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

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.

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Publication Details

Journal
Genetic Epidemiology
Published
2026-06-26
DOI
https://doi.org/10.1002/gepi.70048
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
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article

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

David C. Samuels, Alyssa Scartozzi, Jennifer E Below, Chad D. Huff et al.
Genetic Epidemiology
Genetic Associations and Epidemiology
article

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

David C. Samuels, Alyssa Scartozzi, Jennifer E Below, Chad D. Huff, James Baker, Hung‐Hsin Chen, Grahame Evans, Quinn S. Wells, Ryan J. Bohlender
article en

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.

Genetic EpidemiologyVol. 50(5)
The University of Texas MD Anderson Cancer Center (US), Vanderbilt University (US), Institute of Biomedical Sciences, Academia Sinica (TW), Vanderbilt University Medical Center (US)
Openalex Percentile: Top 6%
Genetic Associations and Epidemiology
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