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Updated: Jun 28, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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Published on: December 27, 2010

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

James T Baker1, Hung-Hsin Chen2, Grahame F Evans1

  • 1Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Genetic Epidemiology
|June 27, 2026
PubMed
Summary
This summary is machine-generated.

New DRIVE v3 software integrates identity-by-descent (IBD) analysis with phenotype data to find shared genetic variations linked to diseases. This tool enhances genetic discovery by connecting haplotype sharing with specific traits.

Keywords:
biobanksidentity‐by‐descentpopulation geneticssoftware

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Existing genetic analytical methods often separate identity-by-descent (IBD) sharing identification from phenotype association testing.
  • There is a need for integrated tools that can discover novel shared haplotypes contributing to disease traits by combining IBD and phenotypic enrichment.
  • Current tools require separate analyses for IBD detection and subsequent interpretation, limiting efficiency in large-scale genetic studies.

Purpose of the Study:

  • To introduce Distant Relatedness for Identification and Variant Evaluation (DRIVE) v3, a novel python command-line interface tool.
  • To enable the identification of participant networks sharing identical haplotypes and to estimate trait enrichment within these networks.
  • To demonstrate the utility and performance improvements of DRIVE v3 in analyzing large-scale genetic data for disease-associated variants.

Main Methods:

  • Development of DRIVE v3, a python-based tool for identifying networks of individuals with shared haplotypes at specific genomic locations.
  • Integration of phenotypic data to perform enrichment testing for dichotomous traits within identified haplotype-sharing networks.
  • Application of DRIVE v3 to analyze genetic data for an autosomal dominant condition (cardiomyopathy) and an autosomal recessive condition (cystic fibrosis).

Main Results:

  • DRIVE v3 successfully identifies networks of participants sharing identical haplotypes across genomic locations.
  • The tool demonstrates significant performance improvements compared to previous versions, facilitating efficient analysis of large datasets.
  • Phenotypic enrichment testing within identified networks provides insights into the association of specific traits with shared haplotypes, as shown in cardiomyopathy and cystic fibrosis cases.

Conclusions:

  • DRIVE v3 offers an integrated approach for genetic discovery by combining IBD analysis with phenotypic enrichment.
  • The tool's efficient design and versatile API allow for flexible integration into existing bioinformatics pipelines for large-scale genetic resources.
  • DRIVE v3 enhances the interpretation of genetic findings by directly linking haplotype sharing networks to specific disease traits.