Software, Statistics, Probability and Uncertainty, Genetics, Neurology (clinical)
10
Scopus Publications
Scopus Publications
Knockoff-Based Fine Mapping of MS-Associated SNPs in Sardinian Trios Giulia Nicole Baldrighi, Andrea Nova, Claus Thorn Ekstrøm, Maria Luisa Piras, Maria Valeria Saddi, et al. Biochemical Genetics, 2026 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.
Exploring the complexities of epigenetics in multiple sclerosis: A study involving meta-analysis of DNA methylation profiles, epigenetic drift, and rare epivariations Giulia Nicole Baldrighi, Rebecca Cavagnola, Davide Sacco, Lucy Costantino, Luisa Bernardinelli, et al. Multiple Sclerosis Journal Experimental Translational and Clinical, 2024 Background Multiple sclerosis (MS) is an autoimmune condition characterized by inflammatory and neurodegenerative traits. Recently, DNA methylation has emerged as a promising field of investigation for elucidating dynamics characterizing MS development and progression. Objectives This study aimed to comprehensively investigate the role of epigenetics in MS by analyzing the methylation profiles from blood and brain tissues from public datasets. Methods Employing a meta-analytical framework for differential methylation analyses, the study extended beyond conventional analyses to explore additional dimensions of epigenetic regulation, including epigenetic drift, age acceleration, and rare epivariations. Results Results of the differential methylation analysis were in line with previously reported findings. No significant differences were observed in age acceleration or global epigenetic drift between MS cases and controls. However, upon closer analysis at the gene level, distinctive patterns of epigenetic drift emerged, particularly within genes implicated in neural biological functions. Conclusions These findings underscore the role of epigenetic modifications in shaping MS pathology. Furthermore, the study unveiled the exclusive presence of rare epivariations within the MS cases, some of which involved genes previously linked to MS or other autoimmune diseases. This highlights the potential significance of rare genetic aberrations in driving MS susceptibility and progression.
Investigating the Epigenetic Landscape of Major Depressive Disorder: A Genome-Wide Meta-Analysis of DNA Methylation Data, Including New Insights into Stochastic Epigenetic Mutations and Epivariations Giulia Nicole Baldrighi, Rebecca Cavagnola, Luciano Calzari, Davide Sacco, Lucy Costantino, et al. Biomedicines, 2024 Background/Objectives: Major depressive disorder (MDD) is a mental health condition that can severely impact patients’ social lives, leading to withdrawal and difficulty in maintaining relationships. Environmental factors such as trauma and stress can worsen MDD by interacting with genetic predispositions. Epigenetics, which examines changes in gene expression influenced by the environment, may help identify patterns linked to depression. This study aimed to explore the epigenetic mechanisms behind MDD by analysing six public datasets (n = 1125 MDD cases, 398 controls in blood; n = 95 MDD cases, 96 controls in brain tissues) from the Gene Expression Omnibus. Methods: As an innovative approach, two meta-analyses of DNA methylation patterns were conducted alongside an investigation of stochastic epigenetic mutations (SEMs), epigenetic age acceleration, and rare epivariations. Results: While no significant global methylation differences were observed between MDD cases and controls, hypomethylation near the SHF gene (brain-specific probe cg25801113) was consistently found in MDD cases. SEMs revealed a gene-level burden in MDD, though epigenetic age acceleration was not central to the disorder. Additionally, 51 rare epivariations were identified in blood tissue and 1 in brain tissue linked to MDD. Conclusions: The study emphasises the potential role of rare epivariations in MDD’s epigenetic regulation but calls for further research with larger, more diverse cohorts to confirm these findings.
A systematic review of Mendelian randomization studies on multiple sclerosis Teresa Fazia, Giulia Nicole Baldrighi, Andrea Nova, Luisa Bernardinelli European Journal of Neuroscience, 2023 Mendelian randomization (MR) is a powerful approach for assessing the causal effect of putative risk factors on an outcome, using genetic variants as instrumental variables. The methodology and application developed in the framework of MR have been dramatically improved, taking advantage of the many public genome‐wide association study (GWAS) data. The availability of summary‐level data allowed to perform numerous MR studies especially for complex diseases, pinpointing modifiable exposures causally related to increased or decreased disease risk. Multiple sclerosis (MS) is a complex multifactorial disease whose aetiology involves both genetic and non‐genetic risk factors and their interplay. Previous observational studies have revealed associations between candidate modifiable exposures and MS risk; although being prone to confounding, and reverse causation, these studies were unable to draw causal conclusions. MR analysis addresses the limitations of observational studies and allows to establish reliable and accurate causal conclusions. Here, we systematically reviewed the studies evaluating the causal effect, through MR, of genetic and non‐genetic exposures on MS risk. Among 107 papers found, only 42 were eligible for final evaluation and qualitative synthesis. We found that, above all, low vitamin D levels and high adult body mass index (BMI) appear to be uncontested risk factors for increased MS risk.
A Pipeline for Phasing and Genotype Imputation on Mixed Human Data (Parents-Offspring Trios and Unrelated Subjects) by Reviewing Current Methods and Software Giulia Nicole Baldrighi, Andrea Nova, Luisa Bernardinelli, Teresa Fazia Life, 2022 Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.
Heritability Estimation of Multiple Sclerosis Related Plasma Protein Levels in Sardinian Families with Immunochip Genotyping Data Andrea Nova, Giulia Nicole Baldrighi, Teresa Fazia, Francesca Graziano, Valeria Saddi, et al. Life, 2022 This work aimed at estimating narrow-sense heritability, defined as the proportion of the phenotypic variance explained by the sum of additive genetic effects, via Haseman–Elston regression for a subset of 56 plasma protein levels related to Multiple Sclerosis (MS). These were measured in 212 related individuals (with 69 MS cases and 143 healthy controls) obtained from 20 Sardinian families with MS history. Using pedigree information, we found seven statistically significant heritable plasma protein levels (after multiple testing correction), i.e., Gc (h2 = 0.77; 95%CI: 0.36, 1.00), Plat (h2 = 0.70; 95%CI: 0.27, 0.95), Anxa1 (h2 = 0.68; 95%CI: 0.27, 1.00), Sod1 (h2 = 0.58; 95%CI: 0.18, 0.96), Irf8 (h2 = 0.56; 95%CI: 0.19, 0.99), Ptger4 (h2 = 0.45; 95%CI: 0.10, 0.96), and Fadd (h2 = 0.41; 95%CI: 0.06, 0.84). A subsequent analysis was performed on these statistically significant heritable plasma protein levels employing Immunochip genotyping data obtained in 155 healthy controls (92 related and 63 unrelated); we found a meaningful proportion of heritable plasma protein levels’ variability explained by a small set of SNPs. Overall, the results obtained, for these seven MS-related proteins, emphasized a high additive genetic variance component explaining plasma levels’ variability.
