A higher burden of multiple sclerosis genetic risk confers an earlier onset

Misicka, Elina; Davis, Mary F.; Kim, Woori; Brugger, Steven W.; Beales, Jeremy; Loomis, Stephanie; Bronson, Paola G.; Briggs, Farren

doi:10.1177/13524585211053155

Cited by 13 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found a weak inverse association between MS-GRS and age at MS onset (R 2 = 0.011, P < 0.0001, Supplementary Fig. 10 ), aligning with a recent study by Misicka et al (2022) reporting higher MS-GRS risk burden and younger age at MS diagnosis 41 . We did not find significant associations with additional clinical risk factors including BMI, smoking, or vitamin D insufficiency at UKBB study entry, which have been highlighted in other studies 22 – 25 , 28 , 29 .…”

Section: Discussionsupporting

confidence: 91%

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Loginovic,

Wang,

et al. 2024

Nat Commun

View full text Add to dashboard Cite

Optic neuritis (ON) is associated with numerous immune-mediated inflammatory diseases, but 50% patients are ultimately diagnosed with multiple sclerosis (MS). Differentiating MS-ON from non-MS-ON acutely is challenging but important; non-MS ON often requires urgent immunosuppression to preserve vision. Using data from the United Kingdom Biobank we showed that combining an MS-genetic risk score (GRS) with demographic risk factors (age, sex) significantly improved MS prediction in undifferentiated ON; one standard deviation of MS-GRS increased the Hazard of MS 1.3-fold (95% confidence interval 1.07–1.55, P < 0.01). Participants stratified into quartiles of predicted risk developed incident MS at rates varying from 4% (95%CI 0.5–7%, lowest risk quartile) to 41% (95%CI 33–49%, highest risk quartile). The model replicated across two cohorts (Geisinger, USA, and FinnGen, Finland). This study indicates that a combined model might enhance individual MS risk stratification, paving the way for precision-based ON treatment and earlier MS disease-modifying therapy.

show abstract

Section: Discussionsupporting

confidence: 91%

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Loginovic,

Wang,

et al. 2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…Beyond the established inverse relationship between HLA-DRB1*15:01 and AAO, there was little evidence to suggest a prominent role for other individual risk variants or variants recently associated with MS severity and longitudinal changes in brain volume and T2 lesion load. 4 , 15 , 23 An interesting observation was that the alleles associated with earlier AAO (major alleles for rs28672722, rs11755689, rs28359884; minor allele for rs37411) were also associated with increased risk for MS—this is consistent with our current understanding that an overall higher burden of MS genetic risk variants (whether it be HLA-DRB1*15:01 or a genetic/polygenic risk score) confers an earlier onset of MS. 4 , 10 The relationships between these 3 AAO variants and MS risk were not unexpected, considering the complexity of the MHC and that there are >30 independent risk alleles in the region. 23 Another intriguing observation was that LD proxies for rs28359884C, the major allele associated with earlier onset and increased MS risk, were also associated with higher MS severity.…”

Section: Discussionmentioning

confidence: 93%

“…This is supported by studies reporting an association between earlier MS onset and both HLA-DRB1*15:01 , the primary MS risk allele, and a higher genetic risk score of ∼200 nonmajor histocompatibility complex (MHC) MS risk variants. 10 - 12 There have been 2 large, published genome-wide association studies (GWAS) for MS AAO. The earlier study was incorporated into the later analysis of ∼465,000 single nucleotide polymorphisms (SNPs) in 9,772 persons with MS (PwMS) of European ancestry.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and Innate Immunity Are Key Drivers of Age at Onset of Multiple Sclerosis

