Andrew J. Bass

This study evaluates genomic foundation models' ability to improve common variant discovery in GWAS and finds they do not outperform standard functional annotations.

This paper introduces KDCA, a framework for differential co-expression analysis of general risk factors, showing improved power and reduced bias compared to existing methods.

This paper introduces sfFDR to enhance variant discovery by integrating related trait summary statistics, increasing power and discovering additional SNPs in GWAS.

SCAMPI detects SNP interactions across multiple traits by analyzing trait correlation patterns, improving power over univariate methods in lipid-related traits from UK Biobank data.

This paper introduces LIT, a method to detect latent genetic interactions using multiple traits, showing improved power compared to univariate methods in obesity-related traits.

The paper extends the optimal discovery procedure (ODP) to handle complex study designs and RNA sequencing, showing improved performance in detecting differentially expressed genes.

The paper introduces superSeq, a method to determine optimal sequencing depth for RNA-Seq studies, finding most studies are undersequenced and saturation depends on statistical methods.