| Title: | Resolving intra-repeat variation in medically relevant VNTRs from short-read sequencing data using the cardiovascular risk gene LPA as a model |
| Journal: | Genome Biology |
| Published: | 26 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38926899/ |
| DOI: | https://doi.org/10.1186/s13059-024-03316-5 |
| URL: | https://genomebiology.biomedcentral.com/counter/pdf/10.1186/s13059-024-03316-5 |
Publication 11787
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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.</p>
7 Keywords
- Cardiovascular Diseases
- Genetic Predisposition to Disease
- Genetic Variation
- Humans
- Lipoprotein(a)
- Minisatellite Repeats
- Sequence Analysis, DNA
12 Authors
- Silvia Di Maio
- Peter Zöscher
- Hansi Weissensteiner
- Lukas Forer
- Johanna F. Schachtl-Riess
- Stephan Amstler
- Gertraud Streiter
- Cathrin Pfurtscheller
- Bernhard Paulweber
- Florian Kronenberg
- Stefan Coassin
- Sebastian Schönherr
1 Application
| Application ID | Title |
|---|---|
| 62905 | Analyzing unmappable ("camouflaged") genome regions in UK Biobank exome sequencing data using the LPA gene as a model. |
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