Lang Wu

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

The paper introduces nanoASM, a method for allele-specific methylation analysis using long-read sequencing, identifying functional noncoding variants in prostate cancer.

The paper introduces a model-free permutation method to estimate P-values in genetic studies, reducing computational demands while maintaining accuracy.

The paper develops a cross-ancestry information transfer framework to improve protein abundance prediction and protein-trait association identification in underrepresented populations.

This paper uses nanopore long-read sequencing to characterize native CpG methylation and identify allele-specific methylation regions in the prostate genome.

The paper analyzes circulating metabolites using genome-wide association data from the TOPMed program, identifying 1,778 loci and highlighting sex-specific genetic contributions.

This paper introduces TReD, a framework integrating population-level disease signatures and cell-based drug responses to identify potential therapeutics for COVID-19 and T2D.

The paper introduces AE-TWAS, a method that improves genotype-phenotype association studies by transforming transcriptome data with autoencoders to enhance expression heritability and TWAS performance.

This study uses multiome single-cell sequencing to identify regulatory eQTLs in prostate cancer risk loci, finding overlaps with bulk eQTLs and validating new regulatory SNPs.

The paper introduces SuShiE, a model that improves multi-ancestry fine-mapping of cis-molQTLs, identifying more genes and prioritizing functionally enriched variants.

The paper identifies a regulatory SNP rs60464856 that increases RUVBL1 expression and contributes to prostate cancer progression through cohesin-CTCF binding and cell-cycle activation.

MIMOSA improves DNA methylation prediction and MWAS power by integrating summary-level mQTL data, identifying more CpG-phenotype associations in whole blood.

SUMMIT improves transcriptomic data imputation and TWAS power by using large eQTL summary datasets, identifying 11 causal genes for COVID-19 severity.

This paper introduces a gene-level methylome-wide association analysis method called CMO that identifies novel Alzheimer's disease genes by integrating DNA methylation and enhancer-promoter interactions with GWAS data.