Evidence map›Paper›PMID 42363641›Full record

ArticleGenetic epidemiology2026

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

James T Baker, Hung-Hsin Chen, Grahame F Evans, Alyssa C Scartozzi, Ryan J Bohlender, Chad D Huff, Quinn S Wells, David C Samuels, Jennifer E Below

Abstract read
In one paragraph

Article in Genetic epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

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5 · Who and what money

Authors and funding

9 authors.

James T BakerDivision of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Hung-Hsin ChenInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Grahame F EvansDivision of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Alyssa C ScartozziDivision of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Ryan J BohlenderUniversity of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Chad D HuffUniversity of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID https://orcid.org/0000-0002-1100-9364
Quinn S WellsDepartment of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
David C SamuelsDepartment of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
Jennifer E BelowDivision of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.ORCID https://orcid.org/0000-0002-1346-1872

Funding

Training Program on Genetic Variation and Human PhenotypesT32GM080178 · NIGMS · VANDERBILT UNIVERSITY · PI COX, NANCY J, SAMUELS, DAVID C · 2007 to 2021
$3.1M
Harnessing the power of genetic relatedness for disease gene discoveryR01GM133169 · NIGMS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BELOW, JENNIFER, HUFF, CHAD DANIEL · 2019 to 2022
$2.6M
NIH HHS R01GM133169NIH HHS R01HL159557NIH HHS RHL174052ANIH HHS T32GM080178
6 · The paper itself

Abstract

There is a need for genetic analytical methods that integrate multi-individual identity-by-descent (IBD) tools with phenotypic enrichment testing to discover novel shared haplotypes contributing to disease traits. Existing tools are designed to identify IBD sharing and leave interpretation and phenotype association tests to further analyses. Here we present Distant Relatedness for Identification and Variant Evaluation (DRIVE) v3, a python command-line interface tool that identifies networks of participants who share an identical haplotype at a given genomic location. Given phenotypic data, DRIVE additionally estimates significant enrichment of dichotomous traits within networks. DRIVE is designed for efficient use across large-scale genetic data resources, featuring a versatile application programming interface and a backend structure designed for flexible integration into existing analytical pipelines. In this work, we describe the implementation of DRIVE v3 and illustrate two applications of the tool to an autosomal dominant condition and to an autosomal recessive condition, cardiomyopathy and cystic fibrosis, respectively. These applications highlight the substantial performance improvements between v1 and v3 and demonstrate practically how the newer features of DRIVE such as the enrichment test can be used in the interpretation of the identified networks.

Indexed as

HaplotypesSoftwareBiological Specimen BanksCardiomyopathiesCluster AnalysisClustering AlgorithmsCystic FibrosisHumansPhenotypebiobanksidentity‐by‐descentpopulation geneticssoftware

Identifiers

PMID42363641
PMCPMC13309748

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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.