Polygenic Multiple Sclerosis Risk and Population‐Based Childhood Brain Imaging

Abstract

Objective: Multiple sclerosis (MS) is a neurological disease with a substantial genetic component and immune-mediated neurodegeneration. Patients with MS show structural brain differences relative to individuals without MS, including smaller regional volumes and alterations in white matter (WM) microstructure. Whether genetic risk for MS is associated with brain structure during early neurodevelopment remains unclear. In this study, we explore the association between MS polygenic risk scores (PRS) and brain imaging outcomes from a large, population-based pediatric sample to gain insight into the underlying neurobiology of MS. Methods: We included 8-to 12-year-old genotyped participants from the Generation R Study in whom T1-weighted volumetric (n = 1,136) and/or diffusion tensor imaging (n = 1,088) had been collected. PRS for MS were calculated based on a large genome-wide association study of MS (n = 41,505) and were regressed on regional volumes, global and tract-specific fractional anisotropy (FA), and global mean diffusivity using linear regression. Results: No associations were observed for the regional volumes. We observed a positive association between the MS PRS and global FA (β = 0.098, standard error [SE] = 0.030, p = 1.08 × 10 −3 ). Tract-specific analyses showed higher FA and lower radial diffusivity in several tracts. We replicated our findings in an independent sample of children (n = 186) who were scanned in an earlier phase (global FA; β = 0.189, SE = 0.072, p = 9.40 × 10 −3 ). Interpretation: This is the first study to show that greater genetic predisposition for MS is associated with higher global brain WM FA at an early age in the general population. Our results suggest a preadolescent time window within neurodevelopment in which MS risk variants act upon the brain.

Integration of epigenetic and genetic profiles identifies multiple sclerosis disease-critical cell types and genes

“…Recent studies demonstrated that polygenetic risk scores are informative measures of risk in MS and other autoimmune diseases [40][41][42][43] . Cell-specific risk scores incorporating variants based on 3D chromatin interaction profiles also show statistically significant association with risk, suggesting that diseaseassociated variants mediate susceptibility to a large degree through chromatin interactions.…”

Evaluation of methods incorporating biological function and GWAS summary statistics to accelerate discovery

“…Traits with worse than expected performance even when thousands of cases are available include autism spectrum disorder 3 , heart failure 4,5 , major depressive disorder (MDD) 6,7 , and some addictions [8][9][10][11] . Increasing sample size to increase statistical power for discovery is not always practical, as encountered for rare diseases 12 , expensive phenotyping 13 , phenotypic heterogeneity 14 , hard-to-reach or socially disadvantaged populations 15 , and population isolates 16 . Our ability to discover trait-associated loci that are ancestryspecific or subject to gene-environment interaction lags in a field where the overwhelming majority of GWAS samples are of European ancestry 17 .…”

Multiple sclerosis risk variants influence the peripheral B‐cell compartment early in life in the general population

“…In addition, we imputed the genetic data using data from 1000 Genomes (Phase I, version 3) [ 20 ]. Quality control measures performed on the genetic data, a detailed description of the imputation method, and the calculation of principal components (PCs) have been published previously [ 9 ].…”

“…Recently, we have shown that children from a population‐based study with a high polygenic risk for MS show alterations in the white matter microstructure of the brain and in the T‐cell compartment of the immune system, which hints at early susceptibility factors for the disease process emerging in childhood [ 9 , 10 ]. The importance of B cells and the interaction between B and T cells in MS pathophysiology warrant additional investigation of the influence of genetic risk for MS on the B‐cell compartment and how this influences the distribution of naive and memory B cells during childhood [ 11 ].…”