Xianyong Yin

MetaboXcan predicts plasma metabolites from genetic data and associates them with complex traits, identifying disease risk genes and metabolites in chronic kidney disease.

This study examines how genetic and lifestyle factors together influence the risk of developing type 2 diabetes in the UK Biobank population.

This study uses Mendelian randomization to show that higher glycemic traits are inversely associated with thoracic aortic diameters and reduced risk of aneurysm and dissection.

This study investigates causal links between T2D genetic clusters and non-cardiovascular comorbidities, identifying potential drug repurposing opportunities.

This study identifies independent genetic contributions of HLA missense variants and C4 copy number variations to SLE risk using a new East Asian MHC imputation panel.

This paper introduces INTERFACE, a probabilistic fine-mapping method that improves the identification of causal genes by integrating colocalization and transcriptome-wide association evidence.

This study integrates multi-omics data to identify genetic variants and molecular mechanisms associated with type 2 diabetes and highlights potential drug targets.

The paper introduces Multi-INTACT, a method that integrates transcriptome-wide association studies with protein QTL data to identify causal genes for metabolites, finding that both gene products are relevant for most gene nominations.

This study performs a multi-ancestry GWAS for adiponectin, identifies 22 loci, and prioritizes target genes using GPScore, revealing novel genes and pathways involved in metabolic regulation.

This study identifies 1,289 genetic signals for type 2 diabetes in 2.5 million individuals, revealing distinct clusters linked to vascular complications and highlighting ancestry-specific genetic risk factors.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This study identifies 118 candidate causal genes and 21 novel lipid loci on the X chromosome through a multi-ancestry meta-analysis of blood lipid levels, revealing sex-biased effects and pleiotropy with cardiovascular and hormonal traits.

This study identifies 400+ genetic loci associated with circulating metabolic traits and highlights pleiotropy and novel disease pathways.

This paper integrates GWAS data with functional genomic datasets to identify regulatory mechanisms and causal genes underlying lipid-related traits.

This paper introduces a probabilistic framework to integrate TWAS and colocalization analysis, improving gene implication and pathway identification for complex traits.

This study identifies 303 novel genetic signals associated with plasma metabolites in Finnish men, revealing biological insights into disease mechanisms.

This study identifies cell-type-specific transcriptomic signatures associated with SLE disease establishment and exacerbation, highlighting their clinical relevance and potential limitations in capturing genetic risk factors.

This study identifies 12,111 SNPs associated with height from 5.4 million individuals, explaining most common SNP-based heritability and showing ancestry-specific prediction accuracy.

This study identifies 38 novel SLE loci and shows genetic heterogeneity across ancestral groups, highlighting antibody production as a potential mechanism for disease disparities.

This study identifies 113 genomic loci for SLE in East Asians, including 46 novel loci, and highlights non-immune cell-related biological mechanisms.

The paper identifies rare non-coding variants from whole genome sequencing in the TOPMed program, showing they contribute significantly to type 2 diabetes heritability.

Exome sequencing in Finnish populations identifies rare trait-associated alleles with higher frequencies and geographical clustering, revealing 30 novel associations with quantitative traits.