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Impact of MS genetic loci on familial aggregation, clinical phenotype, and disease prediction.

Esposito F, Guaschino C, Sorosina M, Clarelli F, Ferre' L, Mascia E, Santoro S, Pagnesi M, Radaelli M, Colombo B, Moiola L, Rodegher M, Stupka E, Martinelli V, Comi G, Martinelli Boneschi F - Neurol Neuroimmunol Neuroinflamm (2015)

Bottom Line: Among them, 461 sporadic and 93 familial probands were genotyped for 107 MS-associated polymorphisms.Their effect sizes were combined to calculate the weighted genetic risk score (wGRS).Additional variants outside the known MS-associated loci, rare variants, and/or environmental factors may explain disease occurrence within families; in females, hormonal and epigenetic factors probably have a predominant role in explaining familial aggregation.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology (F.E., C.G., L.F., M.P., M. Radaelli, B.C., L.M., M. Rodegher, V.M., G.C., F.M.B.) and Laboratory of Genetics of Neurological Complex Disorders (F.E., C.G., M.S., F.C., L.F., E.M., S.S., M.P., G.C., F.M.B.), Institute of Experimental Neurology, Division of Neuroscience, and Center for Translational Genomics and Bioinformatics (E.S.), San Raffaele Scientific Institute, Milan, Italy.

ABSTRACT

Objective: To investigate the role of known multiple sclerosis (MS)-associated genetic variants in MS familial aggregation, clinical expression, and accuracy of disease prediction in sporadic and familial cases.

Methods: A total of 1,443 consecutive patients were screened for MS and familial autoimmune history in a hospital-based Italian cohort. Among them, 461 sporadic and 93 familial probands were genotyped for 107 MS-associated polymorphisms. Their effect sizes were combined to calculate the weighted genetic risk score (wGRS).

Results: Family history of MS was reported by 17.2% of probands, and 33.8% reported a familial autoimmune disorder, with autoimmune thyroiditis and psoriasis being the most frequent. No difference in wGRS was observed between sporadic and familial MS cases. In contrast, a lower wGRS was observed in probands with greater familial aggregation (>1 first-degree relative or >2 relatives with MS) (p = 0.03). Also, female probands of familial cases with greater familial aggregation had a lower wGRS than sporadic cases (p = 0.0009) and male probands of familial cases (p = 0.04). An inverse correlation between wGRS and age at onset was observed (p = 0.05). The predictive performance of the genetic model including all known MS variants was modest but greater in sporadic vs familial cases (area under the curve = 0.63 and 0.57).

Conclusions: Additional variants outside the known MS-associated loci, rare variants, and/or environmental factors may explain disease occurrence within families; in females, hormonal and epigenetic factors probably have a predominant role in explaining familial aggregation. The inclusion of these additional factors in future versions of aggregated genetic measures could improve their predictive ability.

No MeSH data available.


Related in: MedlinePlus

Predictive performance of wGRSThe receiver operating characteristic curves are plotted considering 3 different models: weighted genetic risk score (wGRS) calculated with only with HLA-DRB1 alleles (green line), wGRS non-HLA (blue line), and wGRS calculated including all the 106 known genetic variants plus HLA allele (red line). The area under the curve (AUC) was calculated for the 3 different models, assessing the predictive capability in the entire multiple sclerosis (MS) cohort (A), in the subgroup of patients with sporadic MS (B), and in the subgroup of patients with familial MS (C). HLA = human leukocyte antigen; SNP = single nucleotide polymorphism.
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Figure 3: Predictive performance of wGRSThe receiver operating characteristic curves are plotted considering 3 different models: weighted genetic risk score (wGRS) calculated with only with HLA-DRB1 alleles (green line), wGRS non-HLA (blue line), and wGRS calculated including all the 106 known genetic variants plus HLA allele (red line). The area under the curve (AUC) was calculated for the 3 different models, assessing the predictive capability in the entire multiple sclerosis (MS) cohort (A), in the subgroup of patients with sporadic MS (B), and in the subgroup of patients with familial MS (C). HLA = human leukocyte antigen; SNP = single nucleotide polymorphism.

Mentions: Finally, we assessed the predictive performance of different genetic models using ROC curves. The wGRS score calculated based on only the HLA-DRB1*1501 variant allele had a very limited capacity to discriminate between patients with MS and HC (AUC = 0.55, 95% confidence interval [CI] 0.52–0.58, figure 3A). The AUC increased to 0.59 (95% CI 0.55–0.63) and to 0.61 (95% CI 0.57–0.65) when including wGRS–non-HLA and HLA alleles for building the model, respectively (figure 3A). The difference in predictive performance was significant between the model considering only HLA-DRB1 and the one considering HLA-DRB1*1501 plus 106 SNPs (p = 0.0008). We then evaluated the predictive capability of the different genetic models separately in sMS (figure 3B) and fMS (figure 3C). We observed that the full model (HLA-DRB1*1501 plus 106 genetic loci) was better at discriminating patients with sMS from HC (AUC = 0.63, 95% CI 0.58–0.67, figure 3B) than discriminating patients with fMS from HC (AUC = 0.57, 95% CI 0.50–0.64, figure 3C); however, the difference in the predictive capability was not statistically significant (p = 0.13). Moreover, in the sMS subgroup, the full model had a statistically better predictive performance than the model considering only HLA-DRB1 (p = 0.00006). Finally, no difference in predictive capability was found in female probands compared with male probands (AUC = 0.62, 95% CI 0.55–0.68 vs AUC = 0.60, 95% CI 0.54-0.66; p = 0.65).


