Armin P. Schoech

The paper develops a method to identify population-specific causal effect sizes in functionally important regions using trans-ethnic genetic correlations.

This paper introduces PolyFun, a method that improves fine-mapping accuracy by integrating genome-wide functional data, leading to more causal variant identifications and polygenic localization of trait heritability.

The paper develops ACLR to estimate genomic autocorrelation of causal SNP effects and finds significant negative autocorrelation at short distances in complex traits.

The paper quantifies frequency-dependent genetic architectures and negative selection effects across 25 UK Biobank traits using a statistical model and imputed genotypes.

This paper introduces a method to enhance GWAS power by incorporating functional annotations, resulting in increased detection of genome-wide significant loci.

The paper partitions low-frequency and common variant heritability across functional annotations, finding non-synonymous coding variants and conserved regions explain significant portions of low-frequency heritability.

The paper shows that polygenicity of complex traits is explained by negative selection, which flattens genetic signal across the genome.

The paper quantifies genetic components of population differentiation in UK Biobank traits to detect polygenic selection signals across geographic gradients.

The paper uses molecular QTL annotations to identify genetic variants associated with disease heritability, finding stronger enrichments using causal posterior probabilities than standard annotations.

The paper introduces a faster version of BOLT-LMM for GWAS on large biobank datasets, showing increased power and detecting more loci for height and other traits.

The paper quantifies linkage disequilibrium (LD) dependent architecture of complex traits, finding that low LD SNPs have higher heritability, likely due to negative selection.