Xin He

The paper identifies context-specific genetic effects in neuronal activation using single-cell multiomics, revealing NPD risk variants and genes active during stimulation.

This paper introduces FGMB, a multi-context regulome-wide association atlas, and applies it to Alzheimer's disease to identify 327 regulatory associations and resolve 86 gene-molecular-trait pairs.

DiffDriver identifies context-specific selection of cancer driver genes by accounting for mutation rate variations and functional sequence information, revealing differential selection in clinical traits and immune subtypes.

This study develops M-cTWAS to integrate multi-omics and multi-context QTL data with GWAS, improving the discovery of risk genes for complex traits.

The paper investigates DNA breathing's role in TF-DNA interactions using in vitro and in vivo data, finding correlations between breathing features near motifs and binding affinity across multiple TFs.

The paper constructs a cell-type-resolved chromatin accessibility atlas in the human intestine and identifies regulatory programs and genetic variants associated with Crohn's disease.

This study integrates single-cell epigenomics with GWAS to identify risk variants and genes for Atrial Fibrillation, highlighting key transcription factors and pathways.

DeepGWAS enhances GWAS signals for neuropsychiatric disorders using deep neural networks, improving locus detection in schizophrenia and validating results in other disorders.

This paper introduces a method to adjust for genetic confounders in transcriptome-wide association studies, improving reliability in detecting causal genes.

The paper shows ancestry-related differences in transcriptional response to influenza are driven by epigenetic variation and genetic control of enhancer activity.

The paper identifies multiple genes in cis regulated by a chromatin accessibility variant that contribute to synaptic dysfunction in human neurons associated with schizophrenia.

The paper introduces MetaChrom, a deep transfer learning framework for predicting epigenomic functions of genetic variants in neurodevelopment, showing improved accuracy over existing methods.

The paper integrates GWAS data with functional genomic annotations of decidual stromal cells to identify loci and target genes associated with gestational duration and preterm birth.

This paper introduces a new Mendelian randomization method, CAUSE, that accounts for correlated and uncorrelated pleiotropic effects, showing improved robustness in detecting true causal relationships.

The paper identifies de novo mutations affecting post-transcriptional regulation that contribute to four neuropsychiatric disorders.

The paper identifies allele-specific open chromatin (ASoC) variants in human iPSC-derived neurons and shows their strong enrichment in brain disorder risk variants.

The paper introduces CCmed methods to detect robust trans-eQTLs and assess their effects on human traits by analyzing brain tissue data from GTEx and validating findings with schizophrenia GWAS data.

driverMAPS improves cancer driver gene detection by modeling positive selection and mutation patterns, identifying 159 new potential driver genes including RNA methyltransferases.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

mTADA jointly analyzes de novo mutation data from multiple neuropsychiatric disorders to identify shared and specific risk genes.

This paper introduces a statistical framework to identify risk genes from de novo mutations in whole-genome sequencing studies, finding new autism risk genes.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.