Structural variants in linkage disequilibrium with GWAS-significant SNPs.
Hao Liang1, Joni C Sedillo1,2, Steven J Schrodi1,2
1Department of Medical Genetics, University of Wisconsin-Madison, Madison, WI, USA.
Heliyon
|June 17, 2024
Summary
This study introduces a new database linking structural variants (SVs) with genome-wide significant single nucleotide polymorphisms (SNPs) in high linkage disequilibrium. This resource aids in understanding the role of SVs in disease architecture and risk prediction models.
Area of Science:
- Genomics
- Human Genetics
- Bioinformatics
Background:
- Genome-wide association studies (GWAS) identify significant single nucleotide polymorphisms (SNPs) linked to diseases, but their functional roles are often unclear.
- A proportion of these SNPs may be associated with disease due to linkage disequilibrium with causal structural variants (SVs), not the SNPs themselves.
- Understanding the interplay between SVs and SNPs is crucial for deciphering complex disease architecture.
Purpose of the Study:
- To create a comprehensive resource cataloging pairs of structural variants (SVs) and genome-wide significant single nucleotide polymorphisms (SNPs) in high linkage disequilibrium.
- To facilitate the investigation of disease-associated regions by identifying potential causal SVs linked to known GWAS SNPs.
- To provide a tool for fine-mapping genetic associations and potentially improve disease risk prediction models.
Main Methods:
- Compilation of SNPs exhibiting genome-wide significant associations with traits, predominantly disease phenotypes.
- Integration of newly identified structural variants (SVs) from recent genomic studies.
- Calculation of linkage disequilibrium (LD) values between SVs and SNPs using unphased genetic data.
Main Results:
- Development of the SV-SNP LD Database, a catalog of SV-SNP pairs in high linkage disequilibrium.
- The database includes data on SVs, GWAS SNPs, and their calculated LD values.
- Analysis results provide a valuable fine-mapping tool for exploring SVs in linkage disequilibrium with disease-associated SNPs.
Conclusions:
- The SV-SNP LD Database serves as a critical resource for understanding the contribution of structural variants to human disease.
- This resource can guide future research into the functional roles of SVs and their association with complex traits.
- The findings are expected to advance the incorporation of structural variants into disease risk prediction models.
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