Chong Wu, PhD
Department of Biostatistics, Division of Discovery Science
About Dr. Chong Wu
I am an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson. Before that, I was an Assistant Professor in the Department of Statistics at Florida State University. Prior to FSU, I was a biostatistics Ph.D. student at the University of Minnesota, co-advised by Profs. Weihua Guan and Wei Pan. I received my bachelor's degree in Applied Math from Huazhong University of Science and Technology in 2013.
I have obtained diverse training and experiences in developing statistical methods and algorithms for analyzing multiple types of genetic and genomic data, including genome-wide association study (GWAS) data, DNA methylation data, and human microbiome data. My work includes gene- and pathway-based association testing, integrative -omics analysis (especially TWAS), new algorithms in clustering, Mendelian randomization, and polygenic risk scores. To practice and facilitate reproducible research, I have developed and currently maintain several software (most in R packages) and their online manuals.
My recent research focuses on developing a new generation of data-driven methods and software to address challenges imposed by big and messy genomics data. Briefly, my research aim at 1) identifying putative causal biomarkers to gain insights into the genetic basis of complex diseases, particularly prostate cancer, pancreatic cancer, and Alzheimer's, and 2) enhancing risk predictions to advance precision medicine.
Present Title & Affiliation
Primary Appointment
Affiliate, Department of Institute for Data Science in Oncology (IDSO), Division of Discovery Science, The University of Texas MD Anderson Cancer Center, Houston, Texas
Adjunct Assistant Professor, Department of Statistics, Rice University, Houston, Texas
Adjunct Assistant Professor, Department of Biostatistics and Data Science, UTHealth School of Public Health, Houston, Texas
Adjunct Assistant Professor, Department of Statistics, Florida State University, Tallahassee, FL
Assistant Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
Dual/Joint/Adjunct Appointment
Assistant Professor, Department of Biostatistics, The University of Texas MD Anderson Cancer Center
Research Interests
Statistical genetics Causal inference Machine learning Risk prediction
Education & Training
Degree-Granting Education
| 2018 | University of Minnesota, Minneapolis, Minnesota, US, Biostatistics, PhD |
| 2013 | Huazhong University of Science and Technology, Wuhan, CN, Math & Applied Math, BS |
Experience & Service
Academic Appointments
Assistant Professor, Department of Statistics, Florida State University, 2018 - 2022
Honors & Awards
| 2020 | Dean’s Faculty Travel Award, Florida State University |
Professional Memberships
Selected Publications
Peer-Reviewed Articles
- Gu C, Ghasemi SM, Cai Y, Fahrmann JF, Long JP, Katayama H, Wu C, Vykoukal J, Dennison JB, Hannah S, Do KA, Irajizad E. Grape-Pi: Graph-based neural networks for enhanced protein identification in proteomics pipelines. Bioinformatics Advances. e-Pub 2025.
- Wang Z, Xing X, Mun ZY, Wu C, Ling L. The role of double‐zero‐event studies in evidence synthesis: Evaluating robustness using the fragility index. Journal of Evaluation in Clinical Practice 31(1):e14301, 2025. e-Pub 2025. PMID: 39780615.
- Liu S, Zhu J, Green D, Zhong H, Long Q, Wu C, Wang L, Deng Y, Wu L. Integrating multi-omics data to uncover prostate tissue DNA methylation biomarkers and target genes for prostate cancer risk. Molecular Carcinogenesis 64(1):83-90, 2025. e-Pub 2025. PMID: 39400371.
- Sun Y, Zhu J, Zhong H, Zhang Z, Wang F, Nakamura A, Liu Y, Liu J, Yu J, Zeng G, Lin X, Zhou D, Wu C, Wang L, Deng Y, Wu L. Transcriptome‐wide association study identified novel blood tissue gene biomarkers for prostate cancer risk. The Prostate 85:567-579, 2025. e-Pub 2025. PMID: 39878408.
