Bjarni Jóhann Vilhjálmsson

This paper introduces a summary-statistics-based method to detect and quantify ascertainment bias in genetic studies using polygenic scores across multiple biobanks.

LT-FGRS estimates genetic liabilities from large-scale family data, unifying methods into an R package for efficient population-scale analysis.

The paper shows genotype- and phenotype-based genetic instruments explain largely independent components of liability to psychiatric disorders, supported by theoretical analysis and simulations.

This study shows that as ASD and ADHD diagnoses have increased, the genetic risk associated with these conditions has decreased over time.

This study assesses the progress and limitations of polygenic risk score prediction accuracy for common diseases, showing diminishing returns from recent GWAS and highlighting the need for broader variant coverage.

The paper examines diagnostic transitions and remission in eating disorders using polygenic scores in a Danish cohort, finding significant genetic associations with these outcomes.

Family-based GWAS meta-analysis of 34 phenotypes shows standard GWAS effects carry sizeable confounding uncorrelated with direct genetic effects, while assortative mating likely inflates SNP heritability and genetic correlations.

The paper identifies non-linear genetic regulation of plasma proteins using a deep learning method, finding dominance and epistasis effects in large cohorts.

This study examines the genetic and phenotypic links between neonatal C3 and C4 protein concentrations and psychiatric and autoimmune disorders, finding no strong causal associations with mental disorders but evidence of lower autoimmune risk with higher C4 levels.

This paper validates LDpred2-auto for inferring genetic parameters and shows improved heritability capture and predictive performance with an expanded variant set.

This study investigates whether birth weight has a direct causal effect on psychiatric disorders later in life using Mendelian randomization, finding no direct causal relationship.

This study identifies 40 genetic loci associated with placental weight using GWAS in 179,025 children and parents, revealing shared and distinct genetic influences with fetal growth.

ADuLT is a time-to-event GWAS method that outperforms Cox regression in strongly ascertained studies, finding more genome-wide significant associations in psychiatric disorder analyses.

This study identifies 64 new risk loci for major depression, refines its genetic architecture, and shows strong genetic overlap with other psychiatric disorders and educational attainment.

The paper introduces PA-FGRS, a method to estimate genetic liability from family records, and shows it improves MDD classification and GWAS power when combined with genotypes.

The paper introduces a deep learning framework for polygenic risk score prediction, showing improved performance over traditional methods for various diseases.

This study identifies genetic loci associated with vitamin D-binding protein and 25 hydroxyvitamin D in neonates and links DBP to vitamin D status and disease risk.

This study uses deep learning to predict blood and urine biomarkers from genetic data, outperforming linear methods and capturing non-linear and epistatic effects.

This paper presents a publicly available privacy-preserving benchmark for polygenic prediction using summary-level data, enabling accurate model performance estimation without access to individual-level genetic data.

The paper evaluates polygenic score accuracy across genetic ancestry continua in diverse populations, finding significant variation in performance linked to genetic distance from training data.

This paper introduces a framework to enhance polygenic prediction by combining multiple polygenic scores, showing significant accuracy improvements for psychiatric disorders and other traits.

The paper identifies misspecifications in GWAS summary statistics and shows how to correct them to improve polygenic score predictive performance.

This paper introduces LT-FH++, a method that improves GWAS power by incorporating age-of-onset and family history, showing increased significant associations in psychiatric disorders and mortality data.

This study identifies seven new genetic loci associated with febrile seizures, linking them to fever response and neuronal excitability genes, and suggests potential clinical applications of polygenic risk scores.

This paper develops and compares genetic prediction tools that incorporate heritability models, showing improved accuracy for complex traits using individual-level and summary statistics data.

This study compares methods for combining GWAS summary statistics and individual-level data to improve polygenic prediction, finding that Meta-PRS is often more accurate than Meta-GWAS.

LDpred2 reworks LDpred with a 3 cM LD window plus sparse and auto options, reaching mean AUC 65.1% over 8 real traits versus 63.8% for lassosum.

The paper shows that individual PRS estimates have large uncertainty, impacting risk stratification, and introduces a method to quantify this uncertainty using Bayesian approaches.

This paper benchmarks the performance of two R packages, snpnet and bigstatsr, for fitting penalized regressions on large genetic data, finding bigstatsr significantly faster.

The paper identifies differentially expressed circRNAs in AD brain regions and links their expression patterns to disease severity and pathways.