Thousands of genome-wide association studies link speci c diseases or complex phenotypes to singlemutations in the human genome. But translating these results to medical treatments requires aprecise understanding of how that mutation contributes to the mechanism of disease. Currently,the regulatory role of single nucleotide polymorphisms (SNPs) is, for the most part, con ned tolocal, or cis-, expression quantitative trait loci (eQTLs) in a small number of human tissues. Butnot all diseases or complex phenotypes are mediated by cis-eQTLs. Very few long-distance, ortrans-, eQTLs have been identi ed and validated in human tissues, although trans-eQTLs play animportant role in some complex phenotypes. Alternative splicing has also been shown to modulatecertain phenotypes; however, little is known about SNPs that regulate alternative splicing. Theproposed K99/R00 research seeks to design statistical methods that build gene andtranscript networks to identify SNPs that regulate gene and mRNA isoform tran-scription, both locally and over long distances, and to validate those ndings, for thepurpose of providing insight into mechanisms for complex phenotypes and disease. We propose to leverage cis-eQTLs and gene expression data in humans identi ed in our currentwork to build precise, directed gene networks on a genome-scale. We will build these networks usingBayesian statistical models to compute the probability of a particular network with respect to eachgene in the network jointly, with associated eQTLs providing information about whether regulatedgenes are upstream or downstream of other network genes. We will use Markov chain Monte Carloand linear programming relaxation methods that have been shown to nd near-optimal solutionsto this type of problem. We will use these networks to identify trans-eQTLs, and quantify thee ect of each trans-eQTL in a particular process using Bayesian statistical tests developed in ourlab. Subsequently, we propose to exploit the opportunities of novel RNA sequencing techniquesand nonparametric statistical models to identify transcript isoforms for each transcribed gene and,simultaneously, individual-speci c transcript levels by extending sparse factor analysis models.This will enable us to identify QTLs that regulate the transcription of speci c transcript isoforms(tQTLs) via alternative splicing events by extending the methods we have for eQTL identi cation.We will use the methodology we developed for eQTLs to build networks for transcript isoforms(transcript networks ). Finally, we will use transcript networks to identify and quantify tQTLs thatregulate individual-speci c levels of transcript isoforms both locally and over long genetic distances,as with eQTLs. We will make all of our methods and results publicly available.

Public Health Relevance

Thousands of genome-wide association studies link speci c diseases or complex traits to singlemutations in the human genome; but these results cannot yet be translated to medical treatmentsbecause knowing that a mutation is associated with a disease does not; in fact; give us insight intohow that mutation contributes to the mechanism of disease. Our proposed research will design andvalidate statistical methods that provide a comprehensive road map to understanding the biologicalrole of the mutations that are identi ed in these association studies. With the role of thousands ofpossibly disease-related mutations in hand; researchers can begin to piece together the mechanismof a disease and translate their ndings into treatments for the disease much more quickly.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Transition Award (R00)
Project #
7R00HG006265-05
Application #
9064281
Study Section
Special Emphasis Panel (NSS)
Program Officer
Volpi, Simona
Project Start
2011-09-06
Project End
2016-06-30
Budget Start
2014-09-01
Budget End
2016-06-30
Support Year
5
Fiscal Year
2014
Total Cost
$247,199
Indirect Cost
$16,124
Name
Princeton University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08543
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