The long-term objective of this research proposal is to provide a framework, and statistical and computational tools, to advance the analysis and understanding of expression quantitative trait loci (eQTL) in single and multi-tissue studies, and to elucidate the genotypic basis of differences between tissues. The proposal is divided into four Aims: (1) to develop bipartite extensions of statistical tools from the analysis of networks that can enhance the identification of distal (trans) eQTLs; (2) to develop new statistical methods for fast eQTL association mapping that provide reliable estimates of effect size; (3) to extend our existing multi-tissue eQTL procedure into a High-Tissue modeling platform capable of handling existing data sets with 20 to 30 tissues; and (4) to develop gene-based statistical models for eQTL analysis. Development of the proposed methods will be driven by recent, large-scale eQTL studies in which the investigators have played key roles. The resulting computational tools will address current, critical shortcomings in the analysis of these new data sets, and will have broad utility for the wider eQTL analysis community.

Public Health Relevance

The broad objective of the proposed research is to develop statistical methods and computational tools that will aid biomedical researchers investigating the genetic basis of human disease. Its central goal is to enable and enhance recent large scale initiatives to understand complex diseases through the simultaneous study of multiple human tissues.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG009125-01
Application #
9156968
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Struewing, Jeffery P
Project Start
2016-09-07
Project End
2019-06-30
Budget Start
2016-09-07
Budget End
2017-06-30
Support Year
1
Fiscal Year
2016
Total Cost
$348,194
Indirect Cost
$84,649
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Li, Gen; Jima, Dereje; Wright, Fred A et al. (2018) HT-eQTL: integrative expression quantitative trait loci analysis in a large number of human tissues. BMC Bioinformatics 19:95
Zhu, Anqi; Ibrahim, Joseph G; Love, Michael I (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics :
Palowitch, John; Shabalin, Andrey; Zhou, Yi-Hui et al. (2018) Estimation of cis-eQTL effect sizes using a log of linear model. Biometrics 74:616-625
Rudra, Pratyaydipta; Zhou, Yihui; Wright, Fred A (2017) A procedure to detect general association based on concentration of ranks. Stat (Int Stat Inst) 6:88-101