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.
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.
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|Jia, Cheng; Guan, Weihua; Yang, Amy et al. (2015) MetaDiff: differential isoform expression analysis using random-effects meta-regression. BMC Bioinformatics 16:208|
|Jeng, Jessie; Wu, Qian; Li, Hongzhe (2015) A Statistical Method for Identifying Trait-Associated Copy Number Variants. Hum Hered 79:147-56|
|Wu, Qian; Won, Kyoung-Jae; Li, Hongzhe (2015) Nonparametric Tests for Differential Histone Enrichment with ChIP-Seq Data. Cancer Inform 14:11-22|
|Liu, Yichuan; Morley, Michael; Brandimarto, Jeffrey et al. (2015) RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics 105:83-9|
|Jia, Cheng; Hu, Yu; Liu, Yichuan et al. (2015) Mapping Splicing Quantitative Trait Loci in RNA-Seq. Cancer Inform 14:45-53|
|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|
|Vardhanabhuti, Saran; Jeng, X Jessie; Wu, Yinghua et al. (2014) Parametric modeling of whole-genome sequencing data for CNV identification. Biostatistics 15:427-41|
|Zhao, Sihai Dave; Cai, T Tony; Li, Hongzhe (2014) Direct estimation of differential networks. Biometrika 101:253-268|
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