Buhm Han

The paper compares GWAS methods in admixed populations, showing standard tests are more powerful due to allele frequency heterogeneity and identifying distinct LD patterns in admixed vs. single-continental segments of the same ancestry.

This paper identifies that pseudobulk and cell-wise methods for differential gene expression analysis lead to inflated errors in multi-subject single-cell studies and recommends method selection based on study design.

This paper provides a statistical genetics guide for imputing HLA alleles from genotype data and offers best practices for association testing to identify HLA variants driving complex diseases.

MarcoPolo identifies differentially expressed genes without clustering, improving reliability by directly learning group information from gene expression bimodality.

PLEIO maps and interprets pleiotropic loci by jointly analyzing summary statistics of multiple traits, identifying 20 novel loci related to cardiovascular disease.

scIntegral improves cell-type identification in scRNA-seq data by reducing error rates and enabling efficient integration of multiple donors with batch effects.

The paper identifies a genetic subgroup within depression linked to age of natural menopause using UK Biobank data, but finds no replication in other cohorts.

This paper identifies shared genetic risk factors between schizophrenia and 19 immune diseases, including three variants with pleiotropic effects and significant genetic correlations for several immune diseases.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.

This paper reviews how the multivariate normal distribution framework can be used to analyze GWAS by accounting for variant correlations, performing association testing, and enabling fine mapping and imputation.

The paper introduces a meta-analysis method to identify eQTLs from multi-tissue datasets, detecting more eQTLs and eGenes than previous approaches.

The paper introduces BUHMBOX, a statistical method to distinguish pleiotropy from heterogeneity in genetic data, finding shared genetic bases in autoimmune diseases and between MDD and schizophrenia.

The paper develops a statistical method to distinguish causal non-coding variants from colocalizing annotations in complex trait loci, identifying independent effects in disease-associated regions.