Lars G. Fritsche

This study identifies 63 AMD risk loci across multiple ancestries, highlighting ancestry-dependent genetic heterogeneity and potential therapeutic targets.

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.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

This paper shows that meta-analysis fine-mapping is often miscalibrated due to cohort heterogeneity and introduces a QC method to identify suspicious loci.

This paper introduces TL-PRS, a transfer learning method for constructing polygenic risk scores across ancestries, achieving up to 42% average relative improvement in South Asian and African ancestry groups.

Meta-analysis of harmonized GWAS from up to 18 biobanks (six ancestry groups) across 14 endpoints replicated 320 known loci and identified 188 apparently novel loci.

The paper develops ExPRSweb, an online repository of polygenic risk scores for common exposures, showing improved prediction of chronic conditions when combined with traditional PRSs.

This study evaluates the utility of breast and prostate cancer polygenic risk scores across different ancestry groups using UK Biobank data, showing significant enrichments in cancer cases within top PRS percentiles.

The paper evaluates family history and polygenic scores as predictors for coronary artery disease and type 2 diabetes using the Trondelag Health Study and UK Biobank.

This study performs a genome-wide association analysis of hemorrhoidal disease in 944,133 individuals, identifying 102 risk loci and linking them to vascular and tissue development pathways.

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

This study identifies seven novel genetic loci for Alzheimer's disease using a large GWAS and highlights microglia and immune pathways as key contributors to disease pathology.

This study conducts a meta-analysis of EHR-based lab measurements across two biobanks, identifying novel genetic associations and validating EHR data as a source for GWAS.

The paper introduces Cancer-PRSweb, an online repository of polygenic risk scores for cancer traits, evaluated in two biobanks with predictive performance metrics and phenome-wide association studies.

SAIGE-GENE is a scalable generalized linear mixed model for region-based association tests in large cohorts, efficiently handling over 400,000 samples with controlled type I error rates.

The paper develops a region-based rare variant test for unbalanced binary phenotypes, showing improved type I error control and scalability, and identifies 10 rare variant associations in UK Biobank whole exome data.

Meta-MultiSKAT improves power for region-based genetic association tests by meta-analyzing multiple correlated phenotypes using summary statistics and variance component tests.

This study constructs and evaluates polygenic risk scores for skin cancer subtypes using the Michigan Genomics Initiative and UK Biobank data, and develops a visual catalog to compare PRS methods.

This study identifies 147 genetic loci associated with eGFR, including 53 novel ones, and finds sex-specific effects on kidney function.

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.

This paper introduces SAIGE, a method that improves the accuracy of genetic association tests in unbalanced case-control studies by using saddlepoint approximation, and demonstrates its effectiveness on UK Biobank data.

This study identifies 111 genetic loci and 163 risk variants associated with atrial fibrillation using a GWAS of over 1 million individuals.

This study uses polygenic risk scores (PRS) for cancers in a phenome-wide association study (PheWAS) to identify associations with multiple phenotypes, finding significant links to both cancer and non-cancer diagnoses.

This study identifies two new genetic loci associated with aortic valve stenosis through a large genome-wide association study.

This paper introduces a Bayesian method that integrates functional annotations into GWAS to identify causal variants and improve biological interpretation.