- King A, Wu C. Integrative multi-omics approach for improving causal gene identification. Genetic Epidemiology 49(1):e22601, 2025. e-Pub 2025. PMID: 39444114.
- Yu J, Zhu J, Zhong H, Zhang Z, Liu J, Lin X, Zeng G, Zhang M, Wu C, Deng Y, Sun Y, Wu L. Age-related hearing impairment: Genome and blood methylome data integration reveals candidate epigenetic biomarkers. OMICS: A Journal of Integrative Biology 28(12):620-631, 2024. e-Pub 2024. PMID: 39585213.
- Liu S, Zhu J, Zhong H, Wu C, Xue H, Darst BF, Guo X, Durda P, Tracy RP, Liu Y, Johnson WC, Taylor KD, Manichaikul AW, Goodarzi MO, Gerszten RE, Clish CB, Chen YI, Highland H, Haiman CA, Gignoux CR, Lange L, Conti DV, Raffield LM, Wilkens L, Marchand LL, North KE, Young KL, Loos RJ, Buyske S, Matise T, Peters U, Kooperberg C, Reiner AP, Yu B, Boerwinkle E, Sun Q, Rooney MR, Echouffo-Tcheugui JB, Daviglus ML, Qi Q, Mancuso N, Li C, Deng Y, Manning A, Meigs JB, Rich SS, Rotter JI, Wu L. Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations. Diabetologia 67(12):2754-2770, 2024. e-Pub 2024. PMID: 39349773.
- Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Qing T, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Computers in Biology and Medicine 179, 2024. e-Pub 2024. PMID: 38955127.
- Liu Y, Meng XH, Wu C, Su KJ, Liu A, Tian Q, Zhao LJ, Qiu C, Luo Z, Gonzalez-Ramirez MI, Shen H, Xiao HM, Deng HW. Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study. PLoS Medicine 21(8):e1004451, 2024. e-Pub 2024. PMID: 39213443.
- Lin W, Ji J, Su KJ, Qiu C, Tian Q, Zhao LJ, Luo Z, Wu C, Shen H, Deng HW. omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data. NAR Genomics and Bioinformatics 6(2):lqae071, 2024. e-Pub 2024. PMID: 38881578.
- Meng Z, Wang J, Lin L, Wu C. Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses. Statistics in Medicine 43(8):1549-1563, 2024. e-Pub 2024. PMID: 38318993.
- Lyu Y, Wu C, Sun W, Li Z. Regional analysis to delineate intrasample heterogeneity with RegionalST. Bioinformatics 40(4), 2024. e-Pub 2024. PMID: 38579257.
- Melton HJ, Zhang Z, Wu C. SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations. Human Molecular Genetics 33(7):624-635, 2024. e-Pub 2024. PMID: 38129112.
- Zhu J, Liu S, Walker KA, Zhong H, Ghoneim DH, Zhang Z, Surendran P, Fahle S, Butterworth A, Alam MA, Deng HW, Wu C, Wu L. Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models. Alzheimer's Research & Therapy 16(1):8, 2024. e-Pub 2024. PMID: 38212844.
- Zhu J, Wu K, Liu S, Masca A, Zhong H, Yang T, Ghoneim DH, Surendran P, Liu T, Yao Q, Liu T, Fahle S, Butterworth A, Alam MA, Vadgama JV, Deng Y, Deng HW, Wu C, Wu Y, Wu L. Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma. Gigascience 13, 2024. e-Pub 2024. PMID: 38608280.
- Melton HJ, Zhang Z, Deng HW, Wu L, Wu C. MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations. Epigenetics 19(1), 2024. e-Pub 2024. PMID: 38963888.
- Sun Y, Zhu J, Yang Y, Zhang Z, Zhong H, Zeng G, Zhou D, Nowakowski RS, Long J, Wu C, Wu L. Identification of candidate DNA methylation biomarkers related to Alzheimer's disease risk by integrating genome and blood methylome data. Translational Psychiatry 13(1):387, 2023. e-Pub 2023. PMID: 38092781.
