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Knockoff-Based Fine Mapping of MS-Associated SNPs in Sardinian Trios
Giulia Nicole Baldrighi1, Andrea Nova2, Claus Thorn Ekstrøm3
1Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100, Pavia, Italy. giulianicole.baldrighi01@universitadipavia.it.
Biochemical Genetics
|August 30, 2025
Summary
This study identified genetic variants associated with multiple sclerosis (MS) risk in the Sardinian population. A family-based approach found a risk variant in MYO1D and a protective variant in ASIC2, aiding MS genetic research.
Area of Science:
- Genetics
- Immunology
- Neuroscience
Background:
- Genetic predisposition is crucial in complex diseases like multiple sclerosis (MS).
- Identifying specific genetic variants for MS susceptibility is challenging.
- The Sardinian population offers unique genetic characteristics for studying MS.
Purpose of the Study:
- To identify genetic variants associated with multiple sclerosis susceptibility using a family-based fine mapping approach.
- To leverage the unique genetic landscape of the Sardinian population for enhanced detection of genetic signals.
- To investigate the functional implications of identified variants through expression and trait analyses.
Main Methods:
- A family-based fine mapping approach was applied to 142 Sardinian trios.
- Genotype imputation identified 2537 variants within a targeted genomic region including ASIC2.
- The knockoff Trio method was used to analyze variants for association with MS susceptibility.
Main Results:
- The variant rs756787 (MYO1D 3'UTR) was associated with increased MS risk (OR 1.57, p=0.02).
- The variant rs56175840 (ASIC2 intronic) showed a protective effect against MS (OR 0.17, p=0.02).
- Expression and trait analyses linked rs756787 to genes involved in neurodegeneration and Epstein-Barr virus response.
Conclusions:
- Knockoff-based fine mapping in family datasets is effective for identifying MS genetic risk factors.
- The identified variants in MYO1D and ASIC2 contribute to understanding MS pathogenesis.
- Integrating genetic data with expression and trait information provides insights into disease mechanisms.
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