I like to think that we’re all more than some of our parts. But, our genetic code has us all fooled. Each and every cell has a genetic code that come to think of it probably has a lot more to say about our lives before, now and after than we’d truly like.
We don’t debate that genetics have a role to play in the development of cancer, but MS has always proven to have a complex disease model making it difficult to risk analyze within families with affected individuals.
The biggest know genetic risk factor for MS has always been the HLA-DRB1*15:01 gene with a reported odds ratio of 3.1 (see Figure below). This risk is additive depending on whether you have 0, 1 or 2 copies of the risk allele.
And then there is the HLA-A*02:01 effect, a largely an unknown in the MS story, with no or some protective effect in terms of MS severity. A recent study found that this variant conferred a 35-45% decreased risk of transitioning to secondary progressive MS (see abstract below). By changing the peptide-binding groove variations in the HLA-A2 gene may influence the nature of the peptides (such as viral proteins) that are bound and presented for CD8+ T-cell recognition.
Of course, we don’t understand the multiple interactions that occur with genetic variants or with environmental risk factors, but as we pursue more genetic based solutions for neurological disorders, your exact combination of allelic variants may well matter in the future.
J Neurol. 2020 Apr 24. doi: 10.1007/s00415-020-09850-z. [Epub ahead of print]
Predicting onset of secondary-progressive multiple sclerosis using genetic and non-genetic factors.
Predicting the transition from relapsing-remitting (RR) to secondary-progressive (SP) multiple sclerosis (MS) from early in the disease course is challenging.
To construct prediction models for SPMS using sociodemographic and self-reported clinical measures that would be available at/near MS onset, with specific considerations for MS genetic risk factors.
We conducted a retrospective cross-sectional study based on 1295 white, non-Hispanic individuals. Cox proportional hazard prediction models were generated for three censored SPMS outcomes (ever transitioning, transitioning within 10 years, and transitioning within 20 years) using sociodemographic, comorbid health information, symptomatology, and other measures of early disease activity. HLADRB1*15:01 and HLA-A*02:01, as well as a genetic risk score, were iteratively considered in each model. We also explored the relationships for all 200 MS risk variants located outside the major histocompatibility complex. Nomograms were generated for the final prediction models.
An older age of MS onset and being male predicted a short latency to SPMS, while a longer interval between the first two relapses predicted a much longer latency. Comorbid conditions and onset symptomatology variably predicted the risk for transitioning to SPMS for each censored outcome. The most notable observation was that HLA-A*02:01, which confers decreased risk for MS, also contributed to decreased hazards for SPMS.
These results have the potential to advance prognostication for a person with MS using information available at or near onset, potentially improving care and quality of life for those who live with MS.