GuidelineNature protocols2026
A practical guide to identifying associations between tandem repeats and complex human traits using consensus genotypes from multiple tools.
Guideline in Nature protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed.
- Gene expression and chromatin state mapped in space.Nature protocols · 2025Article
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27 authors.
Funding
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.
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