Jian Zeng

The paper identifies a mid-gestational molecular mechanism linking cortical size and ADHD through genetic and single-cell RNA sequencing analyses.

This study identifies latent regulatory programs from disease-associated variants to reveal biological mechanisms and clinical subtypes in complex diseases.

The paper identifies biases in estimating heritability from ultra-rare variants and shows that accounting for these biases reveals significant heritability estimates for several traits.

This paper improves causal variant identification by genome-wide fine-mapping, showing it outperforms existing methods and identifies new causal variants for complex traits.

The paper identifies sex-specific genetic regulatory effects in chickens using whole-genome sequencing and transcriptomes, revealing sex-dependent gene regulation and structural variants.

This paper introduces SBayesAPP, a Bayesian model that estimates annotation-stratified pleiotropy and co-polygenicity between complex traits using GWAS summary statistics, revealing cell-type-specific genetic correlations.

This study identifies age-varying genetic contributions to BMI using longitudinal data from the ALSPAC cohort, showing distinct genetic profiles in infancy versus later childhood and adolescence.

This study identifies genetic risk factors for panic attacks and disorder through genome-wide meta-analysis and links them to multiple body systems and neuronal functions.

The paper evaluates the contribution of expression, splicing, and protein QTL in blood to SNP-based heritability and polygenic prediction across 27 traits, finding they capture significant portions of genetic variation.

This paper benchmarks 20 methods for mapping trait-cell type associations by integrating GWAS and scRNA-seq data, finding CATCH to be the most robust approach.

This study identifies shared genetic variants and causal relationships between coronary artery disease and stroke using GWAS and Mendelian randomization.

This study identifies shared genetic variants and causal genes between type 2 diabetes and stroke using GWAS data, revealing novel molecular targets.

The paper identifies regions of the human genome under cellular constraint by analyzing chromatin accessibility across cell types, showing their predictive power for functional loci and applications in variant identification and drug discovery.

DINGO improves locus discovery in GWAS of perinatal traits by combining maternal and fetal GWAS data through meta-analysis and novel statistical tests, identifying more genetic loci than separate analyses.

The paper uses GWAS summary statistics to analyze the genetic architecture of 2024 multi-organ AI endophenotypes, revealing genetic correlations and causal relationships across diseases.

This paper introduces SBayesRC, a method that improves polygenic prediction of complex traits by integrating GWAS summary statistics with functional genomic annotations, showing significant accuracy gains across ancestries.

The paper integrates genomic data with single-cell transcriptomics to identify brain cell types and regions associated with psychiatric disorders and traits.

This paper develops DeepGCF, a deep learning method to predict functional conservation between human and pig using epigenome and transcriptome data, showing improved performance and insights into regulatory and heritability patterns.

The paper uses evolutionary constraint scores to identify functional genomic regions and finds they are enriched for variants associated with common diseases.

This study identifies 14 traits with potential causal genetic effects on stroke risk using GWAS and causal inference methods, highlighting shared genetic architectures and biological pathways.

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

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

The paper introduces NN-LMM, a method that extends linear mixed models to multi-layer neural networks for genomic prediction using intermediate omics data, showing improved performance over existing approaches.

The paper compares ten polygenic score methods for psychiatric disorders across multiple cohorts, finding that some methods significantly improve prediction statistics over the baseline PC+T method.

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 predicts promoter-anchored chromatin interactions using DNA methylation QTL data from a human cohort, finding overlaps with experimental data and associations with complex traits.

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

This paper introduces SBayesR, a Bayesian method for polygenic prediction using GWAS summary statistics, achieving higher accuracy and efficiency than existing methods.