Luke Jen O’Connor

The paper introduces KASM to integrate functional annotations and estimate heritability of rare protein coding variants across 786,871 exomes, finding higher heritability than previous estimates.

ALLSPICE identifies heterogeneous rare variant effects across traits in gene-trait associations using summary statistics from the UK Biobank.

The paper defines pangenome variants and introduces pantree to identify them, finding 29.6 million variants in the Minigraph-Cactus human pangenome graph.

This paper introduces graphREML, a likelihood-based method for heritability partitioning and enrichment analysis using GWAS summary statistics, showing greater power than S-LDSC.

The paper defines polygenicity, proposes measures, and estimates them for 36 human traits using GWAS data, finding few traits fall between Mendelian and highly polygenic categories.

The paper introduces a lossless compression method for genotype data using genealogical relatedness, achieving significant size reduction and speedup in genetic analyses.

The paper develops a pipeline to compare polygenic scores (PGS) for detecting genetic interactions between adiposity and cardiometabolic risk factors, finding that interaction-based PGS (iPGS) most consistently identifies these interactions.

The paper introduces TRADE, a statistical framework to detect transcriptome-wide differential expression effects from noisy Perturb-seq data, showing that many true effects are not statistically significant but detectable in aggregate.

This study identifies genetic variants associated with cellular morphology using high-content imaging and genomic data from iPSCs.

HAPNEST generates and evaluates large-scale synthetic genotype and phenotype datasets for polygenic risk score analysis.

The paper introduces LD graphical models (LDGMs) for efficient linkage disequilibrium representation in diverse ancestry studies, showing significant runtime improvements and better polygenic prediction.

The paper quantifies the heritability explained by rare coding variants and compares it to common variants across 22 traits using 400,000 UK Biobank exomes, finding rare variants explain less phenotypic variance but are more concentrated in constrained genes.

The paper develops a combined SNP-to-gene linking strategy (cS2G) to improve the identification of disease-causing genes and assess disease omnigenicity using GWAS data.

This paper introduces PDR to identify shared heritability components across genetically correlated traits and validates it on coronary disease, asthma, and type II diabetes traits.

The paper estimates the fraction of disease heritability mediated by genes linked to SNPs without using eQTLs, finding that the closest gene mediates 27% of heritability and some genes outside the top ten also contribute significantly.

This paper estimates the distribution of common-variant effect sizes using GWAS summary statistics and finds that large sample sizes are needed to explain most heritability.

The paper shows that S-LDSC's significance testing fails for small annotations, leading to inflated type 1 error, and proposes thresholds to control this error.

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 introduces MESC to estimate disease heritability mediated by gene expression, finding that only a small proportion of heritability is explained by cis-genetic expression levels.

The paper analyzes 41 diseases and traits to show transposable elements contribute uniquely to disease heritability, with older elements and SINEs showing specific enrichment patterns.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.

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 the latent causal variable model to distinguish genetic correlation from causation across 52 diseases and traits, identifying 59 pairs with partial or full genetic causality.

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

The paper introduces a method to detect directional effects of transcription factor binding on disease risk using GWAS data and applies it to identify new causal genes and mechanisms.

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 develops GECS regression to estimate how much disease heritability is mediated by gene expression levels, finding that 21% of heritability is mediated across 30 traits.