In this proposal, we develop Bayesian methodology for high dimensional genomic data. The overarching theme in this proposal is that we develop several novel statistical methods for motif discovery in genomic sequence data. Chromatin Immunoprecipitation microarray (ChIP-chip) data allows the direct identification of transcription factor binding sites that are active in particular biological states. Jointly modeling array intensities and DNA sequence will lead to more accurate estimation of binding sites. We develop these joint models to account for multiple motifs and varied relationships between binding sites and array intensities. We also propose a novel joint model framework for direct estimation of a motif using gene expression and the DNA sequence that bypasses computationally expensive motif selection procedures. Chromatin structure, in the form of positioning of nucleosomes in DNA, has long been known to play a huge role in protein-DNA binding, however, a quantitative assessment of this role has not been available until very recently. Taking advantage of the increasing availability of accurate experimental data assessing chromatin features, we propose a novel Bayesian statistical model framework for improving motif detection through integration of nucleosome positioning and genomic sequence data. Alternative splicing of mRNA greatly expands the functional repertoire of many genes in the mammalian genome by including or excluding the exons making up the genetic coding sequence. Standard gene expression arrays fail to capture the variability of the exon composition of mRNA species, but rather give a crude measure of overall gene expression. We propose a method that detects over-representation of specific splice junctions in different biological states while adjusting for overall gene expression. The advent of high-throughput genomic technologies has ushered in a new data-driven era, allowing the ability to measure biological activity on a genome-wide scale. Chromatin Immunoprecipitation (ChIP), histone modification, and FAIRE for example are procedures that benefited from this technology, allowing one to determine relative enrichment for their isolated fragments genome wide. The recent development of Next generation sequencing (NGS) platforms offers greater dynamic range, resolution, and genomic coverage in measuring relative enrichment of DNA fragments compared to microarrays. We develop classes of statistical mixture models based on the zero-inflated negative binomial distribution to model such count data and develop an R software package called Zero-Inflated Negative Binomial Algorithm (ZINBA) to carry out the peak calling for a given dataset. 1

Public Health Relevance

We develop Bayesian methodology for high dimensional genomic data. The overarching theme in this proposal is that we develop several novel statistical methods for motif discovery in genomic sequence data. The proposed methodology has major applications in chronic diseases such as cancer, AIDS, cardiovascular disease, and environmental health. We will develop new statistical methods for ChIP-chip data, integrating chormatin structure into motif discovery, joint modeling of gene expression and sequence data, alternative mRNA splicing, and analysis of next generation sequencing (NGS) data. 1

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Brazhnik, Paul
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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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Leão, William L; Abanto-Valle, Carlos A; Chen, Ming-Hui (2017) Bayesian analysis of stochastic volatility-in-mean model with leverage and asymmetrically heavy-tailed error using generalized hyperbolic skew Student's t-distribution. Stat Interface 10:529-541
Abanto-Valle, Carlos A; Langrock, Roland; Chen, Ming-Hui et al. (2017) Maximum likelihood estimation for stochastic volatility in mean models with heavy-tailed distributions. Appl Stoch Models Bus Ind 33:394-408
Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2017) Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials. J Comput Graph Stat 26:121-133
Rao, Shangbang; Ibrahim, Joseph G; Cheng, Jian et al. (2016) SR-HARDI: Spatially Regularizing High Angular Resolution Diffusion Imaging. J Comput Graph Stat 25:1195-1211
Wang, Wenjie; Chen, Ming-Hui; Chiou, Sy Han et al. (2016) Onset of persistent pseudomonas aeruginosa infection in children with cystic fibrosis with interval censored data. BMC Med Res Methodol 16:122
Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth et al. (2016) Statistical methods and computing for big data. Stat Interface 9:399-414
Schifano, Elizabeth D; Wu, Jing; Wang, Chun et al. (2016) Online Updating of Statistical Inference in the Big Data Setting. Technometrics 58:393-403
Joeng, Hee-Koung; Chen, Ming-Hui; Kang, Sangwook (2016) Proportional exponentiated link transformed hazards (ELTH) models for discrete time survival data with application. Lifetime Data Anal 22:38-62
Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2016) JMFit: A SAS Macro for Joint Models of Longitudinal and Survival Data. J Stat Softw 71:
Chen, Qingxia; Zeng, Donglin; Ibrahim, Joseph G et al. (2015) Quantifying the average of the time-varying hazard ratio via a class of transformations. Lifetime Data Anal 21:259-79

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