SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

Sparse estimation in semiparametric finite mixture of varying coefficient regression models.

Finite mixture of regressions (FMR) are commonly used to model heterogeneous effects of covariates on a response variable in settings where there are unknown underlying subpopulations. FMRs, however, cannot accommodate situations where covariates' effects also vary according to an "index" variable-known as finite mixture of varying coefficient regression (FM-VCR). Although complex, this situation occurs in real data applications: the osteocalcin (OCN) data analyzed in this manuscript presents a heterogeneous relationship where the effect of a genetic variant on OCN in each hidden subpopulation varies over time. Oftentimes, the number of covariates with varying coefficients also presents a challenge: in the OCN study, genetic variants on the same chromosome are considered jointly. The relative proportions of hidden subpopulations may also change over time. Nevertheless, existing methods cannot provide suitable solutions for accommodating all these features in real data applications. To fill this gap, we develop statistical methodologies based on regularized local-kernel likelihood for simultaneous parameter estimation and variable selection in sparse FM-VCR models. We study large-sample properties of the proposed methods. We then carry out a simulation study to evaluate the performance of various penalties adopted for our regularized approach and ascertain the ability of a BIC-type criterion for estimating the number of subpopulations. Finally, we applied the FM-VCR model to analyze the OCN data and identified several covariates, including genetic variants, that have age-dependent effects on OCN.

A hidden markov model for identifying differentially methylated sites in bisulfite sequencing data.

DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called "DMCHMM" which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. Our proposed method is different from other HMM methods since it profiles methylation of each sample separately, hence exploiting inter-CpG autocorrelation within samples, and it is more flexible than previous approaches by allowing multiple hidden states. Using simulations, we show that DMCHMM has the best performance among several competing methods. An analysis of cell-separated blood methylation profiles is also provided.

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model.

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models.

Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

Analyzing aberrant DNA methylation in Colorectal cancer uncovered intangible heterogeneity of gene effects in the survival time of patients.

Colorectal cancer (CRC) involves epigenetic alterations. Irregular gene-methylation alteration causes and advances CRC tumor growth. Detecting differentially methylated genes (DMGs) in CRC and patient survival time paves the way to early cancer detection and prognosis. However, CRC data including survival times are heterogeneous. Almost all studies tend to ignore the heterogeneity of DMG effects on survival. To this end, we utilized a sparse estimation method in the finite mixture of accelerated failure time (AFT) regression models to capture such heterogeneity. We analyzed a dataset of CRC and normal colon tissues and identified 3,406 DMGs. Analysis of overlapped DMGs with several Gene Expression Omnibus datasets led to 917 hypo- and 654 hyper-methylated DMGs. CRC pathways were revealed via gene ontology enrichment. Hub genes were selected based on Protein-Protein-Interaction network including SEMA7A, GATA4, LHX2, SOST, and CTLA4, regulating the Wnt signaling pathway. The relationship between identified DMGs/hub genes and patient survival time uncovered a two-component mixture of AFT regression model. The genes NMNAT2, ZFP42, NPAS2, MYLK3, NUDT13, KIRREL3, and FKBP6 and hub genes SOST, NFATC1, and TLE4 were associated with survival time in the most aggressive form of the disease that can serve as potential diagnostic targets for early CRC detection.

Analyzing aberrant DNA methylation in colorectal cancer uncovered intangible heterogeneity of gene effects in the survival time of patients.

Colorectal cancer (CRC) involves epigenetic alterations. Irregular gene-methylation alteration causes and advances CRC tumor growth. Detecting differentially methylated genes (DMGs) in CRC and patient survival time paves the way to early cancer detection and prognosis. However, CRC data including survival times are heterogeneous. Almost all studies tend to ignore the heterogeneity of DMG effects on survival. To this end, we utilized a sparse estimation method in the finite mixture of accelerated failure time (AFT) regression models to capture such heterogeneity. We analyzed a dataset of CRC and normal colon tissues and identified 3406 DMGs. Analysis of overlapped DMGs with several Gene Expression Omnibus datasets led to 917 hypo- and 654 hyper-methylated DMGs. CRC pathways were revealed via gene ontology enrichment. Hub genes were selected based on Protein-Protein-Interaction network including SEMA7A, GATA4, LHX2, SOST, and CTLA4, regulating the Wnt signaling pathway. The relationship between identified DMGs/hub genes and patient survival time uncovered a two-component mixture of AFT regression model. The genes NMNAT2, ZFP42, NPAS2, MYLK3, NUDT13, KIRREL3, and FKBP6 and hub genes SOST, NFATC1, and TLE4 were associated with survival time in the most aggressive form of the disease that can serve as potential diagnostic targets for early CRC detection.