Genome-wide association studies of cardiovascular disease.

Genome-wide association studies (GWAS) aim to identify common genetic variants that are associated with traits and diseases. Since 2005, more than 5,000 GWAS have been published for almost as many traits. These studies have offered insights into the loci and genes underlying phenotypic traits, have highlighted genetic correlations across traits and diseases, and are beginning to demonstrate clinical utility by identifying individuals at increased risk for common diseases. GWAS have been widely utilized across cardiovascular diseases and associated phenotypic traits, with insights facilitated by multicenter registry studies and large biobank data sets. In this review, we describe how GWAS have informed the genetic architecture of cardiovascular diseases and the insights they have provided into disease pathophysiology, using archetypal conditions for both common and rare diseases. We also describe how biobank data sets can complement disease-specific studies, particularly for rarer cardiovascular diseases, and how findings from GWAS have the potential to impact on clinical care. Finally, we discuss the outstanding challenges facing research in this field and how they can be addressed.

Canalization and Robustness in Human Genetics and Disease.

Canalization refers to the evolution of populations such that the number of individuals who deviate from the optimum trait, or experience disease, is minimized. In the presence of rapid cultural, environmental, or genetic change, the reverse process of decanalization may contribute to observed increases in disease prevalence. This review starts by defining relevant concepts, drawing distinctions between the canalization of populations and robustness of individuals. It then considers evidence pertaining to three continuous traits and six domains of disease. In each case, existing genetic evidence for genotype-by-environment interactions is insufficient to support a strong inference of decanalization, but we argue that the advent of genome-wide polygenic risk assessment now makes an empirical evaluation of the role of canalization in preventing disease possible. Finally, the contributions of both rare and common variants to congenital abnormality and adult onset disease are considered in light of a new kerplunk model of genetic effects.

Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward.

Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.

Multi-ancestry polygenic risk scores for venous thromboembolism.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx_combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx_combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE.

Genetic susceptibility to chronic diseases leads to heart failure among Europeans: the influence of leukocyte telomere length.

BACKGROUND: Genetic susceptibility to various chronic diseases has been shown to influence heart failure (HF) risk. However, the underlying biological pathways, particularly the role of leukocyte telomere length (LTL), are largely unknown. We investigated the impact of genetic susceptibility to chronic diseases and various traits on HF risk, and whether LTL mediates or modifies the pathways. METHODS: We conducted prospective cohort analyses on 404 883 European participants from the UK Biobank, including 9989 incident HF cases. Multivariable Cox regression was used to estimate associations between HF risk and 24 polygenic risk scores (PRSs) for various diseases or traits previously generated using a Bayesian approach. We assessed multiplicative interactions between the PRSs and LTL previously measured in the UK Biobank using quantitative PCR. Causal mediation analyses were conducted to estimate the proportion of the total effect of PRSs acting indirectly through LTL, an integrative marker of biological aging. RESULTS: We identified 9 PRSs associated with HF risk, including those for various cardiovascular diseases or traits, rheumatoid arthritis (P = 1.3E-04), and asthma (P = 1.8E-08). Additionally, longer LTL was strongly associated with decreased HF risk (P-trend = 1.7E-08). Notably, LTL strengthened the asthma-HF relationship significantly (P-interaction = 2.8E-03). However, LTL mediated only 1.13% (P < 0.001) of the total effect of the asthma PRS on HF risk. CONCLUSIONS: Our findings shed light onto the shared genetic susceptibility between HF risk, asthma, rheumatoid arthritis, and other traits. Longer LTL strengthened the genetic effect of asthma in the pathway to HF. These results support consideration of LTL and PRSs in HF risk prediction.

Genetically transitional disease: a new concept in genomic medicine.

Traditional classification of genetic diseases as monogenic and polygenic has lagged far behind scientific progress. In this opinion article, we propose and define a new terminology, genetically transitional disease (GTD), referring to cases where a large-effect mutation is necessary, but not sufficient, to cause disease. This leads to a working disease nosology based on gradients of four types of genetic architecture: monogenic, polygenic, GTD, and mixed. We present four scenarios under which GTD may occur; namely, subsets of traditionally Mendelian disease, modifiable Tier 1 monogenic conditions, variable penetrance, and situations where a genetic mutational spectrum produces qualitatively divergent pathologies. The implications of the new nosology in precision medicine are discussed, in which therapeutic options may target the molecular cause or the disease phenotype.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

SDPRX: A statistical method for cross-population prediction of complex traits.

