Bayesian functional data analysis over dependent regions and its application for identification of differentially methylated regions.

We consider a Bayesian functional data analysis for observations measured as extremely long sequences. Splitting the sequence into several small windows with manageable lengths, the windows may not be independent especially when they are neighboring each other. We propose to utilize Bayesian smoothing splines to estimate individual functional patterns within each window and to establish transition models for parameters involved in each window to address the dependence structure between windows. The functional difference of groups of individuals at each window can be evaluated by the Bayes factor based on Markov Chain Monte Carlo samples in the analysis. In this paper, we examine the proposed method through simulation studies and apply it to identify differentially methylated genetic regions in TCGA lung adenocarcinoma data.

Differential methylation tests of regulatory regions.

Differential methylation of regulatory elements is critical in epigenetic researches and can be statistically tested. We developed a new statistical test, the generalized integrated functional test (GIFT), that tests for regional differences in methylation based on the methylation percent at each CpG site within a genomic region. The GIFT uses estimated subject-specific profiles with smoothing methods, specifically wavelet smoothing, and calculates an ANOVA-like test to compare the average profile of groups. In this way, possibly correlated CpG sites within the regulatory region are compared all together. Simulations and analyses of data obtained from patients with chronic lymphocytic leukemia indicate that GIFT has good statistical properties and is able to identify promising genomic regions. Further, GIFT is likely to work with multiple different types of experiments since different smoothing methods can be used to estimate the profiles of data without noise. Matlab code for GIFT and sample data are available at http://www.augusta.edu/mcg/biostatepi/people/software/gift.html.

A method to detect differentially methylated loci with next-generation sequencing.

Epigenetic changes, especially DNA methylation at CpG loci have important implications in cancer and other complex diseases. With the development of next-generation sequencing (NGS), it is feasible to generate data to interrogate the difference in methylation status for genome-wide loci using case-control design. However, a proper and efficient statistical test is lacking. There are several challenges. First, unlike methylation experiments using microarrays, where there is one measure of methylation for one individual at a particular CpG site, here we have the counts of methylation allele and unmethylation allele for each individual. Second, due to the nature of sample preparation, the measured methylation reflects the methylation status of a mixture of cells involved in sample preparation. Therefore, the underlying distribution of the measured methylation level is unknown, and a robust test is more desirable than parametric approach. Third, currently NGS measures methylation at over 2 million CpG sites. Any statistical tests have to be computationally efficient in order to be applied to the NGS data. Taking these challenges into account, we propose a test for differential methylation based on clustered data analysis by modeling the methylation counts. We performed simulations to show that it is robust under several distributions for the measured methylation levels. It has good power and is computationally efficient. Finally, we apply the test to our NGS data on chronic lymphocytic leukemia. The results indicate that it is a promising and practical test.

Quantification Methods for Methylation Levels in Illumina Arrays.

The genome-wide patterns of methylation levels and identifications of differentially methylated genetic loci or regions of CpG sites are being important, because the methylome has the potential for large effects in disease etiology. There are several quantification methods of methylation level at each CpG site for a given sample, which include ß-value, M-value, and N-value. The performance of three quantification methods of methylation levels has been examined with simulation study and 27K Illumina array data for obesity.

Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements.

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses.

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.

Computational Methods for Detection of Differentially Methylated Regions Using Kernel Distance and Scan Statistics.

MOTIVATION: Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation because they play an important role in regulating gene expression without changes in the sequence of DNA. Abnormal DNA methylation is associated with many human diseases. RESULTS: We propose two different approaches to test for differentially methylated regions (DMRs) associated with complex traits, while accounting for correlations among CpG sites in the DMRs. The first approach is a nonparametric method using a kernel distance statistic and the second one is a likelihood-based method using a binomial spatial scan statistic. The kernel distance method uses the kernel function, while the binomial scan statistic approach uses a mixed-effects model to incorporate correlations among CpG sites. Extensive simulations show that both approaches have excellent control of type I error, and both have reasonable statistical power. The binomial scan statistic approach appears to have higher power, while the kernel distance method is computationally faster. The proposed methods are demonstrated using data from a chronic lymphocytic leukemia (CLL) study.

Disruption of PHF21A causes syndromic intellectual disability with craniofacial anomalies, epilepsy, hypotonia, and neurobehavioral problems including autism.

