GCPBayes pipeline: a tool for exploring pleiotropy at the gene level.

Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data.

Variable selection for longitudinal zero-inflated power series transition model.

In modeling many longitudinal count clinical studies, the excess of zeros is a common problem. To take into account the extra zeros, the zero-inflated power series (ZIPS) models have been applied. These models assume a latent mixture model consisting of a count component and a degenerated zero component that has a unit point mass at zero. Usually, the current response measurement in a longitudinal sequence is a function of previous outcomes. For example, in a study about acute renal allograft rejection, the number of acute rejection episodes for a patient in current time is a function of this outcome at previous follow-up times. In this paper, we consider a transition model for accounting the dependence of current outcome on the previous outcomes in the presence of excess zeros. New variable selection methods for the ZIPS transition model using least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalties are proposed. An expectation-maximization (EM) algorithm using the penalized likelihood is applied for both parameters estimations and conducting variable selection. Some simulation studies are performed to investigate the performance of the proposed approach and the approach is applied to analyze a real dataset.

A two-stage approach for joint modeling of longitudinal measurements and competing risks data.

Joint modeling of longitudinal measurements and time-to-event data is used in many practical studies of medical sciences. Most of the time, particularly in clinical studies and health inquiry, there are more than one event and they compete for failing an individual. In this situation, assessing the competing risk failure time is important. In most cases, implementation of joint modeling involves complex calculations. Therefore, we propose a two-stage method for joint modeling of longitudinal measurements and competing risks (JMLC) data based on the full likelihood approach via the conditional EM (CEM) algorithm. In the first stage, a linear mixed effect model is used to estimate the parameters of the longitudinal sub-model. In the second stage, we consider a cause-specific sub-model to construct competing risks data and describe an approximation for the joint log-likelihood that uses the estimated parameters of the first stage. We express the results of a simulation study and perform this method on the "standard and new anti-epileptic drugs" trial to check the effect of drug assaying on the treatment effects of lamotrigine and carbamazepine through treatment failure.

Bayesian meta-analysis models for cross cancer genomic investigation of pleiotropic effects using group structure.

An increasing number of genome-wide association studies (GWAS) summary statistics is made available to the scientific community. Exploiting these results from multiple phenotypes would permit identification of novel pleiotropic associations. In addition, incorporating prior biological information in GWAS such as group structure information (gene or pathway) has shown some success in classical GWAS approaches. However, this has not been widely explored in the context of pleiotropy. We propose a Bayesian meta-analysis approach (termed GCPBayes) that uses summary-level GWAS data across multiple phenotypes to detect pleiotropy at both group-level (gene or pathway) and within group (eg, at the SNP level). We consider both continuous and Dirac spike and slab priors for group selection. We also use a Bayesian sparse group selection approach with hierarchical spike and slab priors that enables us to select important variables both at the group level and within group. GCPBayes uses a Bayesian statistical framework based on Markov chain Monte Carlo (MCMC) Gibbs sampling. It can be applied to multiple types of phenotypes for studies with overlapping or nonoverlapping subjects, and takes into account heterogeneity in the effect size and allows for the opposite direction of the genetic effects across traits. Simulations show that the proposed methods outperform benchmark approaches such as ASSET and CPBayes in the ability to retrieve pleiotropic associations at both SNP and gene-levels. To illustrate the GCPBayes method, we investigate the shared genetic effects between thyroid cancer and breast cancer in candidate pathways.

Post Liver Transplantation Survival and Related Prognostic Factors among Adult Recipients in Tehran Liver Transplant Center; 2002-2019.

BACKGROUND: Liver transplantation is a standard treatment for patients with end-stage liver disease (ESLD). However, with increasing demand for this treatment and limited resources, it is available only to patients who are more likely to survive. The primary aim was to determine prognostic factors for survival. METHODS: We collected data from 597 adult patients with ESLD, who received a single organ and initial orthotopic liver transplantation (OLT) in our center between 20 March 2008 and 20 March 2018. In this historical cohort study, univariate and multiple Cox model were used to determine prognostic factors of survival after transplantation. RESULTS: After a median follow-up of 825 (0-3889) days, 111 (19%) patients died. Survival rates were 88%, 85%, 82% and 79% at 90 days, 1 year, 3 years, and 5 years, respectively. Older patients (HR = 1.27; 95% CI: 1.01-1.59), presence of pre-OLT ascites (HR = 2.03; 95% CI: 1.16-3.57), pre-OLT hospitalization (HR = 1.88; 95% CI:1.02-3.46), longer operative time (HR = 1.006; 95% CI: 1.004-1.008), post-OLT dialysis (HR = 3.51; 95% CI: 2.07-5.94), cancer (HR = 2.69; 95% CI: 1.23-5.89) and AID (HR = 2.04; 95% CI: 1.17-3.56) as underlying disease versus hepatitis, and higher pre-OLT creatinine (HR = 1.67; 95% CI: 1.10-2.52) were associated with decreased survival. CONCLUSION: In this center, not only are survival outcomes excellent, but also younger patients, cases with better pre-operative health conditions, and those without complications after OLT have superior survival.

Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study.

In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

Impact of Age at Menarche on Breast Cancer: The Assessment of Recall Bias.

BACKGROUND: Our aim was to determine the association between age at menarche (AAM) and breast cancer adjusted for recall bias (misclassification) in AAM. METHODS: We have used data provided from a case-control study conducted in Iran from 2005 to 2009. The cases and controls were frequency matched based on 5-year age groups and region of residence. First, logistic regression was conducted to estimate the odds ratio (OR) and second, Bayesian analysis was applied to estimate the ORs adjusted for misclassification. RESULTS: The study was conducted on 880 cases and 998 controls. In the assumption of no correction for recall bias on self-reported AAM, the OR was 1.36 (95% Credible Interval (0.98, 1.90). Based on a sensitivity value = 71% and a specificity value = 81% (the indices about the ratio of true recall of AAM) for the case and control groups (as the first scenario), the AAM ≤ 12 years of age was associated with a lower OR for breast cancer by 1.23 (95% Credible Interval: 0.50, 3.13). In the other scenario, with consideration of 100% sensitivity and specificity of self- reported AAM in the case group, and 71% and 81% sensitivity and specificity of the item in the control group, the related OR between breast cancer and AAM was found increased to 2.96 (95% Credible Interval: 0.75, 7.66). CONCLUSION: After adjustment for misclassification related to recall bias, this study provides evidence that the self-reported mode of AAM has a moderate impact on calculation of the OR.

Serum Lipids and Cardiovascular Disease Mortality in Iranian Population: Joint Modeling of Longitudinal and Survival Data in Tehran Lipid and Glucose Study (TLGS) Cohort.

BACKGROUND: Lipid abnormalities are major risk factors of death from cardiovascular disease (CVD). As well as, lipid markers are time-dependent covariates that change with aging. Previous cohort studies have only investigated baseline measurements of lipid markers on CVD mortality. MATERIALS AND METHODS: The study sample consisted of 4,148 individuals aged over 40 years. Total cholesterol (TC), LDL-cholesterol (LDL-C), and HDL-cholesterol (HDL-C) were measured in five phases. A joint model analysis was used to investigate the association between each longitudinal lipid markers and CVD mortality in men, women and pooled sample. All analysis was performed using the survival and joint modeling packages in R 3.3.3. RESULTS: Totally, 233 CVD deaths occurred during a median follow-up of 12.4 years. For men, CVD mortality increased by 28% (confidence interval [CI]: 14%,44%) for a 10% increased in TC. For women, CVD mortality increased by 43% (CI: 22%, 68%) and 21% (CI:7%, 37%) for 10 % increase in TC and LDL-C and decreased by 18% (CI:7%, 27%) for a 10% increase in HDL-C. CONCLUSION: Association of lipid markers with CVD mortality is different in men and women, such that high levels of TC and LDL-C and low levels of HDL-C are risk factors of CVD mortality in women, but only TC is a risk factor of CVD mortality in men.

Association of lipid markers with coronary heart disease and stroke mortality: A 15-year follow-up study.

OBJECTIVES: It has been proposed that lipid markers may predict cardiovascular events; however, their effect may vary depending on the type of cardiovascular disease. The purpose of this study was to investigate the effects of lipid markers on death from coronary heart disease (CHD) and stroke in competing risks setting. MATERIALS AND METHODS: Participants included 2502 women and 2020 men, age 40 years or older from Tehran Lipid and Glucose Study. The association between total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C) with hazard and cumulative incidence of CHD and stroke was investigated using cause-specific hazard and sub-distribution hazard models. Statistical analyses were performed using "risk regression" and "cmprsk" package in R 3.3.2. RESULTS: One standard deviation (SD) increase in TC and LDL-C increased the hazard of CHD death by 1.42 (CI=1.07,1.89) and 1.41 (CI=1.04,1.93), respectively. 1-SD increase in TG increased the cumulative incidence of CHD death increased by 1.94 (CI=1.02,3.75) in women. Other risk factors were not associated with the hazard and cumulative incidence of CHD in women, men and the total sample. In addition, none of lipids had a significant effect on the hazard and cumulative incidence of stroke in men, women and the total sample. CONCLUSION: The associations of lipid components on CHD death were modified by gender. TC, LDL-C and TG were independent predictors of CHD mortality in women. Furthermore, death due to stroke changes the association of lipid markers with CHD mortality.

