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
mTADA is a framework for identifying risk genes from de novo mutations in multiple traits.
Nguyen TH, Dobbyn A, Brown RC, Riley BP, Buxbaum JD, Pinto D, Purcell SM, Sullivan PF, He X, Stahl EA. mTADA is a framework for identifying risk genes from de novo mutations in multiple traits. Nat Commun. 2020 Jun 10; 11(1):2929.
Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.
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 May 25.
Detailed modeling of positive selection improves detection of cancer driver genes.
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.
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 06 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.
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.
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.
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.