Genetic Control of MS-Relevant Plasma Proteins in Sardinian Families: Heritability and Immunochip pQTL Insights
Multiple sclerosis (MS) is a chronic autoimmune demyelinating disorder of the central nervous system with a multifactorial etiology that combines genetic susceptibility and environmental exposures. In their 2022 study, Nova and colleagues address a translationally relevant question: to what extent are circulating levels of MS-related plasma proteins under additive genetic control, and can immune-focused genotyping platforms help explain that variability? By focusing on extended Sardinian pedigrees—an advantageous setting due to relative genetic homogeneity and elevated MS prevalence—the authors position plasma protein quantitative traits as an intermediate phenotype that may illuminate biologic pathways and potential biomarker candidates in MS.
Study Design and Cohort Characteristics
The investigators analyzed 212 related individuals from 20 extended Sardinian families enriched for MS (69 cases and 143 controls), with each family spanning multiple relatives and containing a variable number of affected members. Plasma levels for 56 a priori selected MS-relevant proteins were quantified using a bead-based antibody array workflow, producing relative signal intensities subsequently normalized to mitigate dilution and technical variation. A subset of these participants also had Immunochip genotyping data, enabling downstream protein quantitative trait loci (pQTL) analyses; importantly, pQTL testing was restricted to unaffected subjects to reduce the risk of reverse causation (disease activity altering protein levels).
Quantifying Additive Genetic Influence on Protein Levels
To estimate narrow-sense heritability (h²)—the proportion of phenotypic variance attributable to additive genetic effects—the authors used a linear mixed modeling framework incorporating fixed effects for sex and MS status and random effects representing (i) additive genetic similarity derived from pedigree-based kinship and (ii) a shared household environment component. Rather than relying on conventional REML estimation, they implemented Haseman–Elston regression, which operationalizes heritability estimation by regressing pairwise residual cross-products on entries of covariance matrices (kinship, shared environment, identity), and then derives confidence intervals constrained to the natural [0,1] range. Multiple testing was addressed by controlling the false discovery rate (FDR) at 0.05 across the 56 proteins.
Primary Heritability Findings
Seven proteins exhibited statistically significant heritability after FDR correction, each with moderate-to-high additive genetic contributions: Gc (h² ≈ 0.77), Plat (≈ 0.70), Anxa1 (≈ 0.68), Sod1 (≈ 0.58), Irf8 (≈ 0.56), Ptger4 (≈ 0.45), and Fadd (≈ 0.41). These results indicate that, within these pedigrees, a meaningful fraction of inter-individual variability in these plasma proteins is explained by additive genetic variance even after accounting for shared environmental similarity. In parallel, a small number of proteins showed statistically significant shared environment effects (c²) after correction, underscoring that not all familial resemblance is genetic and motivating the authors’ explicit modeling of household-level confounding.
Integrating Immunochip Data to Identify pQTL Signals
For the seven heritable proteins, the authors performed pQTL mapping using Immunochip variants in healthy controls (92 related plus 63 unrelated). After quality control filtering on minor allele frequency, Hardy–Weinberg equilibrium, and linkage disequilibrium pruning, they tested each retained SNP for association with protein levels using linear mixed models that again accounted for kinship and shared environment. To move beyond single-variant signals and quantify joint explanatory power, they selected candidate variants from the strongest univariate associations and applied stepwise model selection at a stringent inclusion threshold (α = 1×10⁻⁴), then estimated the marginal proportion of variance explained by the selected SNP set using an R² framework tailored to mixed models, with confidence intervals obtained via block bootstrap resampling at the family level.
How Much Variability Can a Small SNP Set Explain?
A notable outcome is that each of the seven proteins had at least four significant Immunochip SNPs in the multivariable model, jointly explaining on the order of ~40% or more of protein-level variability in the pQTL sample. The strongest example was Gc, where six SNPs explained roughly two-thirds of variance (about 67%), consistent with its high pedigree-based heritability estimate. Ptger4 and Fadd were each associated with larger multi-SNP sets (eight variants) explaining close to ~60% of variance, whereas Anxa1 had fewer variants and lower explained variance (about 39%). The authors emphasize an important interpretive point: h² and the pQTL-derived R² are not directly interchangeable because they are derived from different samples and model targets—h² captures aggregate additive genetic influence (including untyped variants), while R² summarizes variance explained by a specific, selected subset of Immunochip loci.
Biological Interpretation, Strengths, and Limitations
The discussion links each highly heritable protein to plausible MS-relevant biology: Gc (vitamin D binding and immunomodulation), Plat (fibrinolysis and axonal integrity in neuroinflammation), Anxa1 (resolution of inflammation and leukocyte trafficking), Sod1 (oxidative stress defense), Irf8 and Ptger4 (immune transcriptional and signaling pathways including T-cell regulation), and Fadd (apoptotic signaling with potential relevance to demyelination and CNS inflammation). Methodologically, the study’s strengths include family-based inference with explicit separation of additive genetic and shared environmental effects, and a two-stage design that connects heritability to genotype-based explanatory models. Key limitations are also acknowledged: modest sample sizes for both proteomic and genotyped subsets, lack of orthogonal validation of protein measurements using alternative assays, and the constrained genomic scope of Immunochip (focused on immune loci rather than genome-wide coverage). Taken together, the work supports the view that a subset of MS-related plasma proteins behaves as genetically regulated quantitative traits and provides a practical template for integrating pedigree-based heritability with targeted pQTL mapping to prioritize proteins and pathways for follow-on mechanistic and biomarker studies.
Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.
References:
Nova, A., Baldrighi, G. N., Fazia, T., Graziano, F., Saddi, V., Piras, M., ... & Bernardinelli, L. (2022). Heritability estimation of multiple sclerosis related plasma protein levels in sardinian families with immunochip genotyping data. Life, 12(7), 1101.
