Krista Fischer

This study performs a large-scale GWAS on 249 metabolic traits in 619,372 individuals, identifying 88,604 associations and using MR to explore causal links between metabolites and diseases.

This study uses genomic structural equation modeling to identify latent genetic factors underlying T2D and its comorbidities, showing distinct risk profiles and broader associations with complications.

This paper reviews methodological challenges in GWAS of biobank data with time-to-event outcomes, proposing a two-step martingale residual approach for efficient analysis.

The paper reviews the Estonian Biobank's role in advancing genomics research and personalized medicine through its comprehensive data and translational focus.

The paper identifies 16 genomic loci associated with IgG galactosylation through GWAS and validates three genes using CRISPR/dCas9 in an in vitro system.

The paper develops metabolomic risk scores from half a million blood samples to predict 12 common diseases, showing they outperform polygenic scores and benefit from repeat measurements.

networkGWAS improves network-based GWAS by using mixed models and permutation schemes to detect genetic associations with better statistical control.

The paper identifies age-specific genetic effects on age at natural menopause using UK Biobank data, revealing varying biological pathways and health outcomes across menopause timing.

The paper uses genetic variation to assess the causal impact of food choices on health outcomes, identifying robust associations between eating patterns and several diseases.

The paper develops eleven omics-based biological age clocks with strong correlations to chronological age and shows their association with disease risk.

This study uses Mendelian randomization to identify 417 potential causal links between dietary patterns and blood metabolites, including novel associations and replication of known ones.

This paper introduces BayesW, a Bayesian method for analyzing time-to-event phenotypes, showing improved statistical power and prediction accuracy for age-at-onset traits in large-scale datasets.

The paper identifies BMI-associated metabolites using untargeted metabolomics and causal inference, finding both known and unknown metabolites with causal links to obesity.

This study identifies cell types associated with brain complex traits using GWAS and single-cell transcriptomics, revealing novel roles for oligodendrocytes in Parkinson's disease.

The paper estimates genetic correlations between eating disorder and substance-use-related phenotypes using GWAS data, finding both positive and negative associations.

PAIRUP-MS imputes and annotates unknown metabolite signals in mass spectrometry data, improving pathway analysis and meta-analysis power for metabolomics studies.

This paper compares genetic risk scores for breast cancer, finding that a combined metaGRS2 provides the strongest predictive ability in two cohorts.

The paper identifies genetic loci associated with lifespan using genome-wide association and shows that variants linked to modern diseases explain most lifespan variation.

This study identifies 16 causal coding variants in type 2 diabetes using fine-mapping and shows that one third of signals are false leads.

This paper uses transcriptomic imputation to identify gene-tissue associations linked to Anorexia Nervosa risk, finding significant links in both brain and metabolic tissues.

This study fine-maps type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps, identifying 73 variants with >80% posterior probability of association.

This study uses genome sequencing and epigenomic data to identify genetic variants influencing blood cell traits and uncover regulatory mechanisms in hematopoiesis.

This study develops a genetic risk score for Type 2 Diabetes prediction and finds it improves risk prediction accuracy when added to existing models.