Evidence map›Paper›PMID 42539107›Full record

ArticlebioRxiv : the preprint server for biology2026

PMBB Geno-Pheno Toolkit: A suite of scalable, reproducible pipelines for cross-biobank association analyses.

Zachary B Rodriguez, Lindsay Guare, Lannawill Caruth, Katie M Cardone, Christopher Carson, Tess Cherlin, Stephanie Mohammed, Hritvik Gupta, Rachit Kumar, Karl Keat and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Zachary B RodriguezDepartment of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-4591-803X
Lindsay GuareDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0001-6988-5319
Lannawill CaruthDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0009-0005-4100-5830
Katie M CardoneInstitute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0009-0003-7325-2233
Christopher CarsonJacobs School of Medicine and Biomedical Sciences at the University at Buffalo, Buffalo, NY 14203.ORCID 0009-0004-7952-7629
Tess CherlinDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0001-7495-7313
Stephanie MohammedDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA 19104.ORCID 0000-0002-3458-3991
Hritvik GuptaDepartment of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0009-0001-6088-8078
Rachit KumarDepartment of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-7736-3307
Karl KeatDepartment of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-0945-5816
Shefali S VermaInstitute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.
Anurag VermaDepartment of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA.ORCID 0000-0002-5063-9107

Funding

Phenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4M
NCATS NIH HHS UL1 TR001878
6 · The paper itself

Abstract

Electronic health record (EHR)-linked biobanks generate unprecedented genomic and phenotypic datasets, but their scientific utility is constrained by data fragmentation across institutional silos and incompatible computing infrastructures, forcing researchers to rewrite ad-hoc scripts for each new environment. We present the PMBB Geno-Pheno Toolkit, a suite of modular Nextflow pipelines for biobank-scale association analyses. This note focuses on the toolkit's SAIGE family of pipelines - supporting genome-wide (GWAS), exome-wide (ExWAS), and phenome-wide (PheWAS) association testing - together with the companion GWAMA and ExWAS meta-analysis pipelines that enable cross-biobank replication. All components are containerized (Docker/Apptainer) and orchestrated with Nextflow, allowing the same workflows to run unmodified on local HPC clusters, cloud platforms, and the All of Us Research Workbench. Complementary toolkit pipelines for PLINK-based GWAS, polygenic scoring, LD-based clumping, and phenotype harmonization are also available and briefly noted.

Identifiers

PMID42539107
PMCPMC13419720

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.