Polygenic risk score (PRS) has demonstrated its great utility in biomedical research through identifying high-risk individuals for different diseases from their genotypes. However, the broader application of PRS to the general population is hindered by the limited transferability of PRS developed in Europeans to non-European populations. To improve PRS prediction accuracy in non-European populations, we develop a statistical method called SDPRX that can effectively integrate genome wide association study summary statistics from different populations. SDPRX automatically adjusts for linkage disequilibrium differences between populations and characterizes the joint distribution of the effect sizes of a variant in two populations to be both null, population specific, or shared with correlation. Through simulations and applications to real traits, we show that SDPRX improves the prediction performance over existing methods in non-European populations.

Fast and accurate Bayesian polygenic risk modeling with variational inference.

The advent of large-scale genome-wide association studies (GWASs) has motivated the development of statistical methods for phenotype prediction with single-nucleotide polymorphism (SNP) array data. These polygenic risk score (PRS) methods use a multiple linear regression framework to infer joint effect sizes of all genetic variants on the trait. Among the subset of PRS methods that operate on GWAS summary statistics, sparse Bayesian methods have shown competitive predictive ability. However, most existing Bayesian approaches employ Markov chain Monte Carlo (MCMC) algorithms, which are computationally inefficient and do not scale favorably to higher dimensions, for posterior inference. Here, we introduce variational inference of polygenic risk scores (VIPRS), a Bayesian summary statistics-based PRS method that utilizes variational inference techniques to approximate the posterior distribution for the effect sizes. Our experiments with 36 simulation configurations and 12 real phenotypes from the UK Biobank dataset demonstrated that VIPRS is consistently competitive with the state-of-the-art in prediction accuracy while being more than twice as fast as popular MCMC-based approaches. This performance advantage is robust across a variety of genetic architectures, SNP heritabilities, and independent GWAS cohorts. In addition to its competitive accuracy on the "White British" samples, VIPRS showed improved transferability when applied to other ethnic groups, with up to 1.7-fold increase in R2 among individuals of Nigerian ancestry for low-density lipoprotein (LDL) cholesterol. To illustrate its scalability, we applied VIPRS to a dataset of 9.6 million genetic markers, which conferred further improvements in prediction accuracy for highly polygenic traits, such as height.

Power of inclusion: Enhancing polygenic prediction with admixed individuals.

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

Dissecting the polygenic basis of atherosclerosis via disease-associated cell state signatures.

Coronary artery disease (CAD) is a pandemic disease where up to half of the risk is explained by genetic factors. Advanced insights into the genetic basis of CAD require deeper understanding of the contributions of different cell types, molecular pathways, and genes to disease heritability. Here, we investigate the biological diversity of atherosclerosis-associated cell states and interrogate their contribution to the genetic risk of CAD by using single-cell and bulk RNA sequencing (RNA-seq) of mouse and human lesions. We identified 12 disease-associated cell states that we characterized further by gene set functional profiling, ligand-receptor prediction, and transcription factor inference. Importantly, Vcam1+ smooth muscle cell state genes contributed most to SNP-based heritability of CAD. In line with this, genetic variants near smooth muscle cell state genes and regulatory elements explained the largest fraction of CAD-risk variance between individuals. Using this information for variant prioritization, we derived a hybrid polygenic risk score (PRS) that demonstrated improved performance over a classical PRS. Our results provide insights into the biological mechanisms associated with CAD risk, which could make a promising contribution to precision medicine and tailored therapeutic interventions in the future.

A perspective on genetic and polygenic risk scores-advances and limitations and overview of associated tools.

Polygenetic Risk Scores are used to evaluate an individual's vulnerability to developing specific diseases or conditions based on their genetic composition, by taking into account numerous genetic variations. This article provides an overview of the concept of Polygenic Risk Scores (PRS). We elucidate the historical advancements of PRS, their advantages and shortcomings in comparison with other predictive methods, and discuss their conceptual limitations in light of the complexity of biological systems. Furthermore, we provide a survey of published tools for computing PRS and associated resources. The various tools and software packages are categorized based on their technical utility for users or prospective developers. Understanding the array of available tools and their limitations is crucial for accurately assessing and predicting disease risks, facilitating early interventions, and guiding personalized healthcare decisions. Additionally, we also identify potential new avenues for future bioinformatic analyzes and advancements related to PRS.

Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models.

The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.

Unveiling challenges in Mendelian randomization for gene-environment interaction.

Gene-environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes genetic variants as instrumental variables (IVs) with the aim of providing a valid statistical test and estimation of causal effects in the presence of unmeasured confounders. MR has gained substantial popularity in recent years largely due to the success of genome-wide association studies. Many methods have been developed for MR; however, limited work has been done on evaluating GxE interaction. In this paper, we focus on two primary IV approaches: the two-stage predictor substitution and the two-stage residual inclusion, and extend them to accommodate GxE interaction under both the linear and logistic regression models for continuous and binary outcomes, respectively. Comprehensive simulation study and analytical derivations reveal that resolving the linear regression model is relatively straightforward. In contrast, the logistic regression model presents a considerably more intricate challenge, which demands additional effort.

