Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics

Polygenic risk scores for autoimmune related diseases are significantly different in cancer exceptional responders

A small number of cancer patients respond exceptionally well to therapies and survive significantly longer than patients with similar diagnoses. Profiling the germline genetic backgrounds of exceptional responder (ER) patients, with extreme survival times, can yield insights into the germline polymorphisms that influence response to therapy. As ERs showed a high incidence in autoimmune diseases, we hypothesized the differences in autoimmune disease risk could reflect the immune background of ERs and contribute to better cancer treatment responses. We analyzed the germline variants of 51 ERs using polygenic risk score (PRS) analysis. Compared to typical cancer patients, the ERs had significantly elevated PRSs for several autoimmune-related diseases: type 1 diabetes, hypothyroidism, and psoriasis. This indicates that an increased genetic predisposition towards these autoimmune diseases is more prevalent among the ERs. In contrast, ERs had significantly lower PRSs for developing inflammatory bowel disease. The left-skew of type 1 diabetes score was significant for exceptional responders. Variants on genes involved in the T1D PRS model associated with cancer drug response are more likely to co-occur with other variants among ERs. In conclusion, ERs exhibited different risks for autoimmune diseases compared to typical cancer patients, which suggests that changes in a patient's immune set point or immune surveillance specificity could be a potential mechanistic link to their exceptional response. These findings expand upon previous research on immune checkpoint inhibitor-treated patients to include those who received chemotherapy or radiotherapy.

A Polygenic Risk Analysis for Identifying Ulcerative Colitis Patients with European Ancestry

The incidence of ulcerative colitis (UC) has increased globally. As a complex disease, the genetic predisposition for UC could be estimated by the polygenic risk score (PRS), which aggregates the effects of a large number of genetic variants in a single quantity and shows promise in identifying individuals at higher lifetime risk of UC. Here, based on a cohort of 2869 UC cases and 2900 controls with genotype array datasets, we used PRSice-2 to calculate PRS, and systematically analyzed factors that could affect the power of PRS, including GWAS summary statistics, population stratification, and impact of variants. After leveraging a stepwise condition analysis, we eventually established the best PRS model, achieving an AUC of 0.713. Meanwhile, samples in the top 20% of the PRS distribution had a risk of UC more than ten times higher than samples in the lowest 20% (OR = 10.435, 95% CI 8.571-12.703). Our analyses demonstrated that including population-enriched, more disease-associated SNPs and using GWAS summary statistics from similar ethnic background can improve the power of PRS. Strictly following the principle of focusing on one population in all aspects of generating PRS can be a cost-effective way to apply genotype-array-derived PRS to practical risk estimation.

A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n = 31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction

BACKGROUND

Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level.

RESULTS

We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations.

CONCLUSIONS

By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

Construction, evaluation, and AOP framework-based application of the EpPRS as a genetic surrogate for assessing environmental pollutants

BACKGROUND

Environmental pollutant measurement is essential for accurate health risk assessment. However, the detection of humans' internal exposure to pollutants is cost-intensive and consumes time and energy. Polygenic risk scores (PRSs) have been widely applied in genetic studies of complex trait diseases. It is important to construct a genetically relevant environmental surrogate for pollutant exposure and to explore its utility for disease prediction and risk assessment.

OBJECTIVES

This study enrolled 714 individuals with complete genomic data and exposomic data on 22 plasma-persistent organic pollutants (POPs).

METHODS

We first conducted 22 POP genome-wide association studies (GWAS) and constructed the corresponding environmental pollutant-based PRS (EpPRS) by clumping and P value thresholding (C + T), lassosum, and PRS-CS methods. The best-fit EpPRS was chosen by its regression R2. An adverse outcome pathway (AOP) framework was developed to assess the effects of contaminants on candidate diseases. Furthermore, Mendelian randomization (MR) analysis was performed to explore the causal association between POPs and cancer risk.

