FWF-funded project: Intra-Repeat VNTR Resolution and Risk Prediction
About
Tandem repeats make up about 8% of the human genome, yet sequence variation within variable number tandem repeats (VNTRs) is still largely excluded from large-scale genetic analyses. Reads from VNTR regions map equally well to multiple locations and are usually filtered out before variant calling. Growing evidence suggests that this hidden variation contributes substantially to the missing heritability of complex traits.
Our group has developed computational methods to resolve intra-repeat variation from short-read sequencing data, using the LPA KIV-2 VNTR as a model (Di Maio et al., Genome Biology 2024). LPA determines lipoprotein(a) concentrations, the strongest known genetic risk factor for cardiovascular disease.
This project, funded by the Austrian Science Fund (FWF; Grant DOI: 10.55776/PAT3357425), builds a framework that analyzes repetitive and non-repetitive genomic regions together, enabling GWAS-style association testing, fine-mapping and polygenic risk prediction that include VNTR variants.
The project has four main aims:
- Characterize the genetic diversity of the LPA KIV-2 VNTR across multiple ancestries using large-scale whole-genome sequencing data (UK Biobank, All of Us).
- Develop an analytical framework that integrates repetitive and non-repetitive genomic regions for combined GWAS and fine-mapping analyses.
- Extend the approach to other VNTRs, focusing on genes involved in chronic kidney disease (CKD), with experimental validation using nanopore sequencing.
- Develop methods to predict intra-repeat VNTR variation from SNP array data using fine-mapping and machine-learning approaches.
All workflows are developed with Nextflow and made openly available (e.g. vntr-calling-nf, nf-gwas).
Team
Professor of Digital and Computational Genetics
+43 512 9003 70579
sebastian.schoenherr@i-med.ac.at
PhD Student
Collaborators
- Prof. Kai-Uwe Eckardt, Charité Berlin (GCKD study)
- Dr. Cristian Pattaro, Eurac Research, Bolzano
Related publications
Di Maio S, Zöscher P, Weissensteiner H, Forer L, Schachtl-Riess JF, Amstler S, Streiter G, Pfurtscheller C, Paulweber B, Kronenberg F, Coassin S, Schönherr S: Resolving intra-repeat variation in medically relevant VNTRs from short-read sequencing data using the cardiovascular risk gene LPA as a model. Genome Biol. 25:167, 2024. PMID: 38926899 Journal Article
Amstler S, Streiter G, Pfurtscheller C, Forer L, Di Maio S, Weissensteiner H, Paulweber B, Schönherr S, Kronenberg F, Coassin S: Nanopore sequencing with unique molecular identifiers enables accurate mutation analysis and haplotyping in the complex lipoprotein(a) KIV-2 VNTR. Genome Med. 16:117, 2024. PMID: 39380090 Journal Article
Schönherr S, Schachtl-Riess JF, Di Maio S*, Filosi M, Mark M, Lamina C, Fuchsberger C, Kronenberg F, Forer L: Performing highly parallelized and reproducible GWAS analysis on biobank-scale data. NAR Genom Bioinform 6:lqae015, 2024. PMID: 38327871 Journal Article
Further information on this project can be found in the FWF Research Radar.