Not all roads lead to the immune system: The genetic basis of multiple sclerosis severity

Multiple sclerosis is a leading cause of neurological disability in
adults. Heterogeneity in multiple sclerosis clinical presentation has
posed a major challenge for identifying genetic variants associated with
disease outcomes. To overcome this challenge, we used prospectively
ascertained clinical outcomes data from the largest international multiple
sclerosis Registry, MSBase. We assembled a cohort of deeply phenotyped
individuals of European ancestry with relapse-onset multiple sclerosis. We
used unbiased genome-wide association study and machine learning
approaches to assess the genetic contribution to longitudinally defined
multiple sclerosis severity phenotypes in 1,813 individuals. Our primary
analyses did not identify any genetic variants of moderate to large effect
sizes that met genome-wide significance thresholds. The strongest signal
was associated with rs7289446 (β=-0.4882, P = 2.73 × 10−7), intronic to
SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in
relapse-onset multiple sclerosis are associated with multiple genetic loci
of small effect sizes. Using a machine learning approach incorporating
over 62,000 variants together with clinical and demographic variables
available at multiple sclerosis disease onset, we could predict severity
with an area under the receiver operator curve of 0.84 (95% CI 0.79–0.88).
Our machine learning algorithm achieved positive predictive value for
outcome assignation of 80% and negative predictive value of 88%. This
outperformed our machine learning algorithm that contained clinical and
demographic variables alone (area under the receiver operator curve 0.54,
95% CI 0.48–0.60). Secondary, sex-stratified analyses identified two
genetic loci that met genome-wide significance thresholds. One in females
(rs10967273; βfemale =0.8289, P = 3.52 × 10-8), the other in males
(rs698805; βmale =  -1.5395, P = 4.35 × 10-8), providing some evidence for
sex dimorphism in multiple sclerosis severity. Tissue enrichment and
pathway analyses identified an overrepresentation of genes expressed in
central nervous system compartments generally, and specifically in the
cerebellum (P = 0.023). These involved mitochondrial function, synaptic
plasticity, oligodendroglial biology, cellular senescence, calcium and
g-protein receptor signalling pathways. We further identified six variants
with strong evidence for regulating clinical outcomes, the strongest
signal again intronic to SEZ6L (adjusted hazard ratio 0.72,
P = 4.85 × 10-4). Here we report a milestone in our progress towards
understanding the clinical heterogeneity of multiple sclerosis outcomes,
implicating functionally distinct mechanisms to multiple sclerosis risk.
Importantly, we demonstrate that machine learning using common single
nucleotide variant clusters, together with clinical variables readily
available at diagnosis can improve prognostic capabilities at diagnosis,
and with further validation has the potential to translate to meaningful
clinical practice change.