TIMI 54 (Prevention of Cardiovascular Events in Patients With Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin), and FOURIER (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Patients With Elevated Risk) trials were included in this analysis. A set of 32 single-nucleotide polymorphisms associated with ischemic stroke was used to calculate a GRS in each patient and identify tertiles of genetic risk. A Cox model was used to calculate hazard ratios for ischemic stroke across genetic risk groups, adjusted for clinical risk factors.

RESULTS

In 51 288 subjects across the 5 trials, a total of 960 subjects had an ischemic stroke over a median follow-up period of 2.5 years. After adjusting for clinical risk factors, a higher GRS was strongly and independently associated with increased risk for ischemic stroke (P trend=0.009). In comparison with individuals in the lowest third of the GRS, individuals in the middle and top tertiles of the GRS had adjusted hazard ratios of 1.15 (95% CI, 0.98-1.36) and 1.24 (95% CI 1.05-1.45) for ischemic stroke, respectively. Stratification into subgroups revealed that the performance of the GRS appeared stronger in the primary prevention cohort with an adjusted hazard ratio for the top versus lowest tertile of 1.27 (95% CI, 1.04-1.53), in comparison with an adjusted hazard ratio of 1.06 (95% CI, 0.81-1.41) in subjects with previous stroke. In an exploratory analysis of patients with atrial fibrillation and CHA₂DS₂-VASc score of 2, high genetic risk conferred a 4-fold higher risk of stroke and an absolute risk equivalent to those with CHA₂DS₂-VASc score of 3.

CONCLUSIONS

Across a broad spectrum of subjects with cardiometabolic disease, a 32-single-nucleotide polymorphism GRS was a strong, independent predictor of ischemic stroke. In patients with atrial fibrillation but lower CHA₂DS₂-VASc scores, the GRS identified patients with risk comparable to those with higher CHA₂DS₂-VASc scores.

Development of a Dynamic Diagnosis Grading System for Infertility Using Machine Learning

IMPORTANCE

Many indicators need to be considered when judging the condition of patients with infertility, which makes diagnosis and treatment complicated.

OBJECTIVE

To construct a dynamic scoring system for infertility to assist clinicians in efficiently and accurately assessing the condition of patients with infertility.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study reviewed 95 868 medical records of couples with infertility in which women had undergone in vitro fertilization and embryo transfer at the Reproductive Center of Tongji Medical College, Huazhong University of Science and Technology, in Wuhan, Hubei, China, from January 2006 to May 2019. A dynamic diagnosis and grading system for infertility was constructed. The analysis was conducted between May 20, 2019, and April 15, 2020.

MAIN OUTCOMES AND MEASURES

Patients were divided into pregnant and nonpregnant groups according to eventual pregnancy results. The evaluation index system was constructed based on the test results of the significant difference between the 2 groups of indicators and the clinician's experience. Random forest machine learning was used to determine the weight of the index, and the entropy-based feature discretization algorithm classified the abnormality of the index and the patient's condition. A 10-fold cross-validation method was used to test the validity of the system.

RESULTS

A total of 60 648 couples with infertility were enrolled, in which 15 021 women became pregnant, with a mean (SD) age of 30.30 (4.02) years. A total of 45 627 couples were in the nonpregnant group, with a mean (SD) age among women of 32.17 (5.58) years. Seven indicators were selected to build the dynamic grading system for patients with infertility: age, body mass index, follicle-stimulating hormone level, antral follicle count, anti-Mullerian hormone level, number of oocytes, and endometrial thickness. The importance weight of each indicator obtained by the random forest algorithm was 0.1748 for age, 0.0785 for body mass index, 0.0581 for follicle-stimulating hormone level, 0.1214 for antral follicle count, 0.1616 for anti-Mullerian hormone level, 0.2307 for number of oocytes, and 0.1749 for endometrial thickness. The grading system divided the condition of the patient with infertility into 5 grades from A to E. The worst E grade represented a 0.90% pregnancy rate, and the pregnancy rate in the A grade was 53.82%. The cross-validation results showed that the stability of the system was 95.94% (95% CI, 95.14%-96.74%).

CONCLUSIONS AND RELEVANCE

This machine learning-derived algorithm may assist clinicians in making an efficient and accurate initial judgment on the condition of patients with infertility.

Genomic risk scores for juvenile idiopathic arthritis and its subtypes

OBJECTIVES

Juvenile idiopathic arthritis (JIA) is an autoimmune disease and a common cause of chronic disability in children. Diagnosis of JIA is based purely on clinical symptoms, which can be variable, leading to diagnosis and treatment delays. Despite JIA having substantial heritability, the construction of genomic risk scores (GRSs) to aid or expedite diagnosis has not been assessed. Here, we generate GRSs for JIA and its subtypes and evaluate their performance.