The Genetics of Brugada Syndrome.

Brugada syndrome is a heritable channelopathy characterized by a peculiar electrocardiogram (ECG) pattern and increased risk of cardiac arrhythmias and sudden death. The arrhythmias originate because of an imbalance between the repolarizing and depolarizing currents that modulate the cardiac action potential. Even if an overt structural cardiomyopathy is not typical of Brugada syndrome, fibrosis and structural changes in the right ventricle contribute to a conduction slowing, which ultimately facilitates ventricular arrhythmias. Currently, Mendelian autosomal dominant transmission is detected in less than 25% of all clinical confirmed cases. Although 23 genes have been associated with the condition, only SCN5A, encoding the cardiac sodium channel, is considered clinically actionable and disease causing. The limited monogenic inheritance has pointed toward new perspectives on the possible complex genetic architecture of the disease, involving polygenic inheritance and a polygenic risk score that can influence penetrance and risk stratification.

Systematic comparison of family history and polygenic risk across 24 common diseases.

Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.

The immunogenetics of viral antigen response is associated with subtype-specific glioma risk and survival.

Glioma is a highly fatal cancer with prognostically significant molecular subtypes and few known risk factors. Multiple studies have implicated infections in glioma susceptibility, but evidence remains inconsistent. Genetic variants in the human leukocyte antigen (HLA) region modulate host response to infection and have been linked to glioma risk. In this study, we leveraged genetic predictors of antibody response to 12 viral antigens to investigate the relationship with glioma risk and survival. Genetic reactivity scores (GRSs) for each antigen were derived from genome-wide-significant (p < 5 × 10-8) variants associated with immunoglobulin G antibody response in the UK Biobank cohort. We conducted parallel analyses of glioma risk and survival for each GRS and HLA alleles imputed at two-field resolution by using data from 3,418 glioma-affected individuals subtyped by somatic mutations and 8,156 controls. Genetic reactivity scores to Epstein-Barr virus (EBV) ZEBRA and EBNA antigens and Merkel cell polyomavirus (MCV) VP1 antigen were associated with glioma risk and survival (Bonferroni-corrected p < 0.01). GRSZEBRA and GRSMCV were associated in opposite directions with risk of IDH wild-type gliomas (ORZEBRA = 0.91, p = 0.0099/ORMCV = 1.11, p = 0.0054). GRSEBNA was associated with both increased risk for IDH mutated gliomas (OR = 1.09, p = 0.040) and improved survival (HR = 0.86, p = 0.010). HLA-DQA1∗03:01 was significantly associated with decreased risk of glioma overall (OR = 0.85, p = 3.96 × 10-4) after multiple testing adjustment. This systematic investigation of the role of genetic determinants of viral antigen reactivity in glioma risk and survival provides insight into complex immunogenomic mechanisms of glioma pathogenesis. These results may inform applications of antiviral-based therapies in glioma treatment.

Integrating multiple traits for improving polygenic risk prediction in disease and pharmacogenomics GWAS.

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test.

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities.

Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

Genome-wide association study and meta-analysis of phytosterols identifies a novel locus for serum levels of campesterol.

Sitosterolemia is a rare inherited disorder caused by mutations in the ABCG5/ABCG8 genes. These genes encode proteins involved in the transport of plant sterols. Mutations in these genes lead to decreased excretion of phytosterols, which can accumulate in the body and lead to a variety of health problems, including premature coronary artery disease. We conducted the first genome-wide association study (GWAS) in the Middle East/North Africa population to identify genetic determinants of plant sterol levels in Qatari people. GWAS was performed on serum levels of ß-sitosterol and campesterol using the Metabolon platform from Qatar Biobank (QBB) and genome sequence data provided by Qatar Genome Program. A trans-ancestry meta-analysis of data from our Qatari cohort with summary statistics from a previously published large cohort (9758 subjects) of European ancestry was conducted. Using conditional analysis, we identified two independent single nucleotide polymorphisms associated with ß-sitosterol (rs145164937 and rs4299376), and two others with campesterol (rs7598542 and rs75901165) in the Qatari population in addition to previously reported variants. All of them map to the ABCG5/8 locus except rs75901165 which is located within the Intraflagellar Transport 43 (IFT43) gene. The meta-analysis replicated most of the reported variants, and our study provided significant support for the association of variants in SCARB1 and ABO with sitosterolemia. Evaluation of a polygenic risk score devised from European GWAS data showed moderate performance when applied to QBB (adjusted-R2 = 0.082). These findings provide new insights into the genetic architecture of phytosterol metabolism while showing the importance including under-represented populations in future GWAS studies.