Zhihong Zhu

This study examines the genetic and phenotypic links between neonatal C3 and C4 protein concentrations and psychiatric and autoimmune disorders, finding no strong causal associations with mental disorders but evidence of lower autoimmune risk with higher C4 levels.

This study identifies genetic loci associated with vitamin D-binding protein and 25 hydroxyvitamin D in neonates and links DBP to vitamin D status and disease risk.

This paper introduces a framework to enhance polygenic prediction by combining multiple polygenic scores, showing significant accuracy improvements for psychiatric disorders and other traits.

This paper introduces DENTIST, a method to reduce errors and heterogeneity in GWAS summary data, improving analyses like COJO and fine-mapping.

This study investigates genetic correlations and causal relationships between multiple sclerosis and inflammatory bowel diseases, identifying shared SNPs and tissue-specific heritability patterns.

This paper uses Mendelian randomisation to examine the causal effect of antihypertensive drugs on psychiatric disorders, finding an increased schizophrenia risk with lower ACE gene expression.

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

This study identifies 143 genetic loci associated with 25 hydroxyvitamin D concentration in Europeans, revealing biological pathways and causal relationships with health outcomes.

OSCA provides a tool for omic-data-based complex trait analysis using the MOMENT method, showing improved robustness and lower false positive rates in simulations.

This study uses conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders, finding some variants with reduced pleiotropy effects.

This paper uses Mendelian Randomisation to show that height reduces coronary artery disease risk independently of BMI, lipid, and blood pressure, but has no direct effect on type 2 diabetes risk after adjusting for BMI.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

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.

This study uses genetic data to identify causal relationships between risk factors and common diseases, finding protective and bidirectional effects.