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.
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.
| 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 |
| 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 : |
| 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 |
