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A practical guide to identifying associations between tandem repeats and complex human traits using consensus genotypes from multiple tools

  • Ibra Lujumba
  • , Yagoub Adam
  • , Helyaneh Ziaei Jam
  • , Itunuoluwa Isewon
  • , Nomakhosazana Monnakgotla
  • , Yang Li
  • , Blessing Onyido
  • , Fredrick Kakembo
  • , Faith Adegoke
  • , Jerry Emmanuel
  • , Jumoke Adeyemi
  • , Olajumoke Ibitoye
  • , Samuel Owusu-Ansah
  • , Matthew Boladele Akanle
  • , Joseph Habi
  • , Mike Nsubuga
  • , Ronald Galiwango
  • , Martin Okitwi
  • , Magdalene Namuswe
  • , Walter Odur
  • Zama Mngadi, Marion Adebiyi, Jelili Oyelade, Melissa Nel, Daudi Jjingo, Melissa Gymrek, Ezekiel Adebiyi*
*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

Abstract

Tandem repeats (TRs) are highly variable loci in the human genome that are linked to various human phenotypes. Accurate and reliable genotyping of TRs is important in understanding population TR variation dynamics and their effects in TR-trait association studies. In this protocol, we describe how to generate high-quality consensus TR genotypes for population genomics studies. In particular, we detail steps to: (i) perform TR genotyping from short-read whole-genome sequencing data by using the HipSTR, GangSTR, adVNTR and ExpansionHunter tools, (ii) perform quality control checks on TR genotypes by using TRTools and (iii) integrate TR genotypes from different tools by using EnsembleTR. We further discuss how to visualize and investigate TR variation patterns to identify population-specific expansions and perform TR-trait association analyses. We demonstrate the utility of these steps by analyzing a small dataset from the 1000 Genomes Project. In addition, we recapitulate a previously identified association between TR length and gene expression in the African population and provide a generalized discussion on TR analysis and its relevance to identifying complex traits. The expected time for installing the necessary software for each section is ~10 min. The expected run time on the user’s desired dataset can vary from hours to days depending on factors such as the size of the data, input parameters and the capacity of the computing infrastructure.

Original languageEnglish
Number of pages19
JournalNature Protocols
Early online date1 Sept 2025
DOIs
Publication statusE-pub ahead of print - 1 Sept 2025

Bibliographical note

Publisher Copyright:
© Springer Nature Limited 2025.

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