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.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Ramos, Erin
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University of Chicago
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
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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
Shiraishi, Yuichi; Tremmel, Georg; Miyano, Satoru et al. (2015) A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures. PLoS Genet 11:e1005657
Shim, Heejung; Chasman, Daniel I; Smith, Joshua D et al. (2015) A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One 10:e0120758
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
Raj, Anil; Stephens, Matthew; Pritchard, Jonathan K (2014) fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197:573-89
Zhou, Xiang; Stephens, Matthew (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407-9
Stephens, Matthew (2013) A unified framework for association analysis with multiple related phenotypes. PLoS One 8:e65245
Zhou, Xiang; Carbonetto, Peter; Stephens, Matthew (2013) Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet 9:e1003264
Carbonetto, Peter; Stephens, Matthew (2013) Integrated enrichment analysis of variants and pathways in genome-wide association studies indicates central role for IL-2 signaling genes in type 1 diabetes, and cytokine signaling genes in Crohn's disease. PLoS Genet 9:e1003770

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