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Published on: June 23, 2012
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.
None:
Genetic predisposition plays a key role in autoimmune and complex diseases such as multiple sclerosis (MS). However, identifying the specific variants or genomic regions responsible for disease susceptibility remains a significant challenge. In this study a family-based fine mapping approach was applied to analyze 142 trios, aiming to identify associated genetic variants linked to MS. The targeted genomic region resides within the 17:30,820,506-32,483,270 bp (Ch37/hg19), which includes the protein-coding gene ASIC2, previously implicated in MS and other neurological conditions, with surrounding genes comprising strongly correlated genetic variants to capture the broader signal from the region. Given the high prevalence of MS in Sardinia and the unique genetic characteristics of the Sardinian population, including reduced heterogeneity and extended linkage disequilibrium, we designed our study specifically within this population and focused on family-based data to enhance the power for detecting genetic signals, avoiding false discoveries. Genotype imputation found 2537 variants, which were then analyzed using the knockoff Trio method to identify loci associated with MS susceptibility. We found rs756787 (3'UTR of MYO1D) increased disease risk (OR 1.57, 95% CI [1.07-2.29], p = 0.02), while rs56175840 (intronic ASIC2) showed a protective effect (OR 0.17, 95% CI [0.04-0.74], p = 0.02), demonstrating the power of knockoff-based fine mapping in family datasets. Integrating LD-based expression and trait analyses helped reveal how rs756787 correlates with variants affecting genes involved in neurodegeneration and the immune response to Epstein-Barr virus, a known environmental factor implicated in MS pathogenesis. Our study highlights the effectiveness of knockoff-based fine mapping combined with expression-trait integration to identify genetic variants influencing MS risk in the Sardinian population.
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