Misicka,

Huang,

Loomis

et al. 2024

Neurol Genet

Self Cite

View full text Add to dashboard Cite

Background and Objectives Multiple sclerosis (MS) age at onset (AAO) is a clinical predictor of long-term disease outcomes, independent of disease duration. Little is known about the genetic and biological mechanisms underlying age of first symptoms. We conducted a genome-wide association study (GWAS) to investigate associations between individual genetic variation and the MS AAO phenotype. Methods The study population was comprised participants with MS in 6 clinical trials: ADVANCE (N = 655; relapsing-remitting [RR] MS), ASCEND (N = 555; secondary-progressive [SP] MS), DECIDE (N = 1,017; RRMS), OPERA1 (N = 581; RRMS), OPERA2 (N = 577; RRMS), and ORATORIO (N = 529; primary-progressive [PP] MS). Altogether, 3,905 persons with MS of European ancestry were analyzed. GWAS were conducted for MS AAO in each trial using linear additive models controlling for sex and 10 principal components. Resultant summary statistics across the 6 trials were then meta-analyzed, for a total of 8.3 × 10 −6 single nucleotide polymorphisms (SNPs) across all trials after quality control and filtering for heterogeneity. Gene-based tests of associations, pathway enrichment analyses, and Mendelian randomization analyses for select exposures were also performed. Results Four lead SNPs within 2 loci were identified ( p < 5 × 10 −8 ), including a) 3 SNPs in the major histocompatibility complex and their effects were independent of HLA-DRB1*15:01 and b) a LOC105375167 variant on chromosome 7. At the gene level, the top association was HLA-C ( p = 1.2 × 10 −7 ), which plays an important role in antiviral immunity. Functional annotation revealed the enrichment of pathways related to T-cell receptor signaling, autoimmunity, and the complement cascade. Mendelian randomization analyses suggested a link between both earlier age at puberty and shorter telomere length and earlier AAO, while there was no evidence for a role for either body mass index or vitamin D levels. Discussion Two genetic loci associated with MS AAO were identified, and functional annotation demonstrated an enrichment of genes involved in adaptive and complement immunity. There was also evidence supporting a link with age at puberty and telomere length. The findings suggest that AAO in MS is multifactorial, and the factors driving onset of symptoms overlap with those influencing MS risk.

show abstract

“…There was an enrichment of HLA gene annotations among the prioritized genomic variants in chromosome 6. The HLA variant HLA-DRB1*15:01, belonging to MHC class II genes, is the strongest genetic determinant of MS as defined in the literature (Menegatti et al 2021) (Caillier et al 2008) (Briggs and Sept 2021) (Misicka et al 2022). However, in our work, the most consistently prioritized genomic variant in MS was HLA-A*02:01 , which belongs to the MHC class I.…”

Section: Discussionmentioning

confidence: 99%

Machine learning methods applied to classify complex diseases using genomic data

Segura,

Bini,

Krithara

et al. 2024

Preprint

View full text Add to dashboard Cite

Complex diseases pose challenges in disease prediction due to their multifactorial and polygenic nature. In this work, we explored the prediction of two complex diseases, multiple sclerosis (MS) and Alzheimer's disease (AD), using machine learning (ML) methods and genomic data from UK Biobank. Different ML methods were applied, including logistic regressions (LR), gradient boosting decision trees (GB), extremely randomized trees (ET), random forest (RF), feedforward networks (FFN), and convolutional neural networks (CNN). The primary goal of this research was to investigate the variability of ML models in classifying complex diseases based on genomic risk. LR was the most robust method across folds and diseases, whereas deep learning methods (FFN and CNN) exhibited high variability. When comparing the performance of polygenic risk scores (PRS) with ML methods, PRS consistently performed at an average level. However, PRS still offers several practical advantages over ML methods. Despite implementing feature selection techniques to exclude non-informative and correlated predictors, the performance of ML models did not improve significantly, underscoring the ability of ML methods to achieve optimal performance even in the presence of correlated features due to linkage disequilibrium. Upon applying explainability tools to extract information about the genomic features contributing most to the classification task, the results confirmed the polygenicity of MS. The prevalence of HLA gene annotations among the top genomic features on chromosome 6 aligns with their significance in the context of MS. Overall, the highest-prioritized genomic variants were identified as expression or splicing quantitative trait loci (eQTL or sQTL) located in non-coding regions within or near genes associated with the immune response and MS. In summary, this research offers deeper insights into how ML models discern genomic patterns related to complex diseases.

show abstract

A higher burden of multiple sclerosis genetic risk confers an earlier onset

Cited by 13 publications

References 43 publications

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Applying a genetic risk score model to enhance prediction of future multiple sclerosis diagnosis at first presentation with optic neuritis

Adaptive and Innate Immunity Are Key Drivers of Age at Onset of Multiple Sclerosis

Machine learning methods applied to classify complex diseases using genomic data

Contact Info

Product

Resources

About