Longda Jiang

The paper introduces scSLIDE, a framework that generates sample-level embeddings from single-cell data to analyze variation across samples in disease and development.

The paper uses Perturb-seq to systematically identify signaling regulator targets across multiple cell lines and contexts, introducing Mixscale to improve analysis and infer pathway activation changes.

The paper develops a meta-analysis method to identify reproducible differentially expressed genes across single-cell transcriptomic case-control studies and finds shared genes across neurodegenerative diseases.

This study identifies low-frequency variants in FKBP5 and FOLH1 associated with high myopia in Han Chinese and European populations through whole-exome sequencing and functional experiments.

mBAT-combo improves gene-trait association detection in GWAS by accounting for SNP effect directions, revealing masking effects in complex traits.

This study performs a GWAS on random glucose measurements in 493,036 individuals, identifying 128 loci associated with glycaemic traits and providing insights into diabetes biology and treatment.

The paper quantifies genetic heterogeneity between European and African populations for height and BMI, finding high shared genetic variation suggesting GWAS findings in Europeans are applicable to non-Europeans.

The paper corrects biases in self-reported behavioural traits in UK Biobank data by addressing misreports and longitudinal changes, revealing previously hidden genetic associations.

scSplit demultiplexes pooled single-cell RNA-Seq samples using genetic differences inferred from the data, showing high accuracy and consistency with known truths.

fastGWA is a resource-efficient mixed model tool for GWAS that addresses population stratification and relatedness in large biobank datasets.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.