Wei Cheng

This paper introduces a Bayesian neural network approach for variable selection in genetic fine-mapping, showing improved identification of true effect variables 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 identifies 1,000 gene-level associations and conserved pathway associations for 25 traits across seven ancestries using enrichment analyses on large-scale GWA data.

This study identifies sex-dependent genetic risk loci shared and specific to mood and psychotic disorders using genome-wide interaction analysis.

This paper introduces BANNs, a probabilistic framework for GWA studies that improves association mapping and enrichment analysis by incorporating biological annotations and variational inference.

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 introduces gene-ε, a method to detect enriched genes underlying complex traits by improving SNP effect size estimation and reducing false positives in genome-wide association studies.

The paper finds a genetic association between schizophrenia risk and age at first birth in women, with different genetic correlations in younger and older age groups.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

This study compares LDSC and GREML methods for estimating genetic correlation, showing GREML provides higher accuracy, especially in the presence of genetic heterogeneity.