Clinical presentation

The disease is known for its highly heterogeneous clinical onset and progression, which are mostly unpredictable [6]. The International Advisory Committee on Clinical Trials in Multiple Sclerosis categorized the clinical course descriptors, also known as Lublin-Reingold descriptors, into four main categories [7, 8]. The relapsing-remitting MS (RRMS) is characterized by distinct acute episodes (relapses) separated in time with dissemination in space (i.e. lesions in more than one region of the CNS), followed by recovery [9, 10]. However, partial or complete recovery may become less evident as relapses persist over time, often progressing to secondary progressive MS (SPMS) [7]. The SPMS describes the transition from a previously relapsing-remitting disease to a progressively disabling course, with or without relapses [8, 9]. The primary progressive MS (PPMS) is marked by continuous worsening of neurological disability from disease onset, without relapses or remission phases [7, 10]. The progressive relapsing MS (PRMS) is rarer and presents with steady deterioration from the beginning of the disease, occasionally accompanied by relapses [7].

Diagnosis

Diagnosis typically occurs between the ages of 20 and 50, with 85% of cases initially presenting as RRMS and 15% as PPMS [7]. The diagnosis is primarily clinical, based on the assessment of symptoms, neurological examination, and additional investigations such as magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) analysis [4, 9]. The degree of patient disability should also be evaluated using a standardized scale. The Expanded Disability Status Scale (EDSS) assesses eight functional systems: visual, brainstem, pyramidal, cerebellar, sensory, bladder, bowel, and cerebral functions. The EDSS ranges from 0 (no disability) to 10 (death due to MS) [11].

Pathogenesis

According to the literature, the core of the disease’s pathogenesis lies in the imbalance between autoimmunity and self-tolerance [12]. MS is defined by the presence and activity of autoreactive T cells that escape clonal deletion and are peripherally activated by the presentation of myelin antigens by antigen-presenting cells (APCs) [12–14]. This process may occur through molecular mimicry between a microbial antigen and the myelin autoantigen [12]. After crossing the blood-brain barrier (BBB), microglia present antigens to T cells through classical major histocompatibility complex (MHC) class I and II pathways [3]. In the MHC class II pathway, microglia and other APCs present peptides from extracellular proteins to CD4+ T cells [13]. These cells secrete specific cytokines and differentiate into various T helper (Th) cell subsets. Th1 cells release pro-inflammatory cytokines such as interleukin-2 (IL-2), tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ), while Th17 cells secrete IL-17, IL-21, and IL-22. In contrast, the MHC class I pathway presents peptides derived from endogenously synthesized proteins to CD8+ T cells, which can recognize and kill infected or abnormal cells [3]. Altogether, these processes lead to increased acute neuroinflammation in the CNS, resulting in demyelination, axonal damage, and progressive disease worsening [13].

Treatment

The treatment of MS has evolved significantly over the past decades, focusing primarily on modulating inflammatory activity, preventing disability progression, and improving patients’ quality of life. Currently, MS management involves relapse treatment, disease-modifying therapy (DMT), and functional and symptomatic rehabilitation. DMTs represent the cornerstone of MS treatment, particularly in RRMS, as they primarily act by reducing inflammatory activity and, consequently, the frequency of relapses, although they do not effectively prevent the underlying neurodegeneration driving disease progression. This therapy acts through complementary immunomodulatory mechanisms; for example, IFN-β and glatiramer acetate dampen pro-inflammatory T-cell activity. In SPMS, treatment options remain limited; however, recent advances have shown that simvastatin may reduce brain atrophy, and ongoing clinical trials are evaluating biotin, laquinimod, and ocrelizumab for this disease form. In PPMS, therapeutic resources are also scarce, with the main goal being to slow functional decline, since inflammation is less prominent. For the treatment of acute relapses, methylprednisolone remains the drug of choice. In cases of progressive MS that is refractory to conventional DMTs, autologous hematopoietic stem cell transplantation has emerged as an effective alternative [1, 4, 15].

