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.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-HDM-S (02))
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Brazhnik, Paul
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University of Pennsylvania
Biostatistics & Other Math Sci
Schools of Medicine
United States
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B Sohn, Michael; Li, Hongzhe (2018) A GLM-based latent variable ordination method for microbiome samples. Biometrics 74:448-457
Chen, Eric Z; Bushman, Frederic D; Li, Hongzhe (2017) A Model-Based Approach For Species Abundance Quantification Based On Shotgun Metagenomic Data. Stat Biosci 9:13-27
Shi, Pixu; Li, Hongzhe (2017) A model for paired-multinomial data and its application to analysis of data on a taxonomic tree. Biometrics 73:1266-1278
Zhao, Sihai Dave; Cai, T Tony; Cappola, Thomas P et al. (2017) Sparse simultaneous signal detection for identifying genetically controlled disease genes. J Am Stat Assoc 112:1032-1046
Zhao, Sihai Dave; Cai, T Tony; Li, Hongzhe (2017) Optimal detection of weak positive latent dependence between two sequences of multiple tests. J Multivar Anal 160:169-184
Chen, Eric Z; Li, Hongzhe (2016) A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics 32:2611-7
Cai, T Tony; Li, Hongzhe; Liu, Weidong et al. (2016) Joint Estimation of Multiple High-dimensional Precision Matrices. Stat Sin 26:445-464
Zhao, Ni; Chen, Jun; Carroll, Ian M et al. (2015) Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test. Am J Hum Genet 96:797-807
Kelly, Brendan J; Gross, Robert; Bittinger, Kyle et al. (2015) Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics 31:2461-8
Jia, Cheng; Hu, Yu; Liu, Yichuan et al. (2015) Mapping Splicing Quantitative Trait Loci in RNA-Seq. Cancer Inform 14:45-53

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