Impact of MS genetic loci on familial aggregation, clinical phenotype, and disease prediction.

Esposito F, Guaschino C, Sorosina M, Clarelli F, Ferre' L, Mascia E, Santoro S, Pagnesi M, Radaelli M, Colombo B, Moiola L, Rodegher M, Stupka E, Martinelli V, Comi G, Martinelli Boneschi F - Neurol Neuroimmunol Neuroinflamm (2015)

Predictive performance of wGRSThe receiver operating characteristic curves are plotted considering 3 different models: weighted genetic risk score (wGRS) calculated with only with HLA-DRB1 alleles (green line), wGRS non-HLA (blue line), and wGRS calculated including all the 106 known genetic variants plus HLA allele (red line). The area under the curve (AUC) was calculated for the 3 different models, assessing the predictive capability in the entire multiple sclerosis (MS) cohort (A), in the subgroup of patients with sporadic MS (B), and in the subgroup of patients with familial MS (C). HLA = human leukocyte antigen; SNP = single nucleotide polymorphism.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4503410&req=5

Figure 3: Predictive performance of wGRSThe receiver operating characteristic curves are plotted considering 3 different models: weighted genetic risk score (wGRS) calculated with only with HLA-DRB1 alleles (green line), wGRS non-HLA (blue line), and wGRS calculated including all the 106 known genetic variants plus HLA allele (red line). The area under the curve (AUC) was calculated for the 3 different models, assessing the predictive capability in the entire multiple sclerosis (MS) cohort (A), in the subgroup of patients with sporadic MS (B), and in the subgroup of patients with familial MS (C). HLA = human leukocyte antigen; SNP = single nucleotide polymorphism.
Mentions: Finally, we assessed the predictive performance of different genetic models using ROC curves. The wGRS score calculated based on only the HLA-DRB1*1501 variant allele had a very limited capacity to discriminate between patients with MS and HC (AUC = 0.55, 95% confidence interval [CI] 0.52–0.58, figure 3A). The AUC increased to 0.59 (95% CI 0.55–0.63) and to 0.61 (95% CI 0.57–0.65) when including wGRS–non-HLA and HLA alleles for building the model, respectively (figure 3A). The difference in predictive performance was significant between the model considering only HLA-DRB1 and the one considering HLA-DRB1*1501 plus 106 SNPs (p = 0.0008). We then evaluated the predictive capability of the different genetic models separately in sMS (figure 3B) and fMS (figure 3C). We observed that the full model (HLA-DRB1*1501 plus 106 genetic loci) was better at discriminating patients with sMS from HC (AUC = 0.63, 95% CI 0.58–0.67, figure 3B) than discriminating patients with fMS from HC (AUC = 0.57, 95% CI 0.50–0.64, figure 3C); however, the difference in the predictive capability was not statistically significant (p = 0.13). Moreover, in the sMS subgroup, the full model had a statistically better predictive performance than the model considering only HLA-DRB1 (p = 0.00006). Finally, no difference in predictive capability was found in female probands compared with male probands (AUC = 0.62, 95% CI 0.55–0.68 vs AUC = 0.60, 95% CI 0.54-0.66; p = 0.65).

Bottom Line: Among them, 461 sporadic and 93 familial probands were genotyped for 107 MS-associated polymorphisms.Their effect sizes were combined to calculate the weighted genetic risk score (wGRS).Additional variants outside the known MS-associated loci, rare variants, and/or environmental factors may explain disease occurrence within families; in females, hormonal and epigenetic factors probably have a predominant role in explaining familial aggregation.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology (F.E., C.G., L.F., M.P., M. Radaelli, B.C., L.M., M. Rodegher, V.M., G.C., F.M.B.) and Laboratory of Genetics of Neurological Complex Disorders (F.E., C.G., M.S., F.C., L.F., E.M., S.S., M.P., G.C., F.M.B.), Institute of Experimental Neurology, Division of Neuroscience, and Center for Translational Genomics and Bioinformatics (E.S.), San Raffaele Scientific Institute, Milan, Italy.

ABSTRACT

Objective: To investigate the role of known multiple sclerosis (MS)-associated genetic variants in MS familial aggregation, clinical expression, and accuracy of disease prediction in sporadic and familial cases.

Methods: A total of 1,443 consecutive patients were screened for MS and familial autoimmune history in a hospital-based Italian cohort. Among them, 461 sporadic and 93 familial probands were genotyped for 107 MS-associated polymorphisms. Their effect sizes were combined to calculate the weighted genetic risk score (wGRS).

Results: Family history of MS was reported by 17.2% of probands, and 33.8% reported a familial autoimmune disorder, with autoimmune thyroiditis and psoriasis being the most frequent. No difference in wGRS was observed between sporadic and familial MS cases. In contrast, a lower wGRS was observed in probands with greater familial aggregation (>1 first-degree relative or >2 relatives with MS) (p = 0.03). Also, female probands of familial cases with greater familial aggregation had a lower wGRS than sporadic cases (p = 0.0009) and male probands of familial cases (p = 0.04). An inverse correlation between wGRS and age at onset was observed (p = 0.05). The predictive performance of the genetic model including all known MS variants was modest but greater in sporadic vs familial cases (area under the curve = 0.63 and 0.57).

Conclusions: Additional variants outside the known MS-associated loci, rare variants, and/or environmental factors may explain disease occurrence within families; in females, hormonal and epigenetic factors probably have a predominant role in explaining familial aggregation. The inclusion of these additional factors in future versions of aggregated genetic measures could improve their predictive ability.

No MeSH data available.


Related in: MedlinePlus