Genome wide association studies

Genome wide association studies (GWAS) test common genetic variants across the entire genome, typically in large cohorts of individuals with and without the trait, to identify associations between specific genetic markers [e.g., single nucleotide polymorphisms (SNPs)] and that trait [16]. Until 2007, when the first GWAS [17] was published, the only confirmed genes associated with MS were human leukocyte antigen (HLA)-DR15, -DQw6, and the class I HLA alleles A3 and B7 [16]. Since then, the International Multiple Sclerosis Genetics Consortium (IMSGC) has conducted several GWAS with progressively larger cohorts; in 2019, the study included 47,429 MS patients and 68,374 controls [18]. GWAS have successfully identified multiple genetic loci associated with MS by scanning the genotypes of large population samples, and have detected more than 200 SNPs associated with an increased risk of developing the disease [19]. However, due to linkage disequilibrium, SNPs located near risk variants also tend to be inherited together, amplifying the genetic association with MS [16].

HLA

In the 1970s, the first genetic factor associated with MS was identified: the MHC region, known as the HLA [16]. It was recognized as a key genetic determinant in autoimmune diseases, including MS, accounting for approximately 10.5% of the genetic variance underlying MS susceptibility [20, 21]. The HLA locus comprises a highly complex network of 224 functional genes, densely clustered and interconnected, both anatomically and biochemically [13]. The molecules encoded in this region are highly polymorphic cell surface glycoproteins, essential for the adaptive immune response and for antigen presentation to T cell recognition [22]. HLA genes are located across a 3.6 Mb segment on the short arm of chromosome 6 (6p21) [16].

There are three class I HLA proteins: HLA-A, -B, and -C, encoded by their respective genes. These proteins consist of three domains (α1, α2, and α3) and form membrane-bound heterodimers together with the invariant β2-microglobulin. Antigenic peptides bind to the α1 and α2 domains and are recognized by CD8+ T cell receptors (TCRs). The class I HLA structure allows for the binding of peptides nine to ten amino acids long, and these molecules are expressed on all nucleated cells and platelets, with the exception of intact neurons, the cornea, sperm cells, and some trophoblastic cells in immunoprivileged sites [16].

A second set of HLA genes encodes class II HLA molecules: HLA-DR, -DQ, and -DP. These are composed of two different protein chains, α and β, which pair to form the functional class II molecules. The α1 and β1 domains of these chains bind peptides that are recognized by CD4+ TCRs. While class II HLA molecules typically bind peptides of nine to ten residues, they can present peptides up to 25 amino acids, as their peptide-binding groove is open at both ends, unlike class I molecules. Class II HLA expression is typically restricted to APCs, but under inflammatory conditions, it can also occur in other cell types, such as thyroid epithelial cells, astrocytes, and oligodendrocytes [16].

HLA-DQ and HLA-DP molecules are composed of polymorphic α and β chains, encoded by HLA-DQA1, -DPA1, -DQB1, and -DPB1 genes. HLA-DR molecules consist of a non-polymorphic α-chain (DRα), encoded by HLA-DRA, and a highly polymorphic β-chain (DRβ), which may be encoded by four different genes: HLA-DRB1, -DRB3, -DRB4, and -DRB5 [16]. Many polymorphisms in the DRB chain have been associated with increased susceptibility to specific diseases [13].

Class II HLA expression is essential for maintaining resting immune cell populations and for regulating immune tolerance [13, 23]. Furthermore, HLA class II molecules are strongly associated with autoimmunity due to TCR remodeling influenced by genetic and environmental factors. Their structure facilitates the spontaneous presentation of self-antigens, triggering a signaling cascade that can lead to persistent autoreactivity [13, 23]. Genetic mutations in HLA genes or the presence of certain specific alleles can significantly influence diseases such as fibromyalgia, Alzheimer’s disease, infectious diseases like COVID-19, and autoimmune diseases such as MS [13].

