Hanmin Guo

MOD-scTWAS improves single-cell TWAS by jointly modeling gene co-expression modules, enhancing GReX prediction accuracy and identifying more significant cell-type-gene-trait associations.

This study identifies C4 protein expression in neutrophils and monocytes in schizophrenia, linking it to C4A gene copy number only in patients.

This paper introduces a statistical framework to prioritize disease-related rare variants by integrating gene expression data, showing improved sensitivity in small sample sizes and identifying 16 rare variants in Alzheimer's disease.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

This paper introduces X-Wing, a framework that improves genetic prediction across populations using GWAS summary statistics by identifying portable genetic effects.

The paper develops a method to detect complex structural variants across human genomes and finds they are associated with phenotypic hotspots and evolutionary changes.

This paper introduces EncoreDNM, a statistical framework to quantify shared genetic effects of de novo mutations between disorders using exome-wide summary data, revealing abundant pairwise enrichment correlations.

This paper introduces LOGODetect, a statistical method to identify local genetic correlation signals across multiple phenotypes using GWAS summary statistics, finding 49 non-overlapping genome regions associated with psychiatric disorders.