Research Interests

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My method reserach has been mostly motivated by problems in genetics, genoimics and metagenomics. I have worked on a variety of problems in statistical genetics and genomics, including methods for family-based genetic linkage and association analysis, methods for admixture mapping, methods for genome-wide association analysis, methods for analysis of microarray time course gene expression data, high dimensional regression analysis for genomic data, methods for copy number variation analysis and methods for analysis of next generation sequence data. I have published both statistical methodological research in top statistics/biostatistics journals (JASA, AOS, AOAS, Biometrika, Biometrics, Biostatistics etc ) and in top genetics journals (AJHG, Plos Genetics, etc) and collaborative research in top scientific journals (Science, NEJM, Nature, Nature Genetics, PNAS, Developmental Cell, Cancer Cell etc).

My current collaboration includes several genome-wide association studies, analysis of human microbiome data, analysis of heart failure eQTL data and genomics of ovarian cancer.

Statistical Methods Publications

  1. Li H (2015): Microbiome, Metagenomics and High Dimensional Compositional Data Analysis. Annual Review of Statistics and Its Application, in press.
  2. *Lin W, Feng R and Li H (2014): Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics. Journal of American Statistical Association, Theory and Methods, accepted.
  3. *Lin W, Shi P, Feng R and Li H (2014): Variable selection in regression with compositional covariates. Biometrika, accepted.
  4. *Zhao, S.D., Cai T and Li H (2014): Direct estimation of differential networks. Biometrika, 101(2): 253-268.
  5. *Jeng XJ, Cai TT and Li H (2013): Simultaneous Discovery of Rare and Common Segment Variants. Biometrika, 100(1), 157-172.
  6. Deng W, Geng Z, Li H (2013): Learning Local Directed Acyclic Graphs Based On Multivariate Time Series Data. Annals of Applied Statistics, 7: 1663-1683.
  7. Cai TT, Li H, Liu W and *Xie J (2013): Covariate-Adjusted Precision Matrix Estimation with an Application in Genetical Genomics. Biometrika, 100(1), 139-156.
  8. *Chen J and Li H (2013):Variable Selection for Sparse Dirichlet-Multinomial Regression with An Application to Microbiome Data Analysis. Annals of Applied Statistics, 7(1): 418-442.
  9. Cai T, Jeng J and Li H (2012): Robust Detection and Identification of Sparse Segments in Ultra-High Dimensional Data Analysis. Journal of Royal Statistical Society, Series B., 74 (5): 773-797.
  10. Daye J, Xie J, Li H (2012): A Sparse Structured Shrinkage Estimator for Nonparametric Varying-Coefficient Model with an Application in Genomics. Journal of Computational and Graphical Statistics, 21 (1): 110-133.
  11. Huang J, Ma S, Li H and Zhang CH (2011): The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression. Annals of Statistics, 39(4), 2021-2046.
  12. *Yin J and Li H (2011): A Sparse Conditional Gaussian Graphical Model for Analysis of Genetical Genomics Data. Annals of Applied Statistics, 5(4): 2630-2650.
  13. Lu T, Liang H, Li H , Wu H (2011): High Dimensional ODEs Coupled with Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification. Journal of American Statistical Association, 106: 1242-1258.
  14. Xie J, Cai TT, Li H (2011): Sample Size and Power Analysis for Sparse Signal Recovery in Genome-Wide Association Studies. Biometrika, 98(2), 273-290.
  15. Jeng J, Cai TT and Li H (2010): Optimal sparse segment identification with application in copy number variation analysis. Journal of American Statistical Association, 105 (491): 1156-1166.
  16. *Li C and Li H (2010): Variable Selection and Regression Analysis for Graph-Structured Covariates with an Application to Genomics. Annals of Applied Statistics, 4(3): 1498-1516.
  17. *Wang L, Li H and Huang J (2008): Variable selection in nonparametric varying-coefficient models for analysis of repeated measurements. Journal of the American Statistical Association, 103: 1556-1569.
  18. *Monni S and Li H (2008): Vertex clustering of graphs using reversible jump MCMC. Journal of Computational and Graphical Statistics, 17(2): 388-409.
  19. *Wei Z and Li H (2008): A Hidden Spatial-temporal Markov Random Field Model for Network-based Analysis of Time Course Gene Expression Data. Annals of Applied Statistics, 2(1), 408-429.
Collaborative Publications
