Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods

Zhai, Song; Zhang, Hong; Mehrotra, Devan V.; Shen, Judong

doi:10.1038/s41467-022-32407-9

Cited by 29 publications

(42 citation statements)

References 46 publications

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“…Finally, we did not analyze the genetic factors that may influence cardiovascular risk, which is still another limitation of our study. Several genetic polymorphisms are currently known to be associated with higher concentrations of plasma lipids (i.e., polymorphisms in the APOE gene determining LDL-c levels), blood glucose (i.e., polymorphisms in the TCF7L2 gene), body mass index (i.e., polymorphisms in the FTO gene), and other cardiovascular risk factors [ 179 , 180 , 181 , 182 , 183 ]. More recently, associations of cardiovascular risk factors with microbiota-related polymorphisms have been reported [ 184 ].…”

Section: Discussionmentioning

confidence: 99%

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

et al. 2022

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Trace elements are micronutrients that are required in very small quantities through diet but are crucial for the prevention of acute and chronic diseases. Despite the fact that initial studies demonstrated inverse associations between some of the most important essential trace elements (Zn, Cu, Se, and Mn) and cardiovascular disease, several recent studies have reported a direct association with cardiovascular risk factors due to the fact that these elements can act as both antioxidants and pro-oxidants, depending on several factors. This study aims to investigate the association between plasma and urine concentrations of trace elements and cardiovascular risk factors in a general population from the Mediterranean region, including 484 men and women aged 18–80 years and considering trace elements individually and as joint exposure. Zn, Cu, Se, and Mn were determined in plasma and urine using an inductively coupled plasma mass spectrometer (ICP-MS). Single and combined analysis of trace elements with plasma lipid, blood pressure, diabetes, and anthropometric variables was undertaken. Principal component analysis, quantile-based g-computation, and calculation of trace element risk scores (TERS) were used for the combined analyses. Models were adjusted for covariates. In single trace element models, we found statistically significant associations between plasma Se and increased total cholesterol and systolic blood pressure; plasma Cu and increased triglycerides and body mass index; and urine Zn and increased glucose. Moreover, in the joint exposure analysis using quantile g-computation and TERS, the combined plasma levels of Zn, Cu, Se (directly), and Mn (inversely) were strongly associated with hypercholesterolemia (OR: 2.03; 95%CI: 1.37–2.99; p < 0.001 per quartile increase in the g-computation approach). The analysis of urine mixtures revealed a significant relationship with both fasting glucose and diabetes (OR: 1.91; 95%CI: 1.01–3.04; p = 0.046). In conclusion, in this Mediterranean population, the combined effect of higher plasma trace element levels (primarily Se, Cu, and Zn) was directly associated with elevated plasma lipids, whereas the mixture effect in urine was primarily associated with plasma glucose. Both parameters are relevant cardiovascular risk factors, and increased trace element exposures should be considered with caution.

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Section: Discussionmentioning

confidence: 99%

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

et al. 2022

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“…16 A detailed introduction of the study population, genotyping, genotype quality control (QC), and imputation for the GWAS analyses were described in previous studies. 17,18 After variant-level QC and imputation, there were 9,407,967 variants and 6502 subjects available for analysis. After filtering out subjects who had a cardiovascular event prior to our analysis time point (month 1) to avoid interference unrelated to treatment on LDL-C, a total of 5661 unrelated European subjects were included in the final analysis.…”

Section: Application: Improve-it Pgx Gwas Datamentioning

confidence: 99%

“…We further demonstrate the utility of MAJAR by applying it to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial (IMPROVE-IT). [16][17][18] Under the threshold of Fdr < 0.05, we identified 13 variants associated with the treatment-related low-density lipoprotein cholesterol (LDL-C) reduction or change from baseline, 12 of which are missed by only testing for the main effect (i.e. while using MAMBA method).…”

