Our Faculty

Xin He

My lab uses computational approaches to study the genetics of human diseases. A primary focus of our research is to develop novel tools for mapping risk genes of complex diseases from genome wide association studies (GWAS) and sequencing studies. 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.

Carnegie Mellon University
Pittsburgh
Postdoc - Computational Biology
2014

University of California
San Francisco
Postdoc - Statistical genetics
2011

University of Illinois
Urbana-Champaign
PhD - Computer Science
2009

Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons.
Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons. Cell Rep. 2023 11 28; 42(11):113335.
PMID: 37889749

A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening.
A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening. Nat Methods. 2023 Nov; 20(11):1693-1703.
PMID: 37770710

Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation.
Single-cell genomics improves the discovery of risk variants and genes of atrial fibrillation. Nat Commun. 2023 08 17; 14(1):4999.
PMID: 37591828

Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning.
Annotating functional effects of non-coding variants in neuropsychiatric cell types by deep transfer learning. PLoS Comput Biol. 2022 05; 18(5):e1010011.
PMID: 35576194

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth.
Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth. Sci Adv. 2020 12; 6(49).
PMID: 33268355

Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants.
Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants. Science. 2020 07 31; 369(6503):561-565.
PMID: 32732423

Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability.
Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability. Nat Genet. 2020 09; 52(9):939-949.
PMID: 32601472

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 06 10; 11(1):2929.
PMID: 32522981

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.
Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020 07; 52(7):740-747.
PMID: 32451458

Detailed modeling of positive selection improves detection of cancer driver genes.
Detailed modeling of positive selection improves detection of cancer driver genes. Nat Commun. 2019 07 30; 10(1):3399.
PMID: 31363082

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