Genomic based studies of disease now involve highly diverse types of data collected on large groups of patients. A major challenge facing scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and/or likelihood of response to treatment. This project aims to develop statistical methods to address important problems that arise in genomic based studies of disease. In particular, we propose methods to improve the power and accuracy of results obtained from genome-wide studies of gene expression. We also propose statistical methods that integrate data across multiple platforms and scales. These integrative methods enable powerful inference related to identifying and quantifying groups of features that change across biological conditions (e.g. healthy vs. disease), and they also allow for the identification of important collections of features that affect a patient's disease course and/or treatment response. Successful completion of the project will help to ensure that maximal utility is gained from the powerful genomic-based technologies that are now routinely used in efforts to gain insights into and information about the genomic mechanisms underlying disease manifestation, progression, and maintenance.

Public Health Relevance

The development of statistically sound approaches to resolve the genomic basis of complex traits is vital to individualizing medicine and improving public health. Ideally, high-throughput genetic, genomic, and pheno- typic measurements on diseased individuals would lead quickly to the identification of the salient features underlying their disease, along with a specification about how these features affect disease course. Many challenges in biostatistics must be overcome before this ideal is achieved. This proposal addresses some of those critical challenges.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM102756-03
Application #
8657455
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Marcus, Stephen
Project Start
2012-08-01
Project End
2016-04-30
Budget Start
2014-05-01
Budget End
2015-04-30
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
City
Madison
State
WI
Country
United States
Zip Code
53715
Bacher, Rhonda; Leng, Ning; Chu, Li-Fang et al. (2018) Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinformatics 19:380
Keller, Mark P; Gatti, Daniel M; Schueler, Kathryn L et al. (2018) Genetic Drivers of Pancreatic Islet Function. Genetics 209:335-356
Bacher, Rhonda; Chu, Li-Fang; Leng, Ning et al. (2017) SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 14:584-586
Gasch, Audrey P; Yu, Feiqiao Brian; Hose, James et al. (2017) Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol 15:e2004050
Choi, J; Ye, S; Eng, K H et al. (2017) IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients. Stat Biosci 9:1-12
Ye, Shuyun; Bacher, Rhonda; Keller, Mark P et al. (2017) Statistical Methods for Latent Class Quantitative Trait Loci Mapping. Genetics 206:1309-1317
Korthauer, Keegan D; Chu, Li-Fang; Newton, Michael A et al. (2016) A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 17:222
Leng, Ning; Choi, Jeea; Chu, Li-Fang et al. (2016) OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data. Bioinformatics 32:1408-10
Tian, Jianan; Keller, Mark P; Broman, Aimee Teo et al. (2016) The Dissection of Expression Quantitative Trait Locus Hotspots. Genetics 202:1563-74
Bacher, Rhonda; Kendziorski, Christina (2016) Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol 17:63

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