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Xin He

Associate Professor
xinhe@uchicago.edu
Websites
Research Summary
My lab uses computational approaches to study the genetics of human diseases, including cancer. A primary focus of our research is to develop novel tools for mapping risk genes of complex diseases from genome wide association studies (GWAS), sequencing studies or somatic mutations in the case of cancer. These tools are often been used in close collaboration with experimental biologists. A key feature of our strategy is the integration of multiple genomic datasets, such as transcriptome data, epigenetic data, and biological networks. This integrated approach could combine signals in different datasets to increase the power of studies, and shed light on the mechanism connecting genetic changes to phenotypes. We are also interested in computational questions in regulatory genomics. How do cis-regulatory sequences interpret the information in cellular environments to drive spatial-temporal gene expression patterns? How do variations of regulatory sequences shape phenotypic variation and evolution? We believe a better understanding of these questions will also help the study of human genetics, specifically by improving our ability to interpret variations in non-coding sequences.
Keywords
Statistical genetics, Computational biology, Psychiatric genetics, Cancer genomics, Gene regulation
Education
  • University of Illinois, Urbana-Champaign, PhD Computer Science 09/2009
  • University of California, San Francisco, Postdoc Statistical genetics 07/2011
  • Carnegie Mellon University, Pittsburgh, Postdoc Computational Biology 08/2014
Publications

  1. Lai B, Qian S, Zhang H, Zhang S, Kozlova A, Duan J, Xu J, He X. Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning. PLoS Comput Biol. 2022 05; 18(5):e1010011. View in: PubMed

  2. Sakabe NJ, Aneas I, Knoblauch N, Sobreira DR, Clark N, Paz C, Horth C, Ziffra R, Kaur H, Liu X, Anderson R, Morrison J, Cheung VC, Grotegut C, Reddy TE, Jacobsson B, Hallman M, Teramo K, Murtha A, Kessler J, Grobman W, Zhang G, Muglia LJ, Rana S, Lynch VJ, Crawford GE, Ober C, He X, N?brega MA. Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth. Sci Adv. 2020 12; 6(49). View in: PubMed

  3. Zhang S, Zhang H, Zhou Y, Qiao M, Zhao S, Kozlova A, Shi J, Sanders AR, Wang G, Luo K, Sengupta S, West S, Qian S, Streit M, Avramopoulos D, Cowan CA, Chen M, Pang ZP, Gejman PV, He X, Duan J. Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science. 2020 07 31; 369(6503):561-565. View in: PubMed

  4. Zhang Z, Luo K, Zou Z, Qiu M, Tian J, Sieh L, Shi H, Zou Y, Wang G, Morrison J, Zhu AC, Qiao M, Li Z, Stephens M, He X, He C. Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nat Genet. 2020 09; 52(9):939-949. View in: PubMed

  5. Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020 07; 52(7):740-747. View in: PubMed

  6. Zhao S, Liu J, Nanga P, Liu Y, Cicek AE, Knoblauch N, He C, Stephens M, He X. Detailed modeling of positive selection improves detection of cancer driver genes. Nat Commun. 2019 07 30; 10(1):3399. View in: PubMed

  7. Liu Y, Liang Y, Cicek AE, Li Z, Li J, Muhle RA, Krenzer M, Mei Y, Wang Y, Knoblauch N, Morrison J, Zhao S, Jiang Y, Geller E, Ionita-Laza I, Wu J, Xia K, Noonan JP, Sun ZS, He X. A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies. Am J Hum Genet. 2018 06 07; 102(6):1031-1047. View in: PubMed

  8. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, Murtha MT, Bal VH, Bishop SL, Dong S, Goldberg AP, Jinlu C, Keaney JF, Klei L, Mandell JD, Moreno-De-Luca D, Poultney CS, Robinson EB, Smith L, Solli-Nowlan T, Su MY, Teran NA, Walker MF, Werling DM, Beaudet AL, Cantor RM, Fombonne E, Geschwind DH, Grice DE, Lord C, Lowe JK, Mane SM, Martin DM, Morrow EM, Talkowski ME, Sutcliffe JS, Walsh CA, Yu TW. Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci. Neuron. 2015 Sep 23; 87(6):1215-1233. View in: PubMed

  9. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, Kou Y, Liu L, Fromer M, Walker S, Singh T, Klei L, Kosmicki J, Shih-Chen F, Aleksic B, Biscaldi M, Bolton PF, Brownfeld JM, Cai J, Campbell NG, Carracedo A, Chahrour MH, Chiocchetti AG, Coon H, Crawford EL, Curran SR, Dawson G, Duketis E, Fernandez BA, Gallagher L, Geller E, Guter SJ, Hill RS, Ionita-Laza J, Jimenz Gonzalez P, Kilpinen H, Klauck SM, Kolevzon A, Lee I, Lei I, Lei J, Lehtim?ki T, Lin CF, Ma'ayan A, Marshall CR, McInnes AL, Neale B, Owen MJ, Ozaki N, Parellada M, Parr JR, Purcell S, Puura K, Rajagopalan D, Rehnstr?m K, Reichenberg A, Sabo A, Sachse M, Sanders SJ, Schafer C, Schulte-R?ther M, Skuse D, Stevens C, Szatmari P, Tammimies K, Valladares O, Voran A, Li-San W, Weiss LA, Willsey AJ, Yu TW, Yuen RK. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014 Nov 13; 515(7526):209-15. View in: PubMed
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