Xihao Li, PhD

About

Dr. Xihao Li is an Assistant Professor in the Department of Biostatistics and the Department of Genetics at UNC-Chapel Hill.

His research interests lie in developing novel statistical and machine learning methods that enable scalable and integrative analysis of large whole-genome/exome sequencing (WGS/WES) data and multi-omics data, meta-analysis of WGS/WES studies from ancestrally-diverse consortiums and biobanks, multiple phenotype analysis to identify pleiotropic genetic effects, and prioritization of putative causal genetic variants using functional annotation data to better understand the relationships among genomic variation, genome function, and phenotypes. He has also worked on methodological projects to develop statistical approaches for rare disease clinical trials and real-world evidence studies.

Honors and Awards

CHARGE Travel Award
2023, The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium

Distinguished Student Paper Award in the Section on Statistics in Genomics and Genetics
2022, American Statistical Association

Charles J. Epstein Trainee Awards for Excellence in Human Genetics Research Semifinalist
2020, The American Society of Human Genetics

Robert B. Reed Prize
2017, Department of Biostatistics, Harvard T.H. Chan School of Public Health

Research Activities

Statistical Genetics & Genomics, Integrative Analysis of WGS/WES and Multi-Omics Data, Functional Genomics and Annotations, Data Integration and Meta-Analysis, Multivariate Analysis, Machine Learning.

Service Activities

Meeting/Session Organizer:

• NHLBI Trans-Omics for Precision Medicine (TOPMed) Annual Meeting (2024)
• ASA Boston Chapter, Boston Pharmaceutical Symposium (2023, 2022)

Referee for:

• American Journal of Human Genetics
• Bioinformatics
• BMC Bioinformatics
• Cell Genomics
• Frontiers in Genetics
• Genetic Epidemiology
• Human Molecular Genetics
• JAMA Network Open
• Journal of Computational Biology
• Journal of Genetics and Genomics
• Journal of Translational Medicine
• Molecular Genetics and Genomics
• PLOS Computational Biology
• PLOS Genetics
• PLOS ONE
• R Journal
• Statistical Methods in Medical Research
• The American Statistician

Key Publications

Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole-genome sequencing studies. Li, X., Quick, C., Zhou, H., Gaynor, S.M., Liu, Y., Chen, H., Selvaraj, M.S., Sun, R., Dey, R., Arnett, D.K., Bielak, L.F., Bis, J.C., Blangero, J., Boerwinkle, E., Bowden, D.W., Brody, J.A., Cade, B.E., Correa, A., Cupples, L.A., Curran, J.E., de Vries, P.S., Duggirala, R., Freedman, B.I., Göring, H.H.H., Guo, X., Haessler, J., Kalyani, R.R., Kooperberg, C., Kral, B.G., Lange, L.A., Manichaikul, A., Martin, L.W., McGarvey, S.T., Mitchell, B.D., Montasser, M.E., Morrison, A.C., Naseri, T., O'Connell, J.R., Palmer, N.D., Peyser, P.A., Psaty, B.M., Raffield, R.M., Redline, S., Reiner, A.P., Reupena, M.S., Rice, K.M., Rich, S.S., Sitlani, C.M., Smith, J.A., Taylor, K.D., Vasan, R.S., Willer, C.J., Wilson, J.G., Yanek, L.R., Zhao, W. NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Rotter, J.I., Natarajan, P., Peloso, G.M., Li, Z., & Lin, X. (2023). Nature Genetics, 55(1), 154-164.

A multi-dimensional integrative scoring framework for predicting functional variants in the human genome. Li, X.*, Yung, G.*, Zhou, H., Sun, R., Li, Z., Hou, K., Zhang, M.J., Liu, Y., Arapoglou, T., Wang, C., Ionita-Laza, I., & Lin, X. (2022). The American Journal of Human Genetics, 109(3), 446-456.

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies. Li, Z.*, Li, X.*, Zhou, H., Gaynor, S.M., Selvaraj, M.S., Arapoglou, T., Quick, C., Liu, Y., Chen, H., Sun, R., Dey, R., Arnett, D.K., Auer, P.L., Bielak, L.F., Bis, J.C., Blackwell, T.W., Blangero, J., Boerwinkle, E., Bowden, D.W., Brody, J.A., Cade, B.E., Conomos, M.P., Correa, A., Cupples, L.A., Curran, J.E., de Vries, P.S., Duggirala, R., Franceschini, N., Freedman, B.I., Göring, H.H.H., Guo, X., Kalyani, R.R., Kooperberg, C., Kral, B.G., Lange, L.A., Lin, B.M., Manichaikul, A., Martin, L.W., Mathias, R.A, Meigs, J.B., Mitchell, B.D., Montasser, M.E., Morrison, A.C., Naseri, T., O'Connell, J.R., Palmer, N.D., Peyser, P.A., Psaty, B.M., Raffield, R.M., Redline, S., Reiner, A.P., Reupena, M.S., Rice, K.M., Rich, S.S., Smith, J.A., Taylor, K.D., Taub, M.A., Vasan, R.S., Weeks, D.E., Wilson, J.G., Yanek, L.R., Zhao, W., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Rotter, J.I., Willer, C.J., Natarajan, P., Peloso, G.M., & Lin, X. (2022). Nature Methods, 19(12), 1599-1611.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole genome sequencing studies at scale. Li, X.*, Li, Z.*, Zhou, H., Gaynor, S.M., Liu, Y., Chen, H., Sun, R., Dey, R., Arnett, D.K., Aslibekyan, S., Ballantyne, C.M., Bielak, L.F., Blangero, J., Boerwinkle, E., Bowden, D.W., Broome, J.G., Conomos, M.P., Correa, A., Cupples, L.A., Curran, J.E., Freedman, B.I., Guo, X., Hindy, G., Irvin, M.R., Kardia, S.L.R., Kathiresan, S., Khan, A.T., Kooperberg, C.L., Laurie, C.C., Liu, X.S., Mahaney, M.C., Manichaikul, A.W., Martin, L.W., Mathias, R.A., McGarvey, S.T., Mitchell, B.D., Montasser, M.E., Moore, J.E., Morrison, A.C., O’Connell, J.R., Palmer, N.D., Pampana, A., Peralta, J.M., Peyser, P.A., Psaty, B.M., Redline, S., Rice, K.M., Rich, S.S., Smith, J.A., Tiwari, H.K., Tsai, M.Y., Vasan, R.S., Wang, F.F., Weeks, D.E., Weng, Z., Wilson, J.G., Yanek, L.R., NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Neale, B.M., Sunyaev, S.R., Abeçasis, G.R., Rotter, J.I., Willer, C.J., Peloso, G.M., Natarajan, P., & Lin, X. (2020). Nature Genetics, 52(9), 969-983.

Target population statistical inference with data integration across multiple sources - an approach to mitigate information shortage in rare disease clinical trials. Li, X., & Song, Y. (2020). Statistics in Biopharmaceutical Research, 12(3), 322-333.

Education