We propose to develop an array of novel statistical methods for association analysis of functional phenotypes arising from high-throughput sequencing assays in genetics and genomics, specifically data from RNA-seq, ChIP-seq, and DNase-seq assays. Our proposed approach is to treat the number of reads mapping to each base along the genome as a highly-multivariate, but also highly-structured, phenotype. Using methods from signal processing (wavelets), we will develop methods to identify regions of the genome where these phenotypes differ significantly between samples, or groups of samples (e.g. cell types, treatment groups, or genotype classes). In contrast to approaches based on sliding windows, the methods will be capable of identifying differences that occur at multiple different scales. The statistical methods we develop will facilitate both small-scale comparisons (e.g. identifying differences in binding, o histone modifications, between two samples or conditions), and larger-scale analyses, such as genetic association analyses that aim to identify genetic variants associated with these phenotypes (expression QTLs, binding QTLs, dsQTLs). As an important special case, our methods will tackle the commonly- encountered problem of identifying differentially expressed genes, including variations in splicing or alternative transcripts, from RNA-seq data. These methods will build on and substantially extend methods for association analyses developed during the current funding cycle of this R01. The result of our research will be a suite of statistical tools that will greatly facilitate the analysis of the wide range of genetic and genomi studies that involve functional phenotypes. We will produce and distribute user-friendly software implementing these methods. We will use our methods to analyze existing data generated by our collaborators, and publicly-available data from the NIH-funded GTeX project, both to compare them with existing analysis methods and to identify regulatory genetic variants responsible for phenotypic variation. The overall objective is for the work to provide software and statistical tools for the genetics and genomics research community, facilitating biological discoveries and insights, and, ultimately, understanding of the genetic basis of common disease.

Public Health Relevance

This project will generate statistical tools for the genetics and genomics research community, and apply them to identify functional genetic variants that affect human phenotypes. These tools will help facilitate biological discoveries and insights, and, ultimately, understanding of the genetic basis of common disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG002585-10
Application #
8725216
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Ramos, Erin
Project Start
2002-09-20
Project End
2017-06-30
Budget Start
2014-07-01
Budget End
2015-06-30
Support Year
10
Fiscal Year
2014
Total Cost
$410,576
Indirect Cost
$150,718
Name
University of Chicago
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Zhu, Xiang; Stephens, Matthew (2018) Large-scale genome-wide enrichment analyses identify new trait-associated genes and pathways across 31 human phenotypes. Nat Commun 9:4361
Gerard, David; Stephens, Matthew (2018) Empirical Bayes shrinkage and false discovery rate estimation, allowing for unwanted variation. Biostatistics :
Al-Asadi, Hussein; Dey, Kushal K; Novembre, John et al. (2018) Inference and visualization of DNA damage patterns using a Grade of Membership Model. Bioinformatics :
Zhu, Xiang; Stephens, Matthew (2017) BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES. Ann Appl Stat 11:1561-1592
Dey, Kushal K; Hsiao, Chiaowen Joyce; Stephens, Matthew (2017) Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genet 13:e1006599
Stephens, Matthew (2017) False discovery rates: a new deal. Biostatistics 18:275-294
Lu, Mengyin; Stephens, Matthew (2016) Variance adaptive shrinkage (vash): flexible empirical Bayes estimation of variances. Bioinformatics 32:3428-3434
Raj, Anil; Wang, Sidney H; Shim, Heejung et al. (2016) Thousands of novel translated open reading frames in humans inferred by ribosome footprint profiling. Elife 5:
Petkova, Desislava; Novembre, John; Stephens, Matthew (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48:94-100
Raj, Anil; Shim, Heejung; Gilad, Yoav et al. (2015) msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding. PLoS One 10:e0138030

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