Jingni He

MOKA integrates multi-omics data into GWAS using a kernel association test pipeline, identifying 89 significant genes in schizophrenia with 15.7% validation in DisGeNET.

The paper compares Nanopore direct RNA-seq and Illumina cDNA-seq in sepsis patients, finding strong correlation and identifying post- and co-transcriptional biomarkers.

This study introduces transTF-TWAS, a method that integrates transcription factor-linked trans-located variants into transcriptome-wide association analyses to improve disease risk gene discovery.

IMAS, a statistical method, uses image-derived phenotypes from the UK Biobank to discover genetic associations with four brain disorders, revealing a common cerebellar mechanism.

The paper introduces cLD, a statistic for measuring rare-variant linkage disequilibrium between genomic regions, and shows it identifies novel genomic interactions and associations.

rvTWAS integrates gene expressions with rare variant sequences to identify gene-trait associations in psychiatric disorders.

The paper identifies 58 risk genes associated with common cancers by linking genetically predicted APA levels to cancer risk using GWAS data.

This paper shows that combining feature selection and aggregation in GReX limits TWAS power and adaptability, especially with low expression heritability.

The paper introduces ILMM, a statistical model that integrates genetic interactions into genotype-phenotype association mapping, revealing 3D-genetic basis of ASD and 3D-eQTLs using whole genome and transcriptome sequencing data.

PoolHapX reconstructs microbial haplotypes by integrating genetic sharing and genomic sequencing, outperforming existing tools and revealing new insights into HIV evolution.