Despite great success of GWA studies in identification of common genetic variants associated with complex diseases, we are still faced with great challenges to not only confirm the association finding of significant SNPs, but to ultimately identify the path from genomic information to the complex phenotypes. The current GWA studies have primarily focused on testing association of a single SNP at a time. Since common disease are often caused by multiple genes and environments that are organized into a myriad of complex networks, to only test for association of single SNP offers limited understanding of complex diseases and is insufficient to dissect complex genetic structure of diseases. The current GWA studies have also paid less attention to global """"""""systems-level"""""""" approach that provides a functional context within which DNA variation occurs. The function of many SNPs may not be well characterized, but function of genes and particularly pathways, on the contrary, are much better investigated. SNPs and genes carry out their functions through intricate pathways of reactions and interaction. Attempting to understand and interpret a number of significant SNPs without any unifying biological theme can be demanding. Pending conceptual and statistical challenges of the GWA studies are (1) how to take a comprehensive view of the complex genetic structure of common disease to gain insight into the biological processes and disease mechanism and (2) how to integrate genetics and other functional data to connect genomic variation to final clinical outcomes. Overall objective of this application is to use existing genome-wide genotyping data of RA in WTCCC studies (cases: 1860 and controls: 2938, 459,653 SNPs) and in NARAC studies (cases: 866 and controls: 1194, 545,080 SNPs), ankylosing spondylitis (AS) (cases: 1,000 in U. S., 1,000 in U.K., 1,500 controls in U. S., 3,000 controls in U. K. 375,000 SNPs), and psoriasis in GAIN(cases: 1421 and controls: 1425, 451,725 SNPs) as well as expression data of psoriasis ( 34 controls, 37 involved and uninvolved skins from cases, 54,675 probes) to develop novel analytic strategies for gene and pathway-based genome-wide association analysis in which genes and pathways are taken as basic units of association analysis in addition to SNPs, and integrations of genetic, gene expressions and other functional data to decipher path from genomic information to the clinical endpoints of complex diseases, to meet above conceptual and statistical challenges.

Public Health Relevance

The work proposed in this application uses existing genome-wide genotype data of RA, AS and psoriasis and gene expression data of psoriasis that were obtained from NIH, WTCCC by data access agreement, and our funded project P01 AR052915-01A1: Genetics and Ankylosing Spondylitis (AS) Pathogenesis to perform gene and pathway-based GWA studies of RA, AS and psoriasis. This application will also use structural equations to integrate analysis of genotype and expression data and build network models for deciphering path from genomic information to diseases of RA, AS and psoriasis. We believe that the proposed study is timely and we expect that the methodological developments and insights we gain will influence genetic studies of RA, AS, psoriasis and other complex diseases and continue beyond this project.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
5R01AR057120-02
Application #
7927154
Study Section
Special Emphasis Panel (ZRG1-GGG-M (52))
Program Officer
Wang, Yan Z
Project Start
2009-09-01
Project End
2011-06-30
Budget Start
2010-07-01
Budget End
2011-06-30
Support Year
2
Fiscal Year
2010
Total Cost
$269,856
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Genetics
Type
Schools of Public Health
DUNS #
800771594
City
Houston
State
TX
Country
United States
Zip Code
77225
Xu, Kelin; Jin, Li; Xiong, Momiao (2017) Functional regression method for whole genome eQTL epistasis analysis with sequencing data. BMC Genomics 18:385
Zhang, Futao; Xie, Dan; Liang, Meimei et al. (2016) Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits. PLoS Genet 12:e1005965
Wang, Panpan; Rahman, Mohammad; Jin, Li et al. (2016) A new statistical framework for genetic pleiotropic analysis of high dimensional phenotype data. BMC Genomics 17:881
Zhao, Jinying; Zhu, Yun; Xiong, Momiao (2016) Genome-wide gene-gene interaction analysis for next-generation sequencing. Eur J Hum Genet 24:421-8
Guo, Shicheng; Li, Yuan; Wang, Yi et al. (2016) Copy Number Variation of HLA-DQA1 and APOBEC3A/3B Contribute to the Susceptibility of Systemic Sclerosis in the Chinese Han Population. J Rheumatol 43:880-6
Lin, Nan; Jiang, Junhai; Guo, Shicheng et al. (2015) Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis. PLoS One 10:e0132945
Li, Lerong; Xiong, Momiao (2015) Dynamic Model for RNA-seq Data Analysis. Biomed Res Int 2015:916352
Zhao, Jinying; Zhu, Yun; Boerwinkle, Eric et al. (2015) Pathway analysis with next-generation sequencing data. Eur J Hum Genet 23:507-15
Zhang, Futao; Boerwinkle, Eric; Xiong, Momiao (2014) Epistasis analysis for quantitative traits by functional regression model. Genome Res 24:989-98
Hong, Shengjun; Chen, Xiangning; Jin, Li et al. (2013) Canonical correlation analysis for RNA-seq co-expression networks. Nucleic Acids Res 41:e95

Showing the most recent 10 out of 28 publications