- Liu D, Bae YE, Zhu J, Zhang Z, Sun Y, Deng Y, Wu C, Wu L. Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk. Carcinogenesis 44(10-11):741-747, 2023. e-Pub 2023. PMID: 37769343.
- Zhong H, Zhu J, Liu S, Ghoneim DH, Surendran P, Liu T, Fahle S, Butterworth A, Ashad Alam M, Deng HW, Yu H, Wu C, Wu L. Identification of genetically predicted blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects. Human Molecular Genetics 32(22):3181-3193, 2023. e-Pub 2023. PMID: 37622920.
- Wei W, Petersen M, van der Laan MJ, Zheng Z, Wu C, Wang J. Efficient targeted learning of treatment effects for multiple subgroups in observational studies. Biometrics 79(3):1934-1946, 2023. e-Pub 2023. PMID: 36416173.
- Sun Y, Bae YE, Zhu J, Zhang Z, Zhong H, Cheng C, Deng Y, Wu C, Wu L. A splicing transcriptome-wide association study identifies candidate altered splicing for prostate cancer risk. OMICS 27(8):372-380, 2023. e-Pub 2023. PMID: 37486714.
- Sun Y, Bae YE, Zhu J, Zhang Z, Zhong H, Yu J, Wu C, Wu L. A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility. Neurobiology of Disease 184:106209, 2023. e-Pub 2023. PMID: 37354922.
- Meng Z, Wu C, Lin L. The effect direction should be taken into account when assessing small-study effects. Journal of Evidence-Based Dental Practice 23(1):101830, 2023. e-Pub 2023. PMID: 36914304.
- Ma X, Wang J, Wu C. Breaking the winner's curse in Mendelian randomization. The Annals of Statistics 51(1):211-232, 2023. e-Pub 2023.
- Guo X, Wei W, Liu M, Cai T, Wu C, Wang J. Assessing heterogeneous risk of type 2 diabetes associated with statins usage: Evidence from electronic health record data. Journal of the American Statistical Association 118(543):1488-1499, 2023. e-Pub 2023. PMID: 38223220.
- King A, Wu L, Deng HW, Shen H, Wu C. Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease. BMC Medicine 20(1):385, 2022. e-Pub 2022. PMID: 36336692.
- Zhang Z, Bae YE, Bradley JR, Wu L, Wu C. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Nature Communications 13(1):6336, 2022. e-Pub 2022. PMID: 36284135.
- Liu D, Zhu J, Zhou D, Nikas EG, Mitanis NT, Sun Y, Wu C, Mancuso N, Cox NJ, Wang L, Freedland SJ, Haiman CA, Gamazon ER, Nikas JB, Wu L. A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk. International Journal of Cancer 150(1):80-90, 2022. e-Pub 2022. PMID: 34520569.
- Song M, Greenbaum J, Luttrell J, Zhou W, Wu C, Luo Z, Qiu C, Zhao LJ, Su KJ, Tian Q, Shen H, Hong H, Gong P, Shi X, Deng HW, Zhang C. An autoencoder-based deep learning method for genotype imputation. Frontiers in Artificial Intelligence 5:1028978, 2022. e-Pub 2022. PMID: 36406474.
- Sun Y, Zhou D, Rahman MR, Zhu J, Ghoneim D, Cox NJ, Beach TG, Wu C, Gamazon ER, Wu L. A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk. Human Molecular Genetics 31(2):289-299, 2021. e-Pub 2021. PMID: 34387340.
- Wu C, Zhu J, King A, Tong X, Lu Q, Park JY, Wang L, Gao G, Deng HW, Yang Y, Knudsen KE, Rebbeck TR, Long J, Zheng W, Pan W, Conti DV, Haiman CA, Wu L. Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer. Cancer Communications 41(12):1387-1397, 2021. e-Pub 2021. PMID: 34520132.
- Bae YE, Wu L, Wu C. InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies. Genetic Epidemiology 45(8):848-859, 2021. e-Pub 2021. PMID: 34255882.