Bayesian sample size determination for longitudinal studies with continuous response based on different scientific questions of interest.

Longitudinal study designs are commonly applied in much scientific research, especially in the medical, social, and economic sciences. Longitudinal studies allow researchers to measure changes in each individual's responses over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. In longitudinal studies, because of the complexity of their design, including the selection of the number of individuals and the number of repeated measurements, sample size determination is less studied. This paper uses a simulation-based method to determine the sample size from a Bayesian perspective. For this purpose, several Bayesian criteria for sample size determination are used, of which the most important one is the Bayesian power criterion. We determine the sample size of a longitudinal study based on the scientific question of interest, by the choice of an appropriate model. Most of the methods of determining sample size are based on the definition of a single hypothesis. In this paper, in addition to using this method, we determine the sample size using multiple hypotheses. Using several examples, the proposed Bayesian methods are illustrated and discussed.

The impact of maternal smoking during pregnancy on childhood asthma: adjusted for exposure misclassification; results from the National Health and Nutrition Examination Survey, 2011-2012.

PURPOSE: We sought to examine the association between childhood asthma and self-reported maternal smoking during pregnancy (MSDP) after adjusting for a range of exposure misclassification scenarios using a Bayesian approach that incorporated exposure misclassification probability estimates from the literature. METHODS: Self-reported MSDP and asthma data were extracted from National Health and Nutrition Examination Survey 2011-2012. The association between self-reported MSDP and asthma was adjusted for exposure misclassification using a Bayesian bias model approach. RESULTS: We included 3074 subjects who were 1-15 years of age, including 492 asthma cases. The mean (SD) of age of the participants was 8.5 (4.1) and 7.1 (4.2) years and the number (percentage) of female was 205 (42%) and 1314 (51%) among asthmatic and nonasthmatic groups, respectively. The odds ratio (OR) for the association between self-reported MSDP and asthma in logistic regression adjusted for confounders was 1.28 (95% confidence interval: 0.92, 1.77). In a Bayesian analysis that adjusted for exposure misclassification using external data, we found different ORs between MSDP and asthma by applying different priors (posterior ORs 0.90 [95% credible interval {CRI}: 0.47, 1.60] to 3.05 [95% CRI: 1.73, 5.53] in differential and 1.22 [CRI 95%: 0.62, 2.25] to 1.60 CRI: 1.18, 2.19) in nondifferential misclassification settings. CONCLUSIONS: Given the assumptions and the accuracy of the bias model, the estimated effect of MSDP on asthma after adjusting for misclassification was strengthened in many scenarios.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.

Eliciting road traffic injuries cost among Iranian drivers' public vehicles using willingness to pay method.

BACKGROUND AND AIM: To allocate resources at the national level and ensure the safety level of roads with the aim of economic efficiency, cost calculation can help determine the size of the problem and demonstrate the economic benefits resulting from preventing such injuries. This study was carried out to elicit the cost of traffic injuries among Iranian drivers of public vehicles. MATERIALS AND METHODS: In a cross-sectional study, 410 drivers of public vehicles were randomly selected from all the drivers in city of Tehran, Iran. The research questionnaire was prepared based on the standard for willingness to pay (WTP) method (stated preference (SP), contingent value (CV), and revealed preference (RP) model). Data were collected along with a scenario for vehicle drivers. Inclusion criteria were having at least high school education and being in the age range of 18 to 65 years old. Final analysis of willingness to pay was carried out using Weibull model. RESULTS: Mean WTP was 3,337,130 IRR among drivers of public vehicles. Statistical value of life was estimated 118,222,552,601,648 IRR, for according to 4,694 dead drivers, which was equivalent to 3,940,751,753 $ based on the dollar free market rate of 30,000 IRR (purchase power parity). Injury cost was 108,376,366,437,500 IRR, equivalent to 3,612,545,548 $. In sum, injury and death cases came to 226,606,472,346,449 IRR, equivalent to 7,553,549,078 $. Moreover in 2013, cost of traffic injuries among the drivers of public vehicles constituted 1.25% of gross national income, which was 604,300,000,000$. WTP had a significant relationship with gender, daily payment, more payment for time reduction, more pay to less traffic, and minibus drivers. CONCLUSION: Cost of traffic injuries among drivers of public vehicles included 1.25% of gross national income, which was noticeable; minibus drivers had less perception of risk reduction than others.