  1. Hu X, Feng Y, Zhang D., Zhao SD, Greshock J, Hu Z, Zhang Y, Yang L, Wang L-P, Jean S, Li C, Huang Q, Katsaros D, Montone K, Tanyi JL, Lu Y, Boyd J, Nathanson KL, Li H, Mills H, Lin Z (2014): A Functional genomic approach identifies FAL1 as an oncogenic long noncoding RNA that associates with BMI1 and represses p21 expression in human cancer. Cancer Cell, accepted.
  2. Koeth RA, Wang Z, Levison BS, Buffa JA, Org E, Sheehy B, Britt EB, Fu X, Wu Y, Li L, Smith JD, DiDonato JA, Chen J, Li H, Wu GD, Lewis JD, Warrier M, Brown JM, Krauss RM, Tang WHW, Bushman FD, Lusis AJ, and Hazen SL (2013): Gut microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Medicine, 19: 576-585.
  3. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li H, Bushman FD, Lewis JD (2011): Linking long-term dietary patterns with gut microbial enterotypes. Science, 334:105-108.
  4. Winter C, Austin RS, Blanvillain-Baufumé B, Reback MA, Monniaux M, Wu MF, Sang Y, Yamaguchi A, Yamaguchi N, Parker JE, Parcy F, Jensen ST, Li H and Wagner D (2011): LEAFY Target Genes Reveal a Direct Link Between Biotic Stimulus Response and Flower. Developmental Cell, 20: 430-443.
  5. Wang K, SJ. Diskin, H Zhang, EF Attiyeh, C Winter, C Hou, RW Schnepp, M Diamond, K Bosse, PA. Mayes, J Glessner, C Kim, E Frackelton, M Garris, Q Wang, W Glaberson, R Chiavacci, L Nguyen, J Jagannathan, N Saeki, H Sasaki, SFA Grant, A Iolascon, YP Mosse, KA Cole, Li H, M Devoto, PW McGrady, WB London, M Capasson, N Rahman, H Hakonarson & JM Maris (2011): Integrative genomics identifies LMO1 as a neuroblastoma oncogene. Nature, 469: 216-220.
  6. *Nguyen LB, Diskin SJ, Cappasso M, Wang K, Diamond MA, Glessner J, Kim C, Attiyeh EF, Mosse YP, Cole K, Lolascon A, Devoto M, Hakonarson H, Li H, Maris JM (2011): Phenotype Restricted Genome-Wide Association Study Using a Gene-Centric Approach Identifies Three Low-Risk Neuroblastoma Susceptibility Loci. PLoS Genetics, 7(3): e1002026. doi:10.1371/journal.pgen.1002026.
  7. Sleiman P, Marcin Imielinski, Jonathan P. Bradfield, Kiran Annaiah, Saffron A.G. Willis-Owen, Nicholas M. Rafaels, Sven Michel, Klaus Bønnelykke, Cecilia E. Kim, Edward C. Frackelton, Joseph T. Glessne, Cuiping Hou, James Flory, F. George Otieno, Erin Santa, Kelly Thomas, Ryan M. Smith,Wendy R. Glaberson, Maria Garris, Rosetta M. Chiavacci, Terri H Beaty, Ingo Ruczinski, Julian Allen, Jonathan M. Spergel, Robert Grundmeier, Rasika A. Mathias, Jason D. Christie, Erika von Mutius, William O.C. Cookson, Michael Kabesch, Miriam F. Moffatt, Michael M. Grunstein, Li H, Kathleen C. Barnes, Marcella Devoto, Mark Magnusson, Struan F.A. Grant, Hans Bisgaard and Hakon Hakonarson (2010): A locus on 1q31 harboring /DENND1B/ is associated with asthma susceptibility in both Caucasian and African American children. New England Journal of Medicine, 362: 36-44.
  8. Diskin, SJ, Cuiping Hou, Joseph T. Glessner, Edward F. Attiyeh, Marci Laudenslager, Kristopher Bosse, Kristina Cole, Yael P. Mosse, Andrew Wood, Jill E. Lynch, Katlyn Pecor, Maura Diamond, Cynthia Winter, Kai Wang, Cecilia Kim, Elizabeth A. Geiger, Patrick W. McGrady, Alexandra I. F. Blakemore, Wendy B. London, Tamim H. Shaikh, Jonathan Bradfield, Struan F. A. Grant, Li H, Marcella Devoto, Eric R. Rappaport, Hakon Hakonarson, John M. Maris (2009): Copy number variation at 1q21.1 associated with neuroblastoma. Nature, 459: 987-991.
  9. Capasso M, Hou C, Asgharzadeh S, Attiyeh EF, Mosse YP, Diskin SJ, Cole K, Bosse K, Diamond M, Laudenslager M, Winter C, Bradfield JP, Scott RH, Jagannathan J, Glessner JT, Kim C, London, WB, Seeger RC, Li H, Rahman N, Rappaport E, Hakonarson H, Devoto M, Maris J (2009): Common variations in the BARD1 tumor suppressor gene influence susceptibility to high-risk neuroblastoma. Nature Genetics, 41(6): 718-723.
  10. Kamoun M, Holmes JH, Israni AK, Kearns JD, Teal V, Yang W, Rosasa SE, Joffe MM, Li H , Feldman HI (2008): HLA-A amino acid polymorphism and delayed kidney allograft function. Proceedings of National Academy of Sciences, 105(48): 18883-18888.
  11. Maris JM, Yael PM, Bradfield JP, Hou C, Monni S, Scott RH, Asgharzadeh S, Attiveh EF, Diskin SJ, Laudenslager M, Winter C, Cole K, Glessner JT, Kim C, Frackelton EC, Casalunovo T, Eckert AW, Capasso M, Rappaport EF, McConville C, London WB, Seeger RC, Rahman N, Devoto M, Grant SFA, Li H and Hakonarson H (2008) : A genome-wide association study identifies a susceptibility locus to clinically aggressive neuroblastoma at 6p22. New England Journal of Medicine, 358: 2585-2593.