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Principal Investigator  
Principal Investigator's Name: chen cao
Institution: Nanjing Medical University
Department: School of Biomedical Engineering and Informatics
Country:
Proposed Analysis: The molecular mechanism of Alzheimer's disease is an important issue of numerous recent studies, but few of these investigations have yet presented sufficient explanations. Additionally, there is still no conclusive molecular mechanism to explain the etiology of the disease or its genetic mechanisms. Determining how to statistically distinguish true or false associations between genotype and phenotype has proven to be difficult. Gene-gene interactions, allelic heterogeneity, a huge number of genetic variants, and small sample sizes limit the use of genomes for understanding the molecular mechanism. We are focused on the field of transcriptome-wide association study (TWAS), which is a strategy to identify gene-trait associations, and various frameworks and tools have been developed to perform comprehensive TWAS analysis. Our previous works[1-4] have found susceptibility genes for complex diseases and showed strong statistical significance, and TWAS[5-9] has become a powerful tool to identify susceptible risk genes in complex human traits[10-13]. Imaging-wide association study (IWAS)[14] is a new and powerful approach to integrating imaging endophenotypes for the interpretation of gene-trait associations. Given our relatively strong foundation in TWAS and the degree of overlap between TWAS and IWAS, it is possible to extend our work to IWAS. ANDI data will be used as a bridge between genotype and phenotype in our following research plan, and conduct thorough follow-up studies of published association studies in conjunction with IWAS. To utilize the biological information collected from ANDI, genotype, and phenotype datasets, we will fit novel statistical models based on elastic networks, Bayesian sparse mixture models, etc. ANDI datasets also provide a reference panel of expression architecture in many tissues. Moreover, experimental verification will be conducted whenever new findings are discovered. We hope to contribute to the early diagnosis of Alzheimer's disease and to interrupt the progression of the disease. References: [1] Cao C, Ding B, Li Q, Kwok D, Wu J, Long Q. Power analysis of transcriptome-wide association study: Implications for practical protocol choice. PLoS Genetics. 2021. doi: 10.1371/journal.pgen.1009405. [2] Cao C, Wang J, Devin K, Cui F, Zhang Z, Zhao D, Li J, Zou Q. webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Research. 2022. doi:10.1093/nar/gkab957 [3] Cao C, Kwok D, Edie S, Li Q, Ding B, Kossinna P, Campbell S, Wu J, Greenberg M, Long Q. kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes. Briefings in Bioinformatics. 2021. doi: 10.1093/bib/bbaa270. [4] Cao C, Kossinna P, Kwok D, Qingrun Zhang, Long Q. Disentangling genetic feature selection and aggregation in transcriptome-wide association studies[J]. Genetics. 2022. doi: 10.1093/genetics/iyab216. [5] Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC; GTEx Consortium, Nicolae DL, Cox NJ, Im HK. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015 Sep;47(9):1091-8. doi: 10.1038/ng.3367. [6] Gusev, A., Ko, A., Shi, H. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48, 245–252 (2016). doi: 10.1038/ng.3506. [7] Mancuso N, Freund MK, Johnson R, Shi H, Kichaev G, Gusev A, Pasaniuc B. Probabilistic fine-mapping of transcriptome-wide association studies. Nat Genet. 2019 Apr;51(4):675-682. doi: 10.1038/s41588-019-0367-1. [8] Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK; Alzheimer’s Disease Genetics Consortium,, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nat Genet. 2019 Mar;51(3):568-576. doi: 10.1038/s41588-019-0345-7. [9] Zhou D, Jiang Y, Zhong X, Cox NJ, Liu C, Gamazon ER. A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis. Nat Genet. 2020 Nov;52(11):1239-1246. doi: 10.1038/s41588-020-0706-2. [10] Gusev, A., Lawrenson, K., Lin, X. et al. A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants. Nat Genet 51, 815–823 (2019). doi:10.1038/s41588-019-0395-x [11] Wu, L., Shi, W., Long, J. et al. A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer. Nat Genet 50, 968–978 (2018). doi:10.1038/s41588-018-0132-x [12] Ratnapriya, R., Sosina, O.A., Starostik, M.R. et al. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet 51, 606–610 (2019). doi:10.1038/s41588-019-0351-9 [13] Gusev, A., Mancuso, N., Won, H. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat Genet 50, 538–548 (2018). doi:10.1038/s41588-018-0092-1 [14] Xu Z, Wu C, Pan W; Alzheimer's Disease Neuroimaging Initiative. Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage. 2017 Oct 1;159:159-169. doi: 10.1016/j.neuroimage.2017.07.036.
Additional Investigators