Katarzyna Bryc

This paper introduces a Bayesian framework for inferring population structure using IBD-based stochastic block models, showing improved accuracy over heuristic methods in both simulated and real data.

This study compares polygenic risk scores for Parkinson's disease across seven ancestries, finding varying predictive accuracy and highlighting the need for ancestry-specific models.

This study identifies genetic correlations between hyposmia and Parkinson's disease, finding a strong causal link from PD to hyposmia but weaker evidence for the reverse.

This study investigates the shared genetic architecture between rhythm and dyslexia using large-scale genome-wide association studies.

MUSSEL improves polygenic risk prediction across populations by integrating GWAS summary statistics and using Bayesian hierarchical modeling with ensemble learning.

This paper conducts a multi-ancestry genome-wide meta-analysis of Parkinson's disease, identifying 78 independent loci and 23 genes associated with PD risk.

The paper introduces CT-SLEB, a method for multi-ancestry polygenic prediction that improves performance in non-European populations using GWAS summary statistics and simulation studies.

This study identifies 49 genetic loci associated with sleep apnoea risk using multi-trait GWAS, with 29 replicated in a large cohort, and finds genetic correlations with several complex traits.

This study conducts a genome-wide association study to identify genetic variants linked to musical beat synchronization, finding high polygenicity with 69 significant loci and heritability estimates of 13%-16%.

The paper constructs polygenic indexes for 47 phenotypes across 11 datasets and provides a tool to correct errors-in-variables bias when using PGIs as proxies for the additive SNP factor.

The paper identifies sex-differential participation bias in genetic studies by performing GWAS on sex in 3.3 million individuals, finding 158 loci associated with sex and highlighting traits like BMI that may influence participation.

This study identifies genetic loci associated with daytime napping and finds potential causal links between napping and cardiometabolic traits.

This paper introduces TPBWT for fast and robust IBD inference, showing improved speed and accuracy over existing methods, especially in handling phasing errors.

The paper identifies HLA-B*55:01 as a genetic risk factor for penicillin allergy through genome-wide analysis of electronic health records and imputed HLA data.

This study identifies one known and two novel genetic variants associated with heart failure through GWAS and multi-omic analyses, revealing potential causal mechanisms and shared heritability with musculoskeletal traits.

The paper characterizes the prevalence and health consequences of uniparental disomy in four million individuals, identifying 675 cases and linking UPD of chromosome 22 to autism.

This study identifies two genetic loci, GDF5 and COL27A1, associated with knee pain through a genome-wide association analysis in the UK Biobank and replication in other cohorts.

This study conducts GWAS on impulsive personality traits and drug experimentation in 22,861 adults, identifying genetic associations and correlations with psychiatric disorders.

This study shows common genetic variants significantly contribute to migraine family clustering, with higher burden in familial cases compared to population-based cases.

This study conducts a GWAS on depression phenotypes in UK Biobank, identifying genetic variants enriched in excitatory synaptic pathways.

This study investigates genetic mechanisms underlying sex bias in ADHD prevalence using genome-wide data and finds no mean polygenic burden difference across sexes but increased risk in female siblings and comorbid conditions.

This study replicates and extends findings of CADM2 and MSRA gene associations with personality traits using data from 23andMe.

This study analyzes the genetic ancestry of African, Latino, and European Americans in the US, revealing mixed ancestry and historical influences on regional genetic differences.