Background: PHF21A has been associated with intellectual disability and craniofacial anomalies based on its deletion in the Potocki-Shaffer syndrome region at 11p11.2 and its disruption in three patients with balanced translocations. In addition, three patients with de novo truncating mutations in PHF21A were reported recently. Here, we analyze genomic data from seven unrelated individuals with mutations in PHF21A and provide detailed clinical descriptions, further expanding the phenotype associated with PHF21A haploinsufficiency. Methods: Diagnostic trio whole exome sequencing, Sanger sequencing, use of GeneMatcher, targeted gene panel sequencing, and MiSeq sequencing techniques were used to identify and confirm variants. RT-qPCR was used to measure the normal expression pattern of PHF21A in multiple human tissues including 13 different brain tissues. Protein-DNA modeling was performed to substantiate the pathogenicity of the missense mutation. Results: We have identified seven heterozygous coding mutations, among which six are de novo (not maternal in one). Mutations include four frameshifts, one nonsense mutation in two patients, and one heterozygous missense mutation in the AT Hook domain, predicted to be deleterious and likely to cause loss of PHF21A function. We also found a new C-terminal domain composed of an intrinsically disordered region. This domain is truncated in six patients and thus likely to play an important role in the function of PHF21A, suggesting that haploinsufficiency is the likely underlying mechanism in the phenotype of seven patients. Our results extend the phenotypic spectrum of PHF21A mutations by adding autism spectrum disorder, epilepsy, hypotonia, and neurobehavioral problems. Furthermore, PHF21A is highly expressed in the human fetal brain, which is consistent with the neurodevelopmental phenotype. Conclusion: Deleterious nonsense, frameshift, and missense mutations disrupting the AT Hook domain and/or an intrinsically disordered region in PHF21A were found to be associated with autism spectrum disorder, epilepsy, hypotonia, neurobehavioral problems, tapering fingers, clinodactyly, and syndactyly, in addition to intellectual disability and craniofacial anomalies. This suggests that PHF21A is involved in autism spectrum disorder and intellectual disability, and its haploinsufficiency causes a diverse neurological phenotype.

Blood conservation operations in pediatric cardiac patients: a paradigm shift of blood use.

BACKGROUND: Red blood cell transfusion is associated with high morbidity in pediatric patients undergoing cardiac operations. The aim of this study was to evaluate the clinical effects and outcomes of blood conservation for our pediatric patients undergoing cardiac operations. METHODS: We retrospectively analyzed a collected database of 168 pediatric patients who underwent biventricular (BV) and univentricular (UV) cardiac operations from 2006 to 2010. Patients were grouped into no blood conservation (n = 86 [BV = 74, UV = 12]) and blood conservation (n = 82 [BV = 68, UV = 14]) cohorts. There were no statistical differences in age, sex, weight, and preoperative or postoperative hemoglobin levels in the BV groups. RESULTS: Even though the blood conservation group had longer cardiopulmonary bypass (CPB) (p < 0.0001) and cross-clamp times (p < 0.002) with lower hemoglobin levels (p < 0.0001), there was a decreased need for intraoperative (p < 0.0001) and postoperative blood transfusions (p < 0.018), lower inotropic scores (p < 0.0001), a decrease in ventilator days (p < 0.0009), and a shorter length of hospital stay (p < 0.0008). In the UV blood conservation group, there were no statistical differences in age, sex, weight, CPB and cross-clamp times, preoperative and postoperative hemoglobin levels, and red blood cell transfusions despite lower intraoperative hemoglobin levels (p < 0.0009) and blood transfusion (p < 0.01) requirements. There were significantly lower inotropic scores (p < 0.001) and a trend toward a shorter duration of time on the ventilator (p < 0.07) in the blood conservation group. Logistic regression analysis demonstrated a significant correlation between intraoperative blood transfusion and increased inotropic score, longer duration on the ventilator, and increased length of hospitalization. CONCLUSIONS: Blood conservation in pediatric cardiac operations is associated with fewer ventilator days, lower inotropic scores, and shorter lengths of stay. These findings, in addition to attendant risks and side effects of blood transfusion and the rising cost of safer blood products, justify blood conservation in pediatric cardiac operations.

Longitudinal Studies With Outcome-Dependent Follow-up: Models and Bayesian Regression.

We propose Bayesian parametric and semiparametric partially linear regression methods to analyze the outcome-dependent follow-up data when the random time of a follow-up measurement of an individual depends on the history of both observed longitudinal outcomes and previous measurement times. We begin with the investigation of the simplifying assumptions of Lipsitz, Fitzmaurice, Ibrahim, Gelber, and Lipshultz, and present a new model for analyzing such data by allowing subject-specific correlations for the longitudinal response and by introducing a subject-specific latent variable to accommodate the association between the longitudinal measurements and the follow-up times. An extensive simulation study shows that our Bayesian partially linear regression method facilitates accurate estimation of the true regression line and the regression parameters. We illustrate our new methodology using data from a longitudinal observational study.