Sebastian K. Zöllner

The paper introduces the expected polygenic risk score (ePRS) framework to address population stratification in polygenic risk score analysis by modeling ancestral makeup, showing it provides equitable risk quantification across diverse populations.

This paper introduces a coalescent-based framework to unify population structure and relatedness analysis, showing that full GRMs can replace PCs for modeling genetic covariance.

FICTURE provides a scalable segmentation-free method for analyzing high-resolution spatial transcriptomics data, revealing detailed tissue architecture in complex regions.

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.

This study identifies 31 plasma proteins and expands genetic loci associated with PCOS, highlighting metabolic and hormonal influences and polygenic risk effects on cardio-metabolic outcomes.

The Michigan Genomics Initiative links genotypes with electronic clinical records in Michigan Medicine patients, showing higher disease prevalence and validating GWAS findings.

The paper genotypes and sequences 140,000 adults from the Mexico City Prospective Study, revealing ancestry diversity and developing imputation resources for genetic studies.

GAUSS identifies gene-set-phenotype associations using GWAS summary statistics, improving power and interpretability in UK Biobank data.

The paper develops a coalescent model to analyze theoretical limits of rare variant imputation, showing that model misspecification leads to significant imputation errors.

The paper presents a method combining gene weights from mathematical biology models and gene ranks from GWAS to improve gene-set enrichment analysis for bipolar disorder.

The paper analyzes de novo mutations in 1,465 genomes to show similar mutation rates across ancestries but reduced rates in the Amish population, with environmental factors possibly influencing mutation rates.

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.

The paper identifies loss-of-function variants affecting liver-related blood traits that reduce CVD risk without increasing metabolic disease risk, highlighting potential therapeutic targets.

The paper analyzes rare variants to uncover mutation rate heterogeneity in the human genome, identifying genomic features that influence mutation rates.

Helmsman generates mutation spectra matrices efficiently, enabling mutation signature analysis on large datasets with high speed and low memory usage.