Global heterogeneity

MS exhibits remarkable global heterogeneity, both in its prevalence and in the genetic factors that influence disease susceptibility [4, 5]. Its geographic distribution reflects a complex interaction between genetic polymorphisms, particularly in the HLA region, and environmental factors [20, 21]. Studies have shown that the contribution of HLA alleles to MS risk varies significantly across different populations. For example, alleles such as HLA-DRB1*15 and HLA-DQB1*06:02 increase disease risk in multiple ethnic groups, although with varying magnitudes [6]. Similarly, alleles considered protective in some populations may not exert the same effect in others, highlighting the importance of genetic variation in understanding the disease. The investigation and identification of polymorphisms associated with increased MS risk may help uncover the biological mechanisms underlying disease progression, as well as predict treatment response [4, 6]. In this context, the present systematic review aims to examine HLA-associated polymorphisms with high expression in MS across different populations, deepening our understanding of the genetic influence on the disease’s heterogeneity.

Literature search

To guide the relevant research for this systematic review, the following keywords were defined: “Multiple Sclerosis”, “Genetic Polymorphisms”, “SNPs”, and “Human Leukocyte Antigen”, using the PubMed and Web of Science databases. Additionally, the terms “Europe”, “America”, “Africa”, and “Population” were used to maximize the inclusion of studies involving diverse population groups. The details of the search are described in Table 1.

Inclusion and exclusion criteria

Inclusion criteria were established, such as original research studies, randomized clinical trials, and meta-analyses involving human subjects, written in Portuguese or English, with full and free access, and published between 2019 and 2025.

For the exclusion criteria, articles with unrelated titles, abstracts, or content, as well as reviews, systematic reviews, and duplicate articles from different databases were excluded from this selection.

Instruments and data collection

All articles retrieved from the search were organized using reference management software (Mendeley Desktop 1.19.8). After the removal of duplicates, the articles were screened by title and abstract based on the established criteria. The screened articles were then analyzed in full text, and only those with applicable information were selected. The PRISMA flowchart outlines the selection process (Figure 1).

Display full size

Figure 1. PRISMA flowchart with the description of the selected articles. PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses. Adapted from https://www.prisma-statement.org/prisma-2020-flow-diagram. Accessed May 5, 2025. Distributed under a Creative Commons Attribution (CC BY 4.0) license.

Quality analysis

All studies included in this article were categorized as non-randomized; therefore, the risk of bias was assessed using the ROBINS-I tool, considering bias due to confounding (D1), selection of participants (D2), classification of interventions (D3), deviations from intended interventions (D4), missing data (D5), measurement of outcomes (D6), and selection of the reported results (D7) [25, 26]. Of the 21 articles evaluated, the study “Distribution of Major HLA-A, -B, -DR, and -DQ Loci Potentially Associated with Multiple Sclerosis in a Healthy Population from Southern Morocco” was judged at serious risk of bias in outcome measurement because, in the context of this review, it did not directly assess MS cases or HLA-MS associations, but only reported HLA frequencies in healthy individuals, providing indirect evidence for the outcome of interest. The results of the risk of bias assessment are summarized in Figure 2 and explained in Figure S1.

Display full size

Figure 2. Risk of bias assessed using the ROBINS-I tool.

Europe

Chi et al. [28], Beecham et al. [31], and Briggs and Sept [35] conducted studies involving European Caucasian individuals, all of which demonstrated that the presence of the HLA-DRB1*15:01 allele was significantly associated with MS susceptibility (Table 3). Additional risk alleles such as HLA-DQB1*06:02 and HLA-DPB1*03:01 were also identified (Table 3) [31].

Sweden, which has one of the world’s highest MS prevalence rates, showed enrichment of three genotypes containing the high-risk haplotype HLA-DRB5*01:01:01~DRB1*15:01:01~DQA1*01:02:01~DQB1*06:02:01; alleles such as DQA1*01:01:02 (OR = 9.21; p = 2.18 × 10^–4), DQA1*01:02:01 (OR = 1.85; p = 8.15 × 10^–6), and DQB1*05:02:01 (OR = 4.36; p = 1.71 × 10^–5) were significantly overrepresented in patients [34]. In a case-control study by Hedström et al. [36], the DRB1*15:01 allele additively increased the risk of developing MS. Compared to individuals without this allele, the adjusted OR was 3.7 (95% CI: 3.3–4.1) for heterozygotes and 7.8 (95% CI: 6.4–9.5) for homozygotes [36].

The Orkney Islands in Northern Scotland have the highest reported MS prevalence worldwide (402/100,000