The main purpose of this proposed U19 program project (PPG) is to comprehensively identify male osteoporosis risk genes and their functions. This PPG application will conduct multi-/inter-disciplinary convergence/integration analyses to study and integrate the crosstalk/interaction among multi-level omics obtained from the three individual component projects. The program project involves extensive data management and complex analyses. Such analyses require organic and simultaneous consideration of data from multiple component projects, and demand powerful and innovative integrative analysis methodology. Thus, it is necessary to set up/maintain a Biostatistics and Bioinformatics Core (BBC), focusing on the repurpose of existing, and development and application of novel, integrative analysis approaches. The main Objective of the BBC is to serve as a backbone support resource for experimental design refinement, data quality control, management, integration, analyses and interpretation, and serve as a synergizer to foster data and information exchange and collaboration for individual projects/cores in the PPG. Built upon the Core members? close and long-time collaboration and extensive experience in multi-omics data analyses, this BBC will provide indispensable services through the following Specific Aims: 1) To deliver efficient support and services for data management, including high quality data entry and management database implementation and maintenance, data quality control, safety, monitoring, sharing, etc. 2) To provide strong support for and to conduct extensive biostatistics, bioinformatics, and integrative analyses. Closely working with the PPG project investigators, the BBC will support both single-level omics data analyses and perform integrative analyses of multi-level omics data. Particularly, the BBC will pioneer a sophisticated integrative analysis strategy. This highly innovative strategy will link DNA/miRNA/methylation data through anchoring on gene- based mRNA hubs for constructing unified function multi-omics modules, which will then be used in functional gene unit and disease association analyses. 3) To evaluate, validate, and apply novel, robust and powerful integrative analysis methods for identifying/characterizing (epi-)genes and variants for complex diseases. The novel methods will be developed (mainly in a recently funded R01) under rigorous statistical frameworks, and characterize and incorporate among- omics crosstalk/interactions, along with prior biological information, for studying the causal relationship between multi-omics data and diseases. The innovative methods will be applied to and empirically tested on the multi-omics data obtained from this PPG. The services provided by the Core will be used by all other PPG projects and cores. Overall, the BBC will be efficient, capable and powerful to serve all the PPG projects, and in particular the overall goal of this PPG that intends to integrate the individual project results.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program--Cooperative Agreements (U19)
Project #
5U19AG055373-03
Application #
9688177
Study Section
Special Emphasis Panel (ZAG1)
Project Start
Project End
Budget Start
2019-04-01
Budget End
2020-03-31
Support Year
3
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Tulane University
Department
Type
DUNS #
053785812
City
New Orleans
State
LA
Country
United States
Zip Code
70118
Dong, Shan-Shan; Yao, Shi; Chen, Yi-Xiao et al. (2018) Detecting epistasis within chromatin regulatory circuitry reveals CAND2 as a novel susceptibility gene for obesity. Int J Obes (Lond) :
Lv, Wan-Qiang; Zhang, Xue; Fan, Kun et al. (2018) Genetically driven adiposity traits increase the risk of coronary artery disease independent of blood pressure, dyslipidaemia, glycaemic traits. Eur J Hum Genet 26:1547-1553
Zhao, Qi; Shen, Hui; Su, Kuan-Jui et al. (2018) Metabolomic profiles associated with bone mineral density in US Caucasian women. Nutr Metab (Lond) 15:57
Meng, Xiang-He; Shen, Hui; Chen, Xiang-Ding et al. (2018) Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies. Hum Genet 137:247-255
Zhao, Yan; Ning, Yujie; Zhang, Feng et al. (2018) PCA-based GRS analysis enhances the effectiveness for genetic correlation detection. Brief Bioinform :
Hu, Yuan; Tan, Li-Jun; Chen, Xiang-Ding et al. (2018) Identification of Novel Potentially Pleiotropic Variants Associated With Osteoporosis and Obesity Using the cFDR Method. J Clin Endocrinol Metab 103:125-138
Zhou, Yu; Gao, Yunlong; Xu, Chao et al. (2018) A novel approach for correction of crosstalk effects in pathway analysis and its application in osteoporosis research. Sci Rep 8:668
Liu, Hui-Min; He, Jing-Yang; Zhang, Qiang et al. (2018) Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 293:225-235
Zhang, Wensheng; Flemington, Erik K; Zhang, Kun (2018) Driver gene mutations based clustering of tumors: methods and applications. Bioinformatics 34:i404-i411
Liang, Xiao; Wu, CuiYan; Zhao, Hongmou et al. (2018) Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis. Bone 116:301-306

Showing the most recent 10 out of 26 publications