Systematic dissection of pleiotropic loci and critical regulons in excitatory neurons and microglia relevant to neuropsychiatric and ocular diseases.

Advancements in single-cell multimodal techniques have greatly enhanced our understanding of disease-relevant loci identified through genome-wide association studies (GWASs). To investigate the biological connections between the eye and brain, we integrated bulk and single-cell multiomic profiles with GWAS summary statistics for eight neuropsychiatric and five ocular diseases. Our analysis uncovered five latent factors explaining 61.

Mendelian randomization analysis reveals a causal effect of Streptococcus salivarius on diabetic retinopathy through regulating host fasting glucose.

Diabetic retinopathy (DR) is one of leading causes of vision loss in adults with increasing prevalence worldwide. Increasing evidence has emphasized the importance of gut microbiome in the aetiology and development of DR. However, the causal relationship between gut microbes and DR remains largely unknown.

Integration of human organoids single-cell transcriptomic profiles and human genetics repurposes critical cell type-specific drug targets for severe COVID-19.

Human organoids recapitulate the cell type diversity and function of their primary organs holding tremendous potentials for basic and translational research. Advances in single-cell RNA sequencing (scRNA-seq) technology and genome-wide association study (GWAS) have accelerated the biological and therapeutic interpretation of trait-relevant cell types or states. Here, we constructed a computational framework to integrate atlas-level organoid scRNA-seq data, GWAS summary statistics, expression quantitative trait loci, and gene-drug interaction data for distinguishing critical cell populations and drug targets relevant to coronavirus disease 2019 (COVID-19) severity.

Polygenic regression uncovers trait-relevant cellular contexts through pathway activation transformation of single-cell RNA sequencing data.

Advances in single-cell RNA sequencing (scRNA-seq) techniques have accelerated functional interpretation of disease-associated variants discovered from genome-wide association studies (GWASs). However, identification of trait-relevant cell populations is often impeded by inherent technical noise and high sparsity in scRNA-seq data. Here, we developed scPagwas, a computational approach that uncovers trait-relevant cellular context by integrating pathway activation transformation of scRNA-seq data and GWAS summary statistics.