Research Article

Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

See allHide authors and affiliations

Science  27 Sep 2019:
Vol. 365, Issue 6460, eaav7188
DOI: 10.1126/science.aav7188
  • The MS genetic map implicates microglia as well as multiple different peripheral immune cell populations in the onset of the disease.

    We list some of the immune cells in which we found an excess of MS susceptibility genes, implicating these cells as contributing to the earliest events that trigger MS. The sample size of our genome-wide association study is listed along with a circus plot illustrating main results.

  • Fig. 1 The genetic map of multiple sclerosis.

    The circos plot displays the 4842 prioritized autosomal non-MHC effects and the associations in chromosome X. Joint analysis (discovery and replication) P values are plotted as lines (fixed-effects inverse-variance meta-analysis). The green inner layer displays genome-wide significance (P < 5 × 10−8), the blue inner layer displays suggestive P values (1 × 10−5 < P >5 × 10−8), and the gray layer displays P values > 1 × 10−5. Each line in the inner layers represents one effect. Two hundred autosomal non-MHC and one in chromosome X genome-wide effects are listed. The vertical lines in the inner layers represent one effect, and the respective color displays the replication status (supplementary materials, materials and methods): green (genome-wide), blue (suggestive), and red (nonreplicated). Plotted on the outer surface are 551 prioritized genes. The inner circle space includes PPIs among genome-wide genes (green) and between genome-wide genes and suggestive genes (blue) that are identified as candidates by using PPI networks (9).

  • Fig. 2 Multiple independent effects in the EVI5 locus and chromosome X associations.

    (A) Regional association plot of the EVI5 locus. Discovery P values (fixed-effects inverse-variance meta-analysis) are displayed. The layer tagged “Step 0” plots the associations of the marginal analysis, with the most statistically significant SNP being rs11809700 (ORT = 1.16; P = 3.51 × 10−15). The “Step 1” plots the associations conditioning on rs11809700; rs12133753 is the most statistically significant SNP (ORC = 1.14; P = 8.53 × 10−09). “Step 2” plots the results conditioning on rs11809700 and rs12133753, with rs1415069 displaying the lowest P value (ORG = 1.10; P = 4.01 × 10−5). Last, “Step 3” plots the associations conditioning on rs11809700, rs12133753, and rs1415069, identifying rs58394161 as the most statistically significant SNP (ORC = 1.10; P = 8.63 × 10−4). All four SNPs reached genome-wide significance in the respective joint (discovery plus replication) analyses (table S7). Each of the four independent SNPs—lead SNPs—are highlighted by use of a triangle in the respective layer. (B) Regional association plot for the genome-wide chromosome X variant. Joint analysis P values (fixed-effects inverse-variance meta-analysis) are displayed. Linkage disequilibrium, in terms of r2 based on the 1000 Genomes European panel, is indicated by use of a combination of color grade and symbol size. All positions are in human genome 19.

  • Fig. 3 Independent associations in the major histocompatibility locus.

    Regional association plot in the MHC locus. Only genome-wide statistically independent effects are listed. The order of variants in the x axis represents the order that these were identified. The size of the circle represents different values of –log10(P value) (fixed-effects inverse-variance meta-analysis). Different colors are used to depict class I, II, III, and non-HLA effects. y axis displays million base pairs.

  • Fig. 4 Heritability partitioning.

    Proportion of the overall narrow-sense heritability under the liability model (~19.2%) explained with different genetic components. (A) The overall heritability is partitioned in the SE MHC, the 1962 regions that include all SNPs with P <0.05 (Regions; fixed-effects inverse-variance meta-analysis), and the rest of genome with P >0.05 (Nonassociated regions). (B) The Regions are further partitioned to the seemingly statistically independent effects (Prioritized) and the residual effects (Nonprioritized). (C) The Prioritized component is partitioned on the basis of the replication knowledge to genome-wide effects (GW), suggestive (S), nonreplicated (ND), and no data (ND). The lines connecting the pie charts depict the component that is partitioned. All values were estimated by using the discovery data sets (n = 4802 cases and 26,703 controls).

  • Fig. 5 Tissue- and cell-type enrichment analyses.

    (A) Gene Atlas tissues and cell types gene expression enrichment. (B) DHS enrichment for tissues and cell types from the NIH Epigenetic Roadmap. Rows are sorted from immune cells or tissues to CNS-related ones. Both x axes display –log10 of Benjamini and Hochberg P values (FDR). The vertical black line highlights the threshold of significance for the enrichment analysis.

  • Fig. 6 Dissection of cortical RNA-seq data.

    (A) A heatmap of the results of our analysis assessing whether a cortical eQTL is likely to come from one of the component cell types of the cortex: neurons, oligodendrocytes, endothelial cells, microglia, and astrocytes (in rows). Each column presents results for one of the MS brain eQTLs. The color scheme relates to the P value of the interaction term (linear regression), with red denoting a more extreme result. (B) The same results in a different form, comparing results of assessing for interaction with neuronal proportion (y axis) and microglial proportion (x axis). The SLC12A5 eQTL is significantly stronger when accounting for neuronal proportion, and CLECL1 is significantly stronger when accounting for microglia. The Bonferroni-corrected threshold of significance is highlighted by the dashed line. (C) Locus view of the SLC12A5/CD40 locus, illustrating the distribution of MS susceptibility and the SLC12A5 brain eQTL in a segment of chromosome 20 (x axis); the y axis presents the P value of association with MS susceptibility (top; fixed effects inverse-variance meta-analysis) or SLC12A5 RNA expression (bottom; linear regression). The lead MS SNP is denoted by a triangle; other SNPs are circles, with the intensity of the red color denoting the strength of LD with the lead MS SNP. (D) Plot of the level of expression, transcriptome-wide, for each measured gene in our cortical RNA-seq dataset (n = 455) (y axis) and purified human microglia (n = 10) (x axis) from the same cortical region. In blue, we highlight those genes with greater than fourfold increased expression in microglia relative to bulk cortical tissue and are expressed at a reasonable level in microglia. Each dot is one gene. Gray dots denote the 551 putative MS genes from our integrated analysis. SLC12A5 and CLECL1 are highlighted in red; in blue, we highlight a selected subset of the MS genes—many of them well-validated—which are enriched in microglia. For clarity, we did not include all of the MS genes that fall in this category.

Supplementary Materials

Stay Connected to Science

Navigate This Article