Our 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 data of GWAS, family studies or somatic mutations in the case of cancer. We are also interested in related questions, such as how to predict functional significance of DNA mutations; how genes and environmental factors together influence the disease risks and what is the role of dysregulation of gene expression in diseases.
We develop and employ computational or statistical tools to address our questions, and work closely with geneticists and 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. Furthermore, by putting DNA variations in the context of gene interaction and regulatory networks, it is possible to better understand 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 regulatory sequences change during 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.
Carnegie Mellon University
Postdoc - Computational Biology
University of California
Postdoc - Statistical genetics
University of Illinois
PhD - Computer Science
MS - Computer Science
University of Science and Technology of China
BS - Biological science
Jump-seq: Genome-Wide Capture and Amplification of 5-Hydroxymethylcytosine Sites.
Hu L, Liu Y, Han S, Yang L, Cui X, Gao Y, Dai Q, Lu X, Kou X, Zhao Y, Sheng W, Gao S, He X, He C. Jump-seq: Genome-Wide Capture and Amplification of 5-Hydroxymethylcytosine Sites. J Am Chem Soc. 2019 Jun 05; 141(22):8694-8697.
A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies.
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.
Evolution of transcript modification by N6-methyladenosine in primates.
Ma L, Zhao B, Chen K, Thomas A, Tuteja JH, He X, He C, White KP. Evolution of transcript modification by N6-methyladenosine in primates. Genome Res. 2017 03; 27(3):385-392.
Insights into Autism Spectrum Disorder Genomic Architecture and Biology from 71 Risk Loci.
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.
De novo ChIP-seq analysis.
He X, Cicek AE, Wang Y, Schulz MH, Le HS, Bar-Joseph Z. De novo ChIP-seq analysis. Genome Biol. 2015 Sep 23; 16:205.
Synaptic, transcriptional and chromatin genes disrupted in autism.
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, Lehtimäki 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, Rehnström K, Reichenberg A, Sabo A, Sachse M, Sanders SJ, Schafer C, Schulte-Rüther 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.
Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes.
He X, Sanders SJ, Liu L, De Rubeis S, Lim ET, Sutcliffe JS, Schellenberg GD, Gibbs RA, Daly MJ, Buxbaum JD, State MW, Devlin B, Roeder K. Integrated model of de novo and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 2013; 9(8):e1003671.
Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS.
He X, Fuller CK, Song Y, Meng Q, Zhang B, Yang X, Li H. Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS. Am J Hum Genet. 2013 May 02; 92(5):667-80.
Evolutionary origins of transcription factor binding site clusters.
He X, Duque TS, Sinha S. Evolutionary origins of transcription factor binding site clusters. Mol Biol Evol. 2012 Mar; 29(3):1059-70.
Thermodynamics-based models of transcriptional regulation by enhancers: the roles of synergistic activation, cooperative binding and short-range repression.
He X, Samee MA, Blatti C, Sinha S. Thermodynamics-based models of transcriptional regulation by enhancers: the roles of synergistic activation, cooperative binding and short-range repression. PLoS Comput Biol. 2010 Sep 16; 6(9).