ArticleGenome biology2024
Resolving intra-repeat variation in medically relevant VNTRs from short-read sequencing data using the cardiovascular risk gene LPA as a model.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Umi-pipeline-nf: a modular and scalable workflow for UMI-tagged nanopore amplicon analysis with real-time sequencing integration and GPU-acceleration.Bioinformatics (Oxford, England) · 2026Article
- Recent advances in understanding the spectrum of genetic determinants of lipoprotein(a) levels.Current opinion in lipidology · 2026Review
- Evolutionary Balancing of Genetic Consequence and Innovation in Mammals Through Variable Number Tandem Repeats.Genome biology and evolution · 2026Review
- Diverse short tandem repeat sequences influence gene regulation in human populations.Genome biology · 2025Article
- Accurate detection of tandem repeats exposes ubiquitous reuse of biological sequences.Nucleic acids research · 2025Article
- KILDA: identifying KIV-2 repeats from kmers.NAR genomics and bioinformatics · 2025Article
- Nanopore sequencing with unique molecular identifiers enables accurate mutation analysis and haplotyping in the complex lipoprotein(a) KIV-2 VNTR.Genome medicine · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
backgroundVariable number tandem repeats (VNTRs) are highly polymorphic DNA regions harboring many potentially disease-causing variants. However, VNTRs often appear unresolved ("dark") in variation databases due to their repetitive nature. One particularly complex and medically relevant VNTR is the KIV-2 VNTR located in the cardiovascular disease gene LPA which encompasses up to 70% of the coding sequence.
resultsUsing the highly complex LPA gene as a model, we develop a computational approach to resolve intra-repeat variation in VNTRs from largely available short-read sequencing data. We apply the approach to six protein-coding VNTRs in 2504 samples from the 1000 Genomes Project and developed an optimized method for the LPA KIV-2 VNTR that discriminates the confounding KIV-2 subtypes upfront. This results in an F1-score improvement of up to 2.1-fold compared to previously published strategies. Finally, we analyze the LPA VNTR in > 199,000 UK Biobank samples, detecting > 700 KIV-2 mutations. This approach successfully reveals new strong Lp(a)-lowering effects for KIV-2 variants, with protective effect against coronary artery disease, and also validated previous findings based on tagging SNPs.
conclusionsOur approach paves the way for reliable variant detection in VNTRs at scale and we show that it is transferable to other dark regions, which will help unlock medical information hidden in VNTRs.
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