Robust modeling of differential gene expression data using normal/independent distributions: a Bayesian approach.

In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distributions which have been used previously, but also include extensions such as the slash, the contaminated normal and the Laplace distributions. The purpose of this paper is to identify differentially expressed genes by considering these distributional assumptions instead of the normal distribution. A Bayesian approach using the Markov Chain Monte Carlo method is adopted for parameter estimation. Two publicly available gene expression data sets are analyzed using the proposed approach. The use of the robust models for detecting differentially expressed genes is investigated. This investigation shows that the choice of model for differentiating gene expression data is very important. This is due to the small number of replicates for each gene and the existence of outlying data. Comparison of the performance of these models is made using different statistical criteria and the ROC curve. The method is illustrated using some simulation studies. We demonstrate the flexibility of these robust models in identifying differentially expressed genes.

Misclassification Adjustment of Family History of Breast Cancer in a Case-Control Study: a Bayesian Approach.

BACKGROUND: Misreporting self-reported family history may lead to biased estimations. We used Bayesian methods to adjust for exposure misclassification. MATERIALS AND METHODS: A hospital-based case-control study was used to identify breast cancer risk factors among Iranian women. Three models were jointly considered; an outcome, an exposure and a measurement model. All models were fitted using Bayesian methods, run to achieve convergence. RESULTS: Bayesian analysis in the model without misclassification showed that the odds ratios for the relationship between breast cancer and a family history in different prior distributions were 2.98 (95% CRI: 2.41, 3.71), 2.57 (95% CRI: 1.95, 3.41) and 2.53 (95% CRI: 1.93, 3.31). In the misclassified model, adjusted odds ratios for misclassification in the different situations were 2.64 (95% CRI: 2.02, 3.47), 2.64 (95% CRI: 2.02, 3.46), 1.60 (95% CRI: 1.07, 2.38), 1.61 (95% CRI: 1.07, 2.40), 1.57 (95% CRI: 1.05, 2.35), 1.58 (95% CRI: 1.06, 2.34) and 1.57 (95% CRI: 1.06, 2.33). CONCLUSIONS: It was concluded that self-reported family history may be misclassified in different scenarios. Due to the lack of validation studies in Iran, more attention to this matter in future research is suggested, especially while obtaining results in accordance with sensitivity and specificity values.

Estimating cost of road traffic injuries in Iran using willingness to pay (WTP) method.

We aimed to use the willingness to pay (WTP) method to calculate the cost of traffic injuries in Iran in 2013. We conducted a cross-sectional questionnaire-based study of 846 randomly selected road users. WTP data was collected for four scenarios for vehicle occupants, pedestrians, vehicle drivers, and motorcyclists. Final analysis was carried out using Weibull and maximum likelihood method. Mean WTP was 2,612,050 Iranian rials (IRR). Statistical value of life was estimated according to 20,408 fatalities 402,314,106,073,648 IRR (US$13,410,470,202 based on purchasing power parity at (February 27th, 2014). Injury cost was US$25,637,870,872 (based on 318,802 injured people in 2013, multiple daily traffic volume of 311, and multiple daily payment of 31,030 IRR for 250 working days). The total estimated cost of injury and death cases was 39,048,341,074$. Gross national income of Iran was, US$604,300,000,000 in 2013 and the costs of traffic injuries constituted 6·46% of gross national income. WTP was significantly associated with age, gender, monthly income, daily payment, more payment for time reduction, trip mileage, drivers and occupants from road users. The costs of traffic injuries in Iran in 2013 accounted for 6.64% of gross national income, much higher than the global average. Policymaking and resource allocation to reduce traffic-related death and injury rates have the potential to deliver a huge economic benefit.

Robust joint modeling of longitudinal measurements and time to event data using normal/independent distributions: a Bayesian approach.

Joint modeling of longitudinal data and survival data has been used widely for analyzing AIDS clinical trials, where a biological marker such as CD4 count measurement can be an important predictor of survival. In most of these studies, a normal distribution is used for modeling longitudinal responses, which leads to vulnerable inference in the presence of outliers in longitudinal measurements. Powerful distributions for robust analysis are normal/independent distributions, which include univariate and multivariate versions of the Student's t, the slash and the contaminated normal distributions in addition to the normal. In this paper, a linear-mixed effects model with normal/independent distribution for both random effects and residuals and Cox's model for survival time are used. For estimation, a Bayesian approach using Markov Chain Monte Carlo is adopted. Some simulation studies are performed for illustration of the proposed method. Also, the method is illustrated on a real AIDS data set and the best model is selected using some criteria.