Lide Han

UK BioCoin enables trait-specific covariate adjustment for UK Biobank summary statistics without individual-level data, achieving high correlation with full GWAS results.

The paper identifies convergent coexpressed genes shared between common and rare variant risk genes in six neuropsychiatric disorders, revealing shared biological mechanisms and therapeutic targets.

This study sequenced 35,024 individuals with predominantly African ancestry to address gaps in genomics and healthcare, finding under-documented disease variants and novel associations with drug adverse events and glycemic control.

This study evaluates how different modeling choices affect the performance of clinical risk prediction models integrating genetic and clinical data, finding that PRS inclusion benefits vary by integration method and data type.

This study uses EHR data to estimate TRD probability and identifies two genome-wide significant loci linked to metabolic traits, suggesting shared biology with related conditions.

This study identifies modifiable comorbidities of schizophrenia by analyzing EHR data and polygenic risk scores, finding conditions with no shared genetic risk that may be influenced by treatment or environment.

The paper identifies novel autism risk genes by analyzing convergent coexpression patterns in brain samples, showing these genes may not be detected by sequencing studies.

The paper identifies population-level variation in enhancer expression in the human brain and links enhancer eQTL to schizophrenia heritability.

This study identifies 114 genome-wide significant rare CNV-disease associations across 30 neurological disorders, revealing 38 novel triplosensitive genes and developing a model to predict dosage sensitivity.

The paper develops a model to identify patients likely to have a genetic disease using EHR data, achieving high accuracy in predicting genetic testing needs.

The paper functionally annotates rare structural variants in the human brain by integrating SV calling with RNA-seq data to assess their regulatory effects and prioritize pathogenic deletions.

DRAMS detects and corrects sample mix-ups in multi-omics data by aligning genetic and omics data, improving statistical power and result quality.