Fritsche Lab · University of Michigan

We test what genomic and health data can reliably tell us.

We study prediction models built from genomic data, health records, and patient-reported information. Much of our work begins after a model has been built: we test how well it performs in new populations and health systems, and why performance varies.

Independent evaluation
Develop

One cohort

Model development A model curve follows a set of blue observations in the development cohort. These marks are illustrative, not measured results.
Test

An independent cohort

Independent evaluation The identical model curve is tested against a different set of teal observations. Fine vertical lines mark selected differences between the model and observations. These marks are illustrative, not measured results.
Same model. New people. Conceptual illustration
Independent evaluationTesting models with data that were not used to build them
Longitudinal contextLinking genomics with health records and patient reports
Reusable resultsPublishing methods, score files, mappings, and comparisons
Team scienceWorking with clinical, statistical, and computational colleagues

Research

Do findings hold up in a new setting?

This question connects our work in polygenic risk, biobanks linked to electronic health records (EHRs), mental health, and pain.

Research overview →

Mental health

Precision Mental Health

We study mental health treatment response using genetic, clinical, and patient-reported data.

Current collaboration · Precision mental health

Why does the same mental health treatment help one person more than another?

COMPASS studies treatment response using genetic, clinical, and patient-reported information. The principal investigators are Amy Bohnert, Srijan Sen, and Lars Fritsche. Lars leads the genetics and EHR data work.

Conceptual study design combining genetic data, clinical and treatment history, and patient reports to study mental health treatment response over time. Trajectories are illustrative. Models are tested in a separate dataset when suitable data are available. Conceptual study design combining genetic data, clinical and treatment history, and patient reports to study mental health treatment response over time. Trajectories are illustrative. Models are tested in a separate dataset when suitable data are available.

Research resource

Compare published polygenic risk scores across biobanks

PRSweb reports how published scores performed in the Michigan Genomics Initiative and UK Biobank. The three archived releases remain unchanged, so their results and source files continue to match the papers that describe them.

PRSweb Research Portals

Scores, comparisons, and source files in one place

Each release includes performance results, phenome-wide associations, phenotype mappings, methods, and downloadable score files.

The PRSweb landing page with Fritsche Lab branding, cross-portal trait search, and links to Skin Cancer PRSweb, Cancer PRSweb, and ExPRSweb.

Software and teaching

Tools for reproducible research

Software, practical guides, and teaching materials for data analysis, cluster computing, and coding.

All tools and data →

PheWAS software

phewasFlow

An R package for reproducible phenome-wide association studies in both directions, with local and cluster workflows, multiple-testing correction, and Manhattan and volcano plots.

Research computing guide

Fritsche Lab SLURM Playbook

The SLURM Playbook covers submitting and monitoring CPU jobs and job arrays on U-M clusters, diagnosing failures, and rerunning only failed tasks.

Practical GenAI · Part 1

Practical GenAI Coding Guide

An eight-step workflow for AI-assisted research code, covering planning, prompting, review, testing, version control, and documentation.

Practical GenAI · Part 2

Practical GenAI Agentic Coding Guide

Six lessons for working with coding agents in a runnable research repository, from a task brief through implementation, testing, review, and handoff.

Selected research

Papers from our current and earlier work

Publications →
Published 2026

Polygenic Risk Scores for Prediction of Immune Checkpoint Inhibitor Thyroid Toxicity in Diverse Populations

Clinical Cancer Research

Why it matters: Treatment-related outcomes include adverse effects as well as benefits. In 4,289 veterans treated with checkpoint inhibitors, an updated hypothyroidism polygenic score predicted thyroiditis in both the non-Hispanic White and Black patient groups, but not in chemotherapy controls. The older score, which was derived from European-ancestry data, did not predict thyroiditis in the Black patient group. This shows why scores need to be validated in each population and treatment setting.

Published 2025

Assessing the Clinical Utility of Published Prostate Cancer Polygenic Risk Scores in a Large Biobank Data Set

European Urology Oncology

Why it matters: Researchers tested 16 published prostate cancer scores in the Michigan Genomics Initiative (MGI) and used detailed biopsy data to evaluate their clinical utility. Even the best-performing score separated cases from controls only modestly, and none distinguished aggressive from indolent disease. Predicting a diagnosis is not the same as identifying the cancers that most need treatment.

Published 2024

Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank

Journal of Neurology

Why it matters: Genetics, diagnoses, and survey measures did not contribute equally. In UK Biobank, polygenic scores for amyotrophic lateral sclerosis (ALS) offered modest discrimination. Adding diagnoses recorded before onset improved discrimination, while adding the exposure score did not. In this analysis, combining more types of data did not necessarily improve prediction.

People

A team working across disciplines

Our researchers at Michigan Medicine and the School of Public Health bring experience in statistical genetics, clinical research, and research software development.

Lab members and alumni →
Lars Fritsche, PhDLab Head
Ryan Welch, PhDApplication Programmer/Analyst Lead
Sarah Fazal, PhDPostdoctoral Research Fellow
Shivalika Pathania, PhDPostdoctoral Research Fellow
Tai Yang, MSGraduate Student Research Assistant (GSRA) and PhD Candidate

Latest

Research stories, talks, and project news

All news →