METHODS

We examined three case/control cohorts (UK, US-based and Australia) with genome-wide single nucleotide polymorphism (SNP) genotypes. We trained GRSs for JIA and its subtypes using lasso-penalised linear models in cross-validation on the UK cohort, and externally tested it in the other cohorts.

RESULTS

The JIA GRS alone achieved cross-validated area under the receiver operating characteristic curve (AUC)=0.670 in the UK cohort and externally-validated AUCs of 0.657 and 0.671 in the US-based and Australian cohorts, respectively. In logistic regression of case/control status, the corresponding odds ratios (ORs) per standard deviation (SD) of GRS were 1.831 (1.685 to 1.991) and 2.008 (1.731 to 2.345), and were unattenuated by adjustment for sex or the top 10 genetic principal components. Extending our analysis to JIA subtypes revealed that the enthesitis-related JIA had both the longest time-to-referral and the subtype GRS with the strongest predictive capacity overall across data sets: AUCs 0.82 in UK; 0.84 in Australian; and 0.70 in US-based. The particularly common oligoarthritis JIA also had a GRS that outperformed those for JIA overall, with AUCs of 0.72, 0.74 and 0.77, respectively.

CONCLUSIONS

A GRS for JIA has potential to augment clinical JIA diagnosis protocols, prioritising higher-risk individuals for follow-up and treatment. Consistent with JIA heterogeneity, subtype-specific GRSs showed particularly high performance for enthesitis-related and oligoarthritis JIA.

Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

Aim: To develop robust and accurate prognostic biomarkers to help clinicians optimize therapeutic strategies. Materials & methods: Differentially prognosis-related autophagy genes were identified by bioinformatics analysis method. Results: Seven prognosis-related autophagy genes were more significantly related to the prognosis of hepatocellular carcinoma (HCC). Functional enrichment analysis demonstrated that these genes were mainly enriched in the autophagy pathway. BIRC5, HSPB8 and TMEM74 exhibited significant prognostic value for HCC. Besides, the risk score and BIRC5 have significant significance with clinicopathological significance of HCC. Conclusion: The research has identified a number of prognosis-related autophagy genes that associated with the survival and clinical stage of HCC. In addition, the prognostic model can be used to calculate the patient's risk score and these prognosis-related autophagy genes might serve as therapeutic targets.

Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples

Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability. There is growing evidence that PRS may prove useful for early identification of those at increased risk for certain diseases. The current potential of PRS for alcohol use disorders (AUD) remains an open question. Using data from both a population-based sample [the FinnTwin12 (FT12) study] and a high-risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of (1) alcohol dependence/alcohol problems, (2) alcohol consumption, and (3) risky behaviors with AUD and other substance use disorder (SUD) criteria. These PRSs explain ~2.5-3.5% of the variance in AUD (across FT12 and COGA) when all PRSs are included in the same model. Calculations of area under the curve (AUC) show PRS provide only a slight improvement over a model with age, sex, and ancestral principal components as covariates. While individuals in the top 20, 10, and 5% of the PRS distribution had greater odds of having an AUD compared to the lower end of the continuum in both COGA and FT12, the point estimates at each threshold were statistically indistinguishable. Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis and opioid) SUD criteria. PRSs are associated with risk for SUD in independent samples. However, usefulness for identifying those at increased risk in their current form is modest, at best. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.

Tripeptides Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP) Regulate the Proliferation and Migration of Vascular Smooth Muscle Cells by Interfering Ang II-Induced Human Umbilical Vein Endothelial Cells Derived EVs Delivering RNAs to VSMCs in the Co-culture Model

Angiotensin II (Ang II), a vasoactive factor in the renin-angiotensin-aldosterone system (RAAS), can regulate vasoconstriction and promote multiple vascular diseases. In this study, the effects of potent antihypertensive peptide Val-Pro-Pro (VPP) and Ile-Pro-Pro (IPP) on the proliferation and migration of vascular smooth muscle cells (VSMCs) by extracellular vesicles (EVs) from vascular endothelial cells (VECs) were studied using a cell co-culture model. The VEC-derived EVs were isolated, characterized, and investigated. The present study demonstrated that the EVs from Ang II-induced VECs could promote proliferation, migration, and inflammatory factors (IL-6 increased to 40.75 ± 4.33 pg/mL and IL-1β increased to 28.62 ± 5.42 pg/mL) generation of VSMCs, VPP and IPP exerted discrepant inhibitory effects on this pathway. The EVs with RNase treatment lost the effects on VSMCs, indicating that the RNAs packed into vesicles may be a critical component. These results implied that VPP and IPP could alleviate Ang II-induced vascular dysfunction by modulating the EV-mediated transmission of RNAs between VECs and VSMCs.