Zhili Zheng

This paper constructs phenome-derived polygenic scores and evaluates how they, along with social determinants of health, influence disease risk across different populations and 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 uses population-scale single-nucleus ATAC-seq and RNA-seq to identify regulatory variants and their disease associations in immune cells.

The paper identifies CFHR5 variants that reduce AMD risk by lowering FHR-5 levels and altering complement pathway activity.

The paper estimates heritability for 2,321 plasma proteins using twin data and finds that SNP-based heritability accounts for only half of the total genetic effects.

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.

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.

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.

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.

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

The paper estimates heritability for height and BMI using whole-genome sequence data and finds that rare variants contribute significantly to the missing heritability.

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

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.

This paper identifies 58 significant associations between grey matter structure and diverse human phenotypes using large neuroimaging datasets and shows body size factors explain part of these 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.

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

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

This study performs GWAS on medication-use data from 320,000 UK Biobank participants, identifying 505 genetic loci and linking them to 16 known therapeutic targets.

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

This paper performs a meta-analysis of GWAS for height and BMI in 700,000 individuals, identifying 3,290 and 716 SNPs, with increased prediction accuracy.

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 finds a genetic association between schizophrenia risk and urban living, suggesting genetic factors influence migration to densely populated areas.

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.