The broad, long-term objective of this project concerns the development of novel statistical methods and computational tools for statistical and probabilistic modeling of large-scale next-generation sequence (NGS) data motivated by important biological questions and experiments.
The specific aim of the current project is to develop new statistical models and computational methods for analysis of NGS data, focusing on robust methods for discovering copy number variants (CNVs) in germline DNAs, development of a general log-linear model for identifying alternative exon usages on one- and multi-sample RNA-seq data allowing for non-uniformity on short- read sequencing rates, development of novel nonparametric statistical methods for identifying histone modification sites based on the chromatin immunoprecipitation and high-throughput sequencing (ChIP-seq) data, and novel methods for analysis of metagenomic data from human microbiome studies. These problems are all motivated by the PI's close collaborations with Penn investigators. The methods hinge on novel integration of biological insights and methods for high dimensional data analysis, including detection and identification of sparse structured-signals, wavelet-based nonparametric regression and nonparametric hypothesis testing and penalized regression analysis for tree-structured covariates. The new methods can be applied to different types of NGS data and will ideally facilitate the identifications of genes and biological pathways underlying various complex human diseases and biological processes. The project will also investigate the robustness, power and efficiencies of these methods and compare them with existing methods. In addition, this project will develop practical and feasible computer programs in order to implement the proposed methods, to evaluate the performance of these methods through applications to NGS data sets related to CNV and RNA-seq analysis in African populations, linkage of peroxi- some proliferator activator receptor (PPAR)3 and adipose differentiation and insulin resistance and effects of diets on human microbiome. The work proposed here will contribute statistical methodology to modeling ultra-high dimensional next-generation sequence data and to studying complex phenotypes and biological systems and offer insights into each of the biological areas represented by the various data sets. All programs developed under this grant and detailed documentation will be made available free-of-charge to interested researchers.

Public Health Relevance

This project aims to develop powerful statistical and computational methods for analysis of next-generation sequence data, which has enabled comprehensive analysis of genomes, transcriptomes, and interactomes and micro- biomes. The novel statistical methods are expected to gain more insights into how genomic/metagenmic variations can lead to development of complex phenotypes such as cardiovascular phenotypes and insulin resistance and better understanding the genetic structural variations in African populations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM097505-03
Application #
8643260
Study Section
Special Emphasis Panel (ZRG1-HDM-S (02))
Program Officer
Brazhnik, Paul
Project Start
2012-07-01
Project End
2016-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
3
Fiscal Year
2014
Total Cost
$303,617
Indirect Cost
$108,617
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Hu, Yu; Liu, Yichuan; Mao, Xianyun et al. (2014) PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution. Nucleic Acids Res 42:e20
Wang, Wei; Wei, Zhi; Li, Hongzhe (2014) A change-point model for identifying 3'UTR switching by next-generation RNA sequencing. Bioinformatics 30:2162-70
Li, Hongzhe (2013) Systems biology approaches to epidemiological studies of complex diseases. Wiley Interdiscip Rev Syst Biol Med 5:677-86
Jeng, X Jessie; Cai, T Tony; Li, Hongzhe (2013) Simultaneous Discovery of Rare and Common Segment Variants. Biometrika 100:157-172
Yin, Jianxin; Li, Hongzhe (2013) Adjusting for High-dimensional Covariates in Sparse Precision Matrix Estimation by ýýý1-Penalization. J Multivar Anal 116:365-381
Chen, Jun; Bushman, Frederic D; Lewis, James D et al. (2013) Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics 14:244-58
Chen, Jun; Li, Hongzhe (2013) VARIABLE SELECTION FOR SPARSE DIRICHLET-MULTINOMIAL REGRESSION WITH AN APPLICATION TO MICROBIOME DATA ANALYSIS. Ann Appl Stat 7: