Applications of high-throughput sequencing (such as """"""""ChIP-seq"""""""" and """"""""RNA-seq"""""""") have recently transformed our ability to study protein-DNA interactions, chromatin modifications, and gene expression through their precise quantification of these phenomena genome-wide. Although numerous groundbreaking discoveries have emerged from the application of these techniques, few efforts have considered that the genome is in fact represented by two copies that often differ in functionally important ways. Heterozygous polymorphisms (such as SNPs) in a biological sample that is subject to ChIP-seq or RNA-seq provide a means to quantify allele-specific events by discriminating sequence reads that map to one allele versus the other, since biased representation of alleles among sequence reads can reveal sites/genes that are affected by cis- acting polymorphisms. It is our objective to create tools that will allow the community to more easily analyze their data for allele-specific signals;apply these tools to dozens of existing data sets;and generate new ChIP-seq and RNA-seq data that are ideally suited for the comprehensive characterization of allele-specific biases.

Public Health Relevance

Elucidating the functional consequences of genetic polymorphisms is crucial for understanding the genetic risk factors underlying human diseases. This project will advance our knowledge of how cis-acting polymorphisms, which are a widespread source of phenotypic diversity, affect mRNA abundances. In addition, application of our methodology in human samples has the potential to uncover the precise causal polymorphisms underlying significant associations revealed by genome-wide disease association studies, which is important e.g. for performing targeted follow-up studies and functional assays to better understand each polymorphism and its effects on both gene expression and disease.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HG005240-01A1
Application #
7991174
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Struewing, Jeffery P
Project Start
2010-09-18
Project End
2012-07-31
Budget Start
2010-09-18
Budget End
2011-07-31
Support Year
1
Fiscal Year
2010
Total Cost
$276,500
Indirect Cost
Name
Stanford University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Kaplow, Irene M; MacIsaac, Julia L; Mah, Sarah M et al. (2015) A pooling-based approach to mapping genetic variants associated with DNA methylation. Genome Res 25:907-17
Ariza-Cosano, Ana; Visel, Axel; Pennacchio, Len A et al. (2012) Differences in enhancer activity in mouse and zebrafish reporter assays are often associated with changes in gene expression. BMC Genomics 13:713
Irimia, Manuel; Tena, Juan J; Alexis, Maria S et al. (2012) Extensive conservation of ancient microsynteny across metazoans due to cis-regulatory constraints. Genome Res 22:2356-67