Selected Publication

2021 - Present

  • Dai, Q., Zhou, G., Zhao, H., Võsa, U., Franke, L., Battle, A., Teumer, A., Lehtimäki, T., Raitakari, O.T., Esko, T., eQTLGen Consortium, Epstein, P.M.*, Yang, J.*, (2023). OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nature Communications, 14(1), p.1271. DOI: https://doi.org/10.1038/s41467-023-36862-w.

  • Chen J, Wang L, De Jager PL, Bennett DA, Buchman AS, Yang J.* A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations with applications to studying Alzheimer’s disease. Human Genetics and Genomics Advances. 2022 Sep 17:100143. DOI: https://doi.org/10.1016/j.xhgg.2022.100143. Featured with Inside HGG Advances: A Chat with Jingjing Yang. Paper for BFGWAS_QUANT Tool. Presentation Slides.

  • Yang J*, Oveisgharan S, Liu X, Wilson RS, Bennett DA, Buchman AS. Risk Models Based on Non-Cognitive Measures May Identify Presymptomatic Alzheimer’s Disease. J Alzheimers Dis. 2022; 89(4):1249-1262. PMID: 35988224

  • Parrish, R.L., Gibson, G.C., Epstein, M.P. and Yang, J.*, (2022). TIGAR-V2: Efficient TWAS tool with nonparametric Bayesian eQTL weights of 49 tissue types from GTEx V8. Human Genetics and Genomics Advances, 3(1), p.100068. DOI: https://doi.org/10.1016/j.xhgg.2021.100068. Paper for update TIGAR Tool.

  • Yan, L., Song, H., Guo, Y., Ren, P., Zhou, W., Li, S., Yang, J.*, Shen, X.*, (2022). HLDnet: Novel deep learning based Artificial Intelligence tool fuses acetic acid and Lugol’s iodine cervicograms for accurate pre-cancer screening. Biomedical Signal Processing and Control, 71, p.103163. DOI: https://doi.org/10.1016/j.bspc.2021.103163.

  • Yan, L., Li, S., Guo, Y., Ren, P., Song, H., Yang, J.*, & Shen, X.* (2021). Multi-state colposcopy image fusion for cervical precancerous lesion diagnosis using BF-CNN. Biomedical Signal Processing and Control, 68, 102700. DOI: https://doi.org/10.1016/j.bspc.2021.102700.

  • Kuehner JN, Chen J, Bruggeman EC, Wang F, Li Y, Xu C, McEachin ZT, Li Z, Chen L, Hales CM, Wen Z.*, Yang J.*, Yao B.* (2021) 5-hydroxymethylcytosine is dynamically regulated during forebrain organoid development and aberrantly altered in Alzheimer’s disease. Cell Reports. 2021 Apr 27;35(4):109042. DOI: https://doi.org/10.1016/j.celrep.2021.109042.

  • Shizhen Tang, Aron S Buchman, Philip L De Jager, David A Bennett, Michael P Epstein, Jingjing Yang*. (2021). Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia. Online Print with PLOS Genetics: https://doi.org/10.1371/journal.pgen.1009482. Method paper for VC-TWAS implemented in TIGAR.

2017 - 2020

  • Justin M Luningham, Junyu Chen, Shizhen Tang, Phillip De Jager, David A Bennett, Aron S Buchman, Jingjing Yang*. (2020). Novel Bayesian transcriptome-wide association study method leveraging both cis-and trans-eQTL information through summary statistics. The American Journal of Human Genetics. DOI: https://doi.org/10.1016/j.ajhg.2020.08.022. Method paper for BGW-TWAS.

  • Sini Nagpal, Xiaoran Meng, Michael P. Epstein, Lam C. Tsoi, Matthew Patrick, Greg Gibson, Phillip De Jager, David A. Bennett, Aliza P. Wingo, Thomas S. Wingo, Jingjing Yang. (2019). TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. The American Journal of Human Genetics. DOI: https://doi.org/10.1016/j.ajhg.2019.05.018. Method paper for TIGAR.

  • Yang J., Chen S., and Abecasis G. (2018). Improved Score Statistics for Meta-Analysis in Single-Variant and Gene-Level Association Studies. Genetic Epidemiology, 42(4): 333-343. DOI: https://doi.org/10.1002/gepi.22123. PMID: 29696691.

  • Yang J., Fritsche L.G., Zhou X., Abecasis G., IAMDGC. (2017). A Scalable Bayesian Method for Integrating Functional Information in Genome-wide Association Studies. American Journal of Human Genetics, 101(3): 404-416. DOI: https://doi.org/10.1016/j.ajhg.2017.08.002. PMID: 28844487. Method paper for BFGWAS.

A complete list of publication can be found at Google Scholar.

Upcoming Presentation

  • JSM 2023 (Invited), Toronto, Canada. A scalable Bayesian functional GWAS method accounting for multivariate quantitative functional annotations.

Past Presentation

Oral Presentation

  • ENAR 2023 Spring Meeting (Invited), Nashville, TN. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.

  • ICSA 2021 (Invited), Virtual (Sep. 2021). Bayesian Genome-wide TWAS method integrating both cis- and trans- eQTL with GWAS summary statistics. Slides.

  • EcoSta 2021 (Invited), June 2021, Hongkong, China (Virtual). Scalable Bayesian Functional GWAS Method Accounting for Multiple Quantitative Functional Annotations.

  • ROSMAP Annual Meeting, May 2021, Chicago, IL (Virtual). Novel Variance-Component TWAS Method for Studying Alzheimer’s Disease Dementia. Slides.

  • ENAR 2020 Spring Meeting (Invited), Nashville, TN (Virtual). TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits. Slides.

  • IGES 2019 (Robert C. Elston Best Paper Award, Invited), Houston, TX. Improved Score Statistics for Meta-analysis in Single-variant and Gene-level Association Studies. Slides.

  • North Texas University Department of Mathematics Seminar, 2018 (Invited), Denton, TX. Scalable Bayesian Method for Functional Genome-wide Association Studies. Slides.

  • Georgia Tech Center for Integrative Genomics Seminar, 2018 (Invited), Atlanta, GA. Bayesian Approaches to Functional Integration of Genomic Data. Slides.

Poster Presentation

  • ASHG 2021 (Poster), Virtual (Oct. 2021). TIGAR-V2: Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8. Poster.

  • ASHG Annual Meeting 2020 (Poster, Reviewers’ Choice), Virtual. Bayesian Genome-wide TWAS method to leverage both cis- and trans- eQTL information through summary statistics. Poster.

  • The 61st McKusick Short Course (Poster, 3rd Place Presentation), Bar Harbor, Maine (Virtual). Novel Bayesian Genome-wide TWAS method to leverage both cis- and trans- eQTL information through summary statistics.