Section: Introductionmentioning

confidence: 99%

Statistical assessment of biomarker replicability using MAJAR method

Xie

Zhai

Jiang

et al. 2023

Stat Methods Med Res

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In the era of precision medicine, many biomarkers have been discovered to be associated with drug efficacy and safety responses, which can be used for patient stratification and drug response prediction. Due to the small sample size and limited power of randomized clinical studies, meta-analysis is usually conducted to aggregate all available studies to maximize the power for identifying prognostic and predictive biomarkers. However, it is often challenging to find an independent study to replicate the discoveries from the meta-analysis (e.g. meta-analysis of pharmacogenomics genome-wide association studies (PGx GWAS)), which seriously limits the potential impacts of the discovered biomarkers. To overcome this challenge, we develop a novel statistical framework, MAJAR (meta-analysis of joint effect associations for biomarker replicability assessment), to jointly test prognostic and predictive effects and assess the replicability of identified biomarkers by implementing an enhanced expectation–maximization algorithm and calculating their posterior-probability-of-replicabilities and Bayesian false discovery rates (Fdr). Extensive simulation studies were conducted to compare the performance of MAJAR and existing methods in terms of Fdr, power, and computational efficiency. The simulation results showed improved statistical power with well-controlled Fdr of MAJAR over existing methods and robustness to outliers under different data generation processes. We further demonstrated the advantages of MAJAR over existing methods by applying MAJAR to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial. Compared to testing main effects only, MAJAR identified 12 novel variants associated with the treatment-related low-density lipoprotein cholesterol reduction from baseline.

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“…IMPROVE-IT is a multi-center, double-blind, phase 3b randomized trial to establish the efficacy and safety of Vytorin (ezetimibe + simvastatin tablet) versus simvastatin monotherapy in high-risk subjects [16]. A detailed introduction of the study population, genotyping, genotype quality control (QC), and imputation for the GWAS analyses were described in previous studies [17,18]. After variant-level QC and imputation, there were 9,407,967 variants and 6,502 subjects available for analysis.…”

Section: Application To Improve-it Pgx Gwas Datamentioning

confidence: 99%

“…Our extensive simulation studies show that MAJAR outperforms other methods when both prognostic and predictive effects affect the phenotype of interest. We further demonstrate the utility of MAJAR by applying it to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial (IMPROVE-IT) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Statistical Assessment of Biomarker Replicability using MAJAR Method

Xie

Zhai

Jiang

et al. 2022

Preprint

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In the era of precision medicine, many biomarkers have been discovered to be associated with drug efficacy and safety responses, which can be used for patient stratification and drug response prediction. Due to small sample size and limited power of randomized clinical studies, meta-analysis is usually conducted to aggregate all available studies to maximize the power for identifying prognostic and predictive biomarkers. Since all available data are already aggregated, it is often challenging to find an independent study to replicate the discoveries from the meta-analysis (e.g., in meta-analysis of pharmacogenomics genome-wide association studies (PGx GWAS)), which seriously limits the potential impacts of the discovered biomarkers. To overcome this challenge, we develop a novel statistical framework, MAJAR (Meta-Analysis of Joint effect Associations for biomarker Replicability assessment), to jointly test prognostic and predictive effects and assess the replicability of identified biomarkers by implementing an enhanced Expectation-Maximization algorithm and calculating their posterior-probability-of-replicabilities (PPR) and Bayesian false discovery rates (Fdr). Extensive simulation studies were conducted to compare the performance of MAJAR and existing methods in terms of Fdr, power, and computational efficiency. The simulation results showed improved statistical power with well-controlled Fdr of MAJAR over existing methods and robustness to outliers under different data generation processes while considering both prognostic and predictive effects in the model. We further demonstrated the advantages of MAJAR over existing methods by applying MAJAR to the PGx GWAS summary statistics data from a large cardiovascular randomized clinical trial (IMPROVE-IT). Compared to testing main effects only, MAJAR identified 12 novel variants associated with the treatment-related LDL cholesterol (LDL-C) reduction from baseline.

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Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods

Cited by 29 publications

References 46 publications

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

Single and Combined Associations of Plasma and Urine Essential Trace Elements (Zn, Cu, Se, and Mn) with Cardiovascular Risk Factors in a Mediterranean Population

Statistical assessment of biomarker replicability using MAJAR method

Statistical Assessment of Biomarker Replicability using MAJAR Method

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