Omer Weissbrod

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data by allowing causal effect sizes to vary across ancestries, achieving higher power and lower computational costs.

The paper introduces LDSPEC to estimate correlations between causal disease effects of proximal SNPs, finding significant correlations that vary with distance, allele frequency, LD, and functional annotations, implicating stabilizing selection.

This paper introduces improved fine-mapping methods that model infinitesimal effects, showing better calibration and increased PRS accuracy for causal variant identification in complex traits.

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.

The paper identifies potent siRNAs against SARS-CoV-2 through RNAi screening, validates their efficacy against variants, and demonstrates their prophylactic potential in a hamster model.

This paper introduces PolyPred and PolyPred+ to improve cross-population polygenic risk score accuracy by addressing LD, MAF, and causal effect size differences using fine-mapping and non-European training data.

The paper constructs a microbiome-phenotype atlas using large cohorts from Israel and the U.S., showing robust associations and high prediction accuracy across continents.

The paper describes a scalable pipeline for constructing SARS-CoV-2 replicons using de-novo synthesis, demonstrating their safety and utility for drug testing.

This paper predicts the utility of polygenic embryo screening for disease based on selection strategies and finds that selecting the lowest PRS embryo can significantly reduce disease risk.

The paper identifies non-coding variants in the IL18RAP 3'UTR that reduce ALS risk by modulating neuro-inflammation through NF-κB signaling in microglia.

The paper identifies 31 host genetic loci influencing gut microbiome composition through GWAS and suggests causal effects in inflammatory diseases.

The paper assesses and improves Mendelian disease-derived pathogenicity scores for common disease using stratified LD score regression and a gradient boosting framework.

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 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 introduces Sci-LMM, a framework for analyzing population-scale family trees, and estimates heritability of longevity and reproductive fitness in a large historical pedigree.

This paper evaluates methods for estimating heritability and genetic correlation in case-control studies, finding that PCGC-s accurately handles non-genetic risk factors and provides unbiased estimates for schizophrenia and bipolar disorder.

The paper creates a population-scale family tree resource using crowd-sourced genealogy data to study genetic architecture and family dispersion.

This study shows environmental factors have a greater impact on human gut microbiota than host genetics, with microbiome data adding significant predictive value to phenotypes.

RL-SKAT improves the accuracy of score tests for heritability and set-based genetic association by calculating exact p-values, avoiding the inflation or deflation of p-values seen in traditional methods.