A systematic review of Mendelian randomization studies on multiple sclerosis.

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.

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.

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.