No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS‐DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM‐MSS). The GeM‐MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM‐MSS correlates with MS‐DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression.

中文翻译：

---

MS 严重程度的遗传模型可预测未来残疾的累积


多发性硬化症 (MS) 严重程度的遗传修饰因素尚未经过独立验证，导致缺乏对残疾进展速度的遗传决定因素的深入了解。我们利用以下进展，在前瞻性训练 (N = 205) 和验证 (N = 94) 队列中研究了 MS 严重程度的遗传修饰因素： (1) 我们重点关注了先前确定与 MS 严重程度相关的 113 个遗传变异； (2) 我们使用了一种新颖、敏感的结果：MS 疾病严重程度量表 (MS-DSS)； (3) 我们没有验证单个等位基因，而是使用机器学习技术（随机森林）来捕获等位基因之间的线性和复杂的非线性效应，以得出 MS 严重程度的单一遗传模型 (GeM-MSS)。 GeM-MSS 由位于 12 个基因附近的 19 个变体组成，这些基因涉及调节免疫细胞的细胞毒性、补体激活、神经元功能和纤维化。在建模步骤中未使用的验证队列中，GeM-MSS 与 MS-DSS 相关（r = 0.214；p = 0.043）。公认的生物学确定了抑制多发性硬化症残疾进展的新治疗靶点。