- Yu G, Sun K, Xu C, Shi XH, Wu C, Xie T, Meng RQ, Meng XH, Wang KS, Xiao HM, Deng HW. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images. Nature Communications 12(1):6311, 2021. e-Pub 2021. PMID: 34728629.
- Wu L, Zhu J, Liu D, Sun Y, Wu C. An integrative multiomics analysis identifies putative causal genes for COVID-19 severity. Genetics in Medicine 23(11):2076-2086, 2021. e-Pub 2021. PMID: 34183789.
- Sun Y, Zhu J, Zhou D, Canchi S, Wu C, Cox NJ, Rissman RA, Gamazon ER, Wu L. A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes. Genome Medicine 13(1):141, 2021. e-Pub 2021. PMID: 34470669.
- Wu C, Bradley J, Li Y, Wu L, Deng HW. A gene-level methylome-wide association analysis identifies novel Alzheimer's disease genes. Bioinformatics 37(14):1933-1940, 2021. e-Pub 2021. PMID: 33523132.
- Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, Zhou X, Li Q, Li J, Wang J, Ma K, Qi J, Hu Z, Tang P, Deng J, Qiu X, Li BY, Shen WD, Quan RP, Yang JT, Huang LY, Xiao Y, Yang ZC, Li Z, Wang SC, Ren H, Liang C, Guo W, Li Y, Xiao H, Gu Y, Yun JP, Huang D, Song Z, Fan X, Chen L, Yan X, Li Z, Huang ZC, Huang J, Luttrell J, Zhang CY, Zhou W, Zhang K, Yi C, Wu C, Shen H, Wang YP, Xiao HM, Deng HW. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Medicine 19(1):76, 2021. e-Pub 2021. PMID: 33752648.
- He Y, Xu G, Wu C, Pan W. Asymptotically independent U-statistics in high-dimensional testing. Annals of Statistics 49(1):154-181, 2021. e-Pub 2021. PMID: 34857975.
- Zhu J, Wu C, Wu L. Associations Between Genetically Predicted Protein Levels and COVID-19 Severity. Journal of Infectious Diseases 223(1):19-22, 2021. e-Pub 2021. PMID: 33083826.
- Wu C, Wu L, Wang J, Lin L, Li Y, Lu Q, Deng HW. Systematic identification of risk factors and drug repurposing options for Alzheimer's disease. Alzheimer's Dement 7(1):e12148, 2021. e-Pub 2021. PMID: 33718584.
- Liu D, Zhou D, Sun Y, Zhu J, Ghoneim D, Wu C, Yao Q, Gamazon ER, Cox NJ, Wu L. A transcriptome-wide association study identifies candidate susceptibility genes for pancreatic cancer risk. Cancer Research 80(20):4346-4354, 2020. e-Pub 2020. PMID: 32907841.
- Xue H, Wu C, Pan W. Leveraging existing GWAS summary data to improve power for a new GWAS. Genetic Epidemiology 44(7):717-732, 2020. e-Pub 2020. PMID: 32677173.
- Wu L, Yang Y, Guo X, Shu XO, Cai Q, Shu X, Li B, Tao R, Wu C, Nikas JB, Sun Y, Zhu J, Roobol MJ, Giles GG, Brenner H, John EM, Clements J, Grindedal EM, Park JY, Stanford JL, Kote-Jarai Z, Haiman CA, Eeles RA, Zheng W, Long J. An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk. Nature Communications 11(1):3905, 2020. e-Pub 2020. PMID: 32764609.
- Wu C. Multi-trait genome-wide analyses of the brain imaging phenotypes in UK biobank. Genetics 215(4):947-958, 2020. e-Pub 2020. PMID: 32540950.
