Bogdan Paşaniuc

The paper uses admixture mapping in the All of Us Research Program to identify genetic ancestry associations with 22 traits, finding 71 novel loci where African ancestry explains phenotypic variation.

FELIX improves genetic association analysis by accounting for local ancestry in admixed populations, increasing significant loci and improving polygenic score prediction.

This study identifies genetic and transcriptomic factors associated with severe coccidioidomycosis, finding a founder variant in NLRX1 linked to African ancestry and increased disease risk.

The paper maps gene expression and chromatin accessibility QTLs in fibroblasts from European and Latin American ancestries, finding high concordance and shared regulatory mechanisms.

This study evaluates the GIRA approach in a health system, finding 30.4% of patients labeled high-risk based on genomic data, with ancestry differences and social deprivation influences.

CalPred provides well-calibrated prediction intervals for polygenic risk scores, maintaining calibration across different contextual factors unlike other methods.

The paper analyzes genetic and health record data from a diverse population to uncover gene-phenotype associations and assess polygenic score performance across ancestries.

This study shows that inclusion biases in biobanks affect genetic analysis results and suggests using inverse probability weighting to improve variant discovery and replication.

The paper shows that statistical uncertainty explains disagreement in type 2 diabetes polygenic scores and proposes using uncertainty measures to improve risk prediction.

This study identifies genetic loci associated with Meniere's disease and links them to inner ear development and retinoic acid metabolism.

This study creates a 3D multi-omic atlas of developing human basal ganglia and inhibitory neurons, revealing lineage-specific 3D genomic dynamics and regulatory programs associated with neuropsychiatric disease risk.

This paper improves genetic discovery and polygenic risk prediction for respiratory diseases by analyzing multiple traits and ancestries, identifying novel loci and developing a new polygenic risk score method.

This study examines common and rare variant genetic contributions to autism in African Americans, highlighting differences in genetic effects and polygenic score transferability between European and African ancestries.

This paper examines how genetic interactions with ancestry affect polygenic scores and shows that models of local and global ancestry interactions can be distinguished using partial polygenic scores when causal variants are known.

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

This study identifies genetic risk mechanisms for six cancers by analyzing isoform-level expression data, finding more associations and greater heritability explained compared to gene-level analysis.

The paper compares GWAS methods in admixed populations, showing standard tests are more powerful due to allele frequency heterogeneity and identifying distinct LD patterns in admixed vs. single-continental segments of the same ancestry.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

This study calibrates polygenic risk scores (PRS) for African American adults based on genetic ancestry, improving risk stratification and effect estimation for type 2 diabetes and height.

This study compares polygenic scores from genotyping arrays and whole-genome sequencing across traits and populations, finding that WGS generally improves prediction accuracy but with context-dependent advantages.

The paper integrates polygenic risk scores with single-cell methylation data to identify methylation-driven disease relevance in depression and other traits.

PennPRS provides a cloud platform for training polygenic risk scores without individual-level data, validated with simulations and real data across 6,000 phenotypes.

This study evaluates the reliability of coronary artery disease polygenic risk scores across diverse populations, finding poor individual agreement despite equivalent population-level performance.

This study investigates genetic factors influencing weight loss from GLP1-RA and bariatric surgery in 10,960 individuals across 9 biobanks, finding no strong genetic associations with GLP1-RA effectiveness but a modest link between BMI polygenic scores and reduced bariatric surgery weight loss.

The paper improves polygenic prediction accuracy by using diverse datasets, showing greater benefits for underrepresented populations.

The paper introduces context-aware prediction intervals for polygenic scores to improve calibration across diverse populations and traits.

This study develops multi-ancestry polygenic risk scores for venous thromboembolism using diverse GWAS data and shows improved predictive performance across European- and African-ancestry populations.

The paper identifies extensive isoform diversity in the developing human neocortex and links it to neuropsychiatric disorder risk mechanisms.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This paper evaluates a European-derived polygenic score for tobacco use disorder in a diverse biobank, finding inconsistent performance across ancestries and associations with systemic health risks.