RESULTS

The C + T method produced the best-performing EpPRSs for 7 PCBs and 4 PBDEs. EpPRSs replicated the correlations of environmental exposure measurements based on consistent patterns. The diagnostic performance of type 2 diabetes mellitus (T2DM) PRS was improved by the combined model of T2DM-EpPRS of PCB126/BDE153. Finally, the AKT1-mediated AOP framework illustrated that PCB126 and BDE153 may increase the risk of T2DM by decreasing AKT1 phosphorylation through the cGMP-PKG pathway and promoting abnormal glucose homeostasis. MR analysis showed that digestive system tumors, such as colorectal cancer and biliary tract cancer, are more sensitive to POP exposure.

CONCLUSIONS

EpPRSs can serve as a proxy for assessing pollutant internal exposure. The application of the EpPRS to disease risk assessment can reveal the toxic pathway and mode of action linking exposure and disease in detail, providing a basis for the development of environmental pollutant control strategies.

Construction and evaluation of the functional polygenic risk score for gastric cancer in a prospective cohort of the European population

BACKGROUND

A polygenic risk score (PRS) derived from 112 single-nucleotide polymorphisms (SNPs) for gastric cancer has been reported in Chinese populations (PRS-112). However, its performance in other populations is unknown. A functional PRS (fPRS) using functional SNPs (fSNPs) may improve the generalizability of the PRS across populations with distinct ethnicities.

METHODS

We performed functional annotations on SNPs in strong linkage disequilibrium (LD) with the 112 previously reported SNPs to identify fSNPs that affect protein-coding or transcriptional regulation. Subsequently, we constructed an fPRS based on the fSNPs by using the LDpred2-infinitesimal model and then analyzed the performance of the PRS-112 and fPRS in the risk prediction of gastric cancer in 457,521 European participants of the UK Biobank cohort. Finally, the performance of the fPRS in combination with lifestyle factors were evaluated in predicting the risk of gastric cancer.

RESULTS

During 4,582,045 person-years of follow-up with a total of 623 incident gastric cancer cases, we found no significant association between the PRS-112 and gastric cancer risk in the European population (hazard ratio [HR] = 1.00 [95% confidence interval (CI) 0.93-1.09], P = 0.846). We identified 125 fSNPs, including seven deleterious protein-coding SNPs and 118 regulatory non-coding SNPs, and used them to constructed the fPRS-125. Our result showed that the fPRS-125 was significantly associated with gastric cancer risk (HR = 1.11 [95% CI, 1.03-1.20], P = 0.009). Compared to participants with a low fPRS-125 (bottom quintile), those with a high fPRS-125 (top quintile) had a higher risk of incident gastric cancer (HR = 1.43 [95% CI, 1.12-1.84], P = 0.005). Moreover, we observed that participants with both an unfavorable lifestyle and a high genetic risk had the highest risk of incident gastric cancer (HR = 4.99 [95% CI, 1.55-16.10], P = 0.007) compared to those with both a favorable lifestyle and a low genetic risk.

CONCLUSION

These results indicate that the fPRS-125 derived from fSNPs may act as an indicator to measure the genetic risk of gastric cancer in the European population.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction

Background

Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level.

Results

We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations.

Conclusions

By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils.

ExPRSweb: An online repository with polygenic risk scores for common health-related exposures

Complex traits are influenced by genetic risk factors, lifestyle, and environmental variables, so-called exposures. Some exposures, e.g., smoking or lipid levels, have common genetic modifiers identified in genome-wide association studies. Because measurements are often unfeasible, exposure polygenic risk scores (ExPRSs) offer an alternative to study the influence of exposures on various phenotypes. Here, we collected publicly available summary statistics for 28 exposures and applied four common PRS methods to generate ExPRSs in two large biobanks: the Michigan Genomics Initiative and the UK Biobank. We established ExPRSs for 27 exposures and demonstrated their applicability in phenome-wide association studies and as predictors for common chronic conditions. Especially the addition of multiple ExPRSs showed, for several chronic conditions, an improvement compared to prediction models that only included traditional, disease-focused PRSs. To facilitate follow-up studies, we share all ExPRS constructs and generated results via an online repository called ExPRSweb.

Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.

Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies

Polygenic scores (PGS) are important tools for carrying out genetic prediction of common diseases and disease related complex traits, facilitating the development of precision medicine. Unfortunately, despite the critical importance of PGS and the vast number of PGS methods recently developed, few comprehensive comparison studies have been performed to evaluate the effectiveness of PGS methods. To fill this critical knowledge gap, we performed a comprehensive comparison study on 12 different PGS methods through internal evaluations on 25 quantitative and 25 binary traits within the UK Biobank with sample sizes ranging from 147 408 to 336 573, and through external evaluations via 25 cross-study and 112 cross-ancestry analyses on summary statistics from multiple genome-wide association studies with sample sizes ranging from 1415 to 329 345. We evaluate the prediction accuracy, computational scalability, as well as robustness and transferability of different PGS methods across datasets and/or genetic ancestries, providing important guidelines for practitioners in choosing PGS methods. Besides method comparison, we present a simple aggregation strategy that combines multiple PGS from different methods to take advantage of their distinct benefits to achieve stable and superior prediction performance. To facilitate future applications of PGS, we also develop a PGS webserver (http://www.pgs-server.com/) that allows users to upload summary statistics and choose different PGS methods to fit the data directly. We hope that our results, method and webserver will facilitate the routine application of PGS across different research areas.

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores

MOTIVATION

Identifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores.

RESULTS

In the Lifelines population-based biobank, we calculated polygenic scores (PGSs) for 25 traits for 32 786 participants. We then applied Idéfix to compare the actual phenotypes to PGSs, and to use the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Subsequent simulations present Idéfix's potential in varying datasets with more powerful PGSs. This suggests its performance will likely improve when more highly powered GWASs for commonly measured traits will become available. Idéfix can be used to identify a set of high-quality participants for whom it is very unlikely that they reflect sample mix-ups, and for these participants we can use genetic data for clinical purposes, such as pharmacogenetic profiles. For instance, in Lifelines, we can select 34.4% of participants, reducing the sample mix-up rate from 0.15% to 0.01%.

AVAILABILITYAND IMPLEMENTATION

Idéfix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix. The individual-level data that support the findings were obtained from the Lifelines biobank under project application number ov16_0365. Data is made available upon reasonable request submitted to the LifeLines Research office (research@lifelines.nl, https://www.lifelines.nl/researcher/how-to-apply/apply-here).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic prediction of complex traits with polygenic scores: a statistical review

Accurate genetic prediction of complex traits can facilitate disease screening, improve early intervention, and aid in the development of personalized medicine. Genetic prediction of complex traits requires the development of statistical methods that can properly model polygenic architecture and construct a polygenic score (PGS). We present a comprehensive review of 46 methods for PGS construction. We connect the majority of these methods through a multiple linear regression framework which can be instrumental for understanding their prediction performance for traits with distinct genetic architectures. We discuss the practical considerations of PGS analysis as well as challenges and future directions of PGS method development. We hope our review serves as a useful reference both for statistical geneticists who develop PGS methods and for data analysts who perform PGS analysis.

Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets

Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R2 = 0.144; highest R2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts (N = 1107 K) increased prediction R2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits.

The distribution of common-variant effect sizes

The genetic effect-size distribution of a disease describes the number of risk variants, the range of their effect sizes and sample sizes that will be required to discover them. Accurate estimation has been a challenge. Here I propose Fourier Mixture Regression (FMR), validating that it accurately estimates real and simulated effect-size distributions. Applied to summary statistics for ten diseases (average [Formula: see text]), FMR estimates that 100,000-1,000,000 cases will be required for genome-wide significant SNPs to explain 50% of SNP heritability. In such large studies, genome-wide significance becomes increasingly conservative, and less stringent thresholds achieve high true positive rates if confounding is controlled. Across traits, polygenicity varies, but the range of their effect sizes is similar. Compared with effect sizes in the top 10% of heritability, including most discovered thus far, those in the bottom 10-50% are orders of magnitude smaller and more numerous, spanning a large fraction of the genome.

A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics

Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR.

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.

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.

Polygenic Risk Score-Enhanced Risk Stratification of Coronary Artery Disease in Patients With Stable Chest Pain

BACKGROUND

Polygenic risk scores (PRSs) are associated with coronary artery disease (CAD), but the clinical potential of using PRSs at the single-patient level for risk stratification has yet to be established. We investigated whether adding a PRS to clinical risk factors (CRFs) improves risk stratification in patients referred to coronary computed tomography angiography on a suspicion of obstructive CAD.

METHODS

In this prespecified diagnostic substudy of the Dan-NICAD trial (Danish study of Non-Invasive testing in Coronary Artery Disease), we included 1617 consecutive patients with stable chest symptoms and no history of CAD referred for coronary computed tomography angiography. CRFs used for risk stratification were age, sex, symptoms, prior or active smoking, antihypertensive treatment, lipid-lowering treatment, and diabetes. In addition, patients were genotyped, and their PRSs were calculated. All patients underwent coronary computed tomography angiography. Patients with a suspected ≥50% stenosis also underwent invasive coronary angiography with fractional flow reserve. A combined end point of obstructive CAD was defined as a visual invasive coronary angiography stenosis >90%, fractional flow reserve <0.80, or a quantitative coronary analysis stenosis >50% if fractional flow reserve measurements were not feasible.

RESULTS

The PRS was associated with obstructive CAD independent of CRFs (adjusted odds ratio, 1.8 [95% CI, 1.5-2.2] per SD). The PRS had an area under the curve of 0.63 (0.59-0.68), which was similar to that for age and sex. Combining the PRS with CRFs led to a CRF+PRS model with area under the curve of 0.75 (0.71-0.79), which was 0.04 more than the CRF model (P=0.0029). By using pretest probability (pretest probability) cutoffs at 5% and 15%, a net reclassification improvement of 15.8% (P=3.1×10^-4) was obtained, with a down-classification of risk in 24% of patients (211 of 862) in whom the pretest probability was 5% to 15% based on CRFs alone.

CONCLUSIONS

Adding a PRS improved risk stratification of obstructive CAD beyond CRFs, suggesting a modest clinical potential of using PRSs to guide diagnostic testing in the contemporary clinical setting. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02264717.

Polygenic risk modeling with latent trait-related genetic components

Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.

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.

Multifactorial Activation of NLRP3 Inflammasome: Relevance for a Precision Approach to Atherosclerotic Cardiovascular Risk and Disease

Chronic low-grade inflammation, through the specific activation of the NACHT leucine-rich repeat- and PYD-containing (NLRP)3 inflammasome-interleukin (IL)-1β pathway, is an important contributor to the development of atherosclerotic cardiovascular disease (ASCVD), being triggered by intracellular cholesterol accumulation within cells. Within this pathological context, this complex pathway is activated by a number of factors, such as unhealthy nutrition, altered gut and oral microbiota, and elevated cholesterol itself. Moreover, evidence from autoinflammatory diseases, like psoriasis and others, which are also associated with higher cardiovascular disease (CVD) risk, suggests that variants of NLRP3 pathway-related genes (like NLRP3 itself, caspase recruitment domain-containing protein (CARD)8, caspase-1 and IL-1β) may carry gain-of-function mutations leading, in some individuals, to a constitutive pro-inflammatory pattern. Indeed, some reports have recently associated the presence of specific single nucleotide polymorphisms (SNPs) on such genes with greater ASCVD prevalence. Based on these observations, a potential effective strategy in this context may be the identification of carriers of these NLRP3-related SNPs, to generate a genomic score, potentially useful for a better CVD risk prediction, and, possibly, for personalized therapeutic approaches targeted to the NLRP3-IL-1β pathway.