- Zhu J, Shu X, Guo X, Liu D, Bao J, Milne RL, Giles GG, Wu C, Du M, White E, Risch HA, Malats N, Duell EJ, Goodman PJ, Li D, Bracci P, Katzke V, Neale RE, Gallinger S, Van Den Eeden SK, Arslan AA, Canzian F, Kooperberg C, Beane Freeman LE, Scelo G, Visvanathan K, Haiman CA, Le Marchand L, Yu H, Petersen GM, Stolzenberg-Solomon R, Klein AP, Cai Q, Long J, Shu XO, Zheng W, Wu L. Associations between genetically predicted blood protein biomarkers and pancreatic cancer risk. Cancer Epidemiology Biomarkers & Prevention 29(7):1501-1508, 2020. e-Pub 2020. PMID: 32439797.
- Yang T, Wu C, Wei P, Pan W. Integrating DNA sequencing and transcriptomic data for association analyses of low-frequency variants and lipid traits. Human Molecular Genetics 29(3):515-526, 2020. e-Pub 2020. PMID: 31919517.
- Wu C, Pan W. A powerful fine-mapping method for transcriptome-wide association studies. Human Genetics 139(2):199-213, 2020. e-Pub 2020. PMID: 31844974.
- Yang T, Kim J, Wu C, Ma Y, Wei P, Pan W. An adaptive test for meta-analysis of rare variant association studies. Genetic Epidemiology 44(1):104-116, 2020. e-Pub 2020. PMID: 31830326.
- Wu C, Xu G, Shen X, Pan W. A regularization-based adaptive test for high-dimensional generalized linear models. Journal of Machine Learning Research 21:128, 2020. e-Pub 2020. PMID: 32802002.
- Wu C, Pan W. Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes. Bioinformatics 35(19):3576-3583, 2019. e-Pub 2019. PMID: 30850848.
- Wu C, Xu G, Pan W. An adaptive test on high dimensional parameters in generalized linear models. Statistica Sinica 29(4):2163-2186, 2019. e-Pub 2019.
- Park JY, Wu C, Pan W. An adaptive gene-level association test for pedigree data. BMC Genetics 19(Suppl 1):68, 2018. e-Pub 2018. PMID: 30255770.
- Wu C, Pan W. Integration of enhancer-promoter interactions with GWAS summary results identifies novel schizophrenia-associated genes and pathways. Genetics 209(3):699-709, 2018. e-Pub 2018. PMID: 29728367.
- Wu C, Pan W. Integrating eQTL data with GWAS summary statistics in pathway based analysis. Genetic Epidemiology 42(3):303-316, 2018. e-Pub 2018. PMID: 29411426.
- Zhu L, Li Y, Chen YC, Carrera CA, Wu C, Fok A. Comparison between two post-dentin bond strength measurement methods. Scientific Reports 8(1):2350, 2018. e-Pub 2018. PMID: 29403067.
- Park JY, Wu C, Basu S, McGue M, Pan W. Adaptive SNP-Set Association Testing in Generalized Linear Mixed Models with Application to Family Studies. Behavior Genetics 48(1):55-66, 2018. e-Pub 2018. PMID: 29150721.
- Wu C, Park JY, Guan W, Pan W. An adaptive gene-based test for methylation data. BMC Proceedings 12(9):60, 2018. e-Pub 2018. PMID: 30275902.
- Xu Z, Wu C, Wei P, Pan W. A Powerful Framework for Integrating eQTL and GWAS Summary Data. Genetics 207(3):893-902, 2017. e-Pub 2017. PMID: 28893853.
- Xu Z, Wu C, Pan W, Neuroimaging Initiative AD. Imaging-wide association study: Integrating imaging endophenotypes in GWAS. Neuroimage 159:159-169, 2017. e-Pub 2017. PMID: 28736311.
- Liu B, Wu C, Shen X, Pan W. A novel and efficient algorithm for de novo discovery of mutated driver pathways. Annals of Applied Statistics 11(3):1481-1512, 2017. e-Pub 2017. PMID: 29479394.
- Wu C, Chen J, Kim J, Pan W. An adaptive association test for microbiome data. Genome Medicine 8(1):56, 2016. e-Pub 2016. PMID: 27198579.
- Wu C, Kwon S, Shen X, Pan W. A new algorithm and theory for penalized regression-based clustering. Journal of Machine Learning Research 17(188):1-25, 2016. e-Pub 2016. PMID: 31662706.
- Wu C, Demerath EW, Pankow JS, Bressler J, Fornage M, Grove ML, Chen W, Guan W. Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data. Epigenetics 11(2):132-139, 2016. e-Pub 2016. PMID: 26890800.
- Bose M, Wu C, Pankow JS, Demerath EW, Bressler J, Fornage M, Grove ML, Mosley TH, Hicks C, North K, Kao WH, Zhang Y, Boerwinkle E, Guan W. Evaluation of microarray-based DNA methylation measurement using technical replicates: the Atherosclerosis Risk In Communities (ARIC) Study. BMC Bioinformatics 15(1):312, 2014. e-Pub 2014. PMID: 25239148.
Other Articles
- Song M, Greenbaum J, Luttrell J 4th, Zhou W, Wu C, Shen H, Gong P, Zhang C, Deng HW A review of integrative imputation for multi-omics datasets. Frontiers in Genetics 11:570255, 2020. PMID: 33193667.
Book Chapters
- Wu C. Using R for cell-type composition imputation in epigenome-wide association studies. In: Epigenome-Wide Association Studies. Springer Nature, 2022.
Selected Presentations & Talks
Local Presentations
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. UTHealth Houston School of Public Health. Houston, Texas, US.
- 2020. Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation: a multi-phased study of prostate cancer. Invited. Florida State University School of Medicine. Tallahassee, Florida, US.
- 2017. An adaptive test on high dimensional parameters in GLMs. Poster. MSI Research Exhibition. Minneapolis, Minnesota, US.
Regional Presentations
- 2024. Large-scale imputation models for multi-ancestry proteome-wide association analysis. Invited. Division of Epidemiology Seminar, US.
- 2024. Large-scale imputation models for multi-ancestry proteome-wide association analysis. Invited. Mid South Computational Biology and Bioinformatics Society (MCBIOS) 2024, US.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. Department of Epidemiology and Biostatistics Seminar, US.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. Statistical Genetics/Genomics Journal Club, US.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. University of Hawaii Cancer Center Seminar, US.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. Indiana University School of Medicine Seminar, US.
- 2022. Accounting for winner’s curse and pleiotropy in two-sample Mendelian randomization. Invited. Causal Inference Working Group Journal Club, US.
- 2021. Accounting for winner’s curse and pleiotropy in two-sample Mendelian randomization. Invited. Biomedical Informatics & Genomics Center Seminar, US.
- 2021. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. New Investigator in AD and AFAR Grantee Conference, US.
- 2020. A regularization-based adaptive test for high-dimensional generalized linear models. Invited. Department of Biostatistics Seminar, US.
- 2017. A gene-level adaptive association test for methylation data. Invited. Genetic Analysis Workshop (GAW) 20. San Diego, CA, US.
National Presentations
- 2024. Large-scale imputation models for multi-ancestry proteome-wide association analysis. Invited. Association of Chinese Americans in Cancer Research(ACACR) summer seminar, US.
- 2022. Accounting for winner’s curse and pleiotropy in two-sample Mendelian randomization. Invited. The ICSA 2022 Applied Statistics Symposium. Gainesville, FL, US.
- 2019. An adaptive test for high-dimensional generalized linear models with application to detect gene-environment interactions. Invited. ENAR 2019 Spring Meeting. Philadelphia, PA, US.
- 2018. Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes. Conference. IGES 27th Annual Meeting. San Diego, California, US.
- 2017. An adaptive test on high dimensional parameters in GLMs. Invited. ENAR 2017 Spring Meeting. Washington, DC, US.
- 2016. An adaptive association test for microbiome data. Invited. Eastern North American Region (ENAR) 2016 Spring Meeting. Austin, TX, US.
International Presentations
- 2024. Large-scale imputation models for multi-ancestry proteome-wide association analysis. Invited. 2024 Joint Statistical Meetings. Portland, US.
- 2024. Large-scale imputation models for multi-ancestry proteome-wide association analysis. Invited. ICSA 2024 China Conference. Wuhan, CN.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. The 12th International Chinese Statistical Association (ICSA) International Conference. Hong Kong, CN.
- 2023. SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification. Invited. 2023 International Chinese Statistical Association (ICSA) Applied Statistics Symposium. Ann Arbor, US.
- 2021. Accounting for winner’s curse and pleiotropy in two-sample Mendelian randomization. Invited. Department of Statistics Seminar, US.
- 2021. Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation: a multi-phased study of prostate cancer. Conference. Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation: a multi-phased study of prostate cancer, US.
- 2020. A regularization-based adaptive test for high-dimensional generalized linear models. Invited. School of Statistics and Management Seminar, US.
- 2020. A gene-level methylome-wide association analysis identifies novel Alzheimer’s disease genes. Poster. ASHG 2020 Annual Meeting, US.
- 2020. A powerful fine-mapping method for transcriptome-wide association studies. Invited. 2020 Joint Statistical Meetings, US.
- 2019. Multi-trait genome-wide analyses of the brain imaging phenotypes in UK Biobank. Conference. ASHG 2019 Annual Meeting. Houston, US.
- 2019. Complex disease risk prediction via a deep learning method. Invited. 2019 Joint Statistical Meetings. Denver, US.
- 2017. Integrating eQTL data with GWAS summary statistics in pathway-based analysis. Conference. ASHG 2017 Annual Meeting. Orlando, US.
- 2017. An adaptive test on high dimensional parameters in GLMs. Invited. 2017 Joint Statistical Meetings. Baltimore, US.
- 2016. Iterative PCA in epigenome-wide association studies. Poster. American Society of Human Genetics (ASHG) 2016 Annual Meeting. Vancouver, CA.
- 2016. An adaptive association test for microbiome data. Invited. 2016 Joint Statistical Meetings (JSM). Chicago, US.
Grant & Contract Support
| Date: | 2024 - 2029 |
| Title: | Uncovering Causal Protein Markers to Characterize Pancreatic Cancer Etiology and Improve Risk Prediction |
| Funding Source: | NIH/NCI |
| Role: | Co-PI |
| ID: | 1U01CA293883-01 |
| Date: | 2024 - 2027 |
| Title: | Investigating the Impact of Interferon Gamma Signaling on Therapeutic Resistance in Acute Myeloid Leukemia |
| Funding Source: | Cancer Prevention & Research Institute of Texas (CPRIT) |
| Role: | Biostatistician |
| ID: | RP240287 |
| Date: | 2023 - 2026 |
| Title: | Targeting Immune-checkpoint Protein B7-H3 in Acute Myeloid Leukemia |
| Funding Source: | Department of Defense RCRP-IDA |
| Role: | Other Key |
| ID: | RA220172 |
| Date: | 2023 - 2027 |
| Title: | Trans-omics Integration of Multi-omics Studies for Osteoporosis |
| Funding Source: | Tulane University |
| Role: | PD/PI |
| ID: | U19AG055373 |
| Date: | 2022 - 2027 |
| Title: | Uncovering causal protein markers to improve prostate cancer etiology understanding and risk prediction in Africans and Europeans |
| Funding Source: | NIH/NCI |
| Role: | Co-PI |
| ID: | 1R01CA263494-01A1 |
| Date: | 2022 - 2027 |
| Title: | Spatial and temporal tumor-immune co-evolution and interactions that model lung adenocarcinoma development |
| Funding Source: | NIH/NCI |
| Role: | Co-I |
| ID: | 1U01CA264583-01A1 |
| Date: | 2019 - 2026 |
| Title: | Cancer Center Support Grant (CCSG) - Biostatistics Resource Group (BRG) |
| Funding Source: | NIH/NCI |
| Role: | Biostatistician |
| ID: | 5P30CA016672-46 |
Patient Reviews
CV information above last modified May 15, 2025