James Liley

The paper introduces an R package called fcfdr that extends the cFDR framework to use binary functional genomic data in GWAS, improving power while controlling FDR, and applies it to type 1 diabetes.

This paper introduces a new method for controlling type-1 error rates in high-dimensional association testing using conditional false discovery rates, showing improved power over existing methods.

The paper identifies novel BMI genotype-environment interactions using a conditional false discovery rate approach on UK Biobank data, finding 140 candidate loci with GxE effects.

This study identifies genetic subsets of EGPA associated with asthma and eosinophil levels, revealing two distinct clinical and genetic syndromes.

The paper introduces new estimators for conditional false discovery rates (cFDR) and demonstrates their impact on power in high-dimensional association studies.

The paper introduces a statistical method to detect genetic heterogeneity in disease subgroups defined by phenotype, showing evidence of differential genetic architecture in type 1 diabetes subgroups.

This paper introduces a pleiotropy-informed Bayesian false discovery rate method for GWAS summary statistics, identifying 59 new SNP-disease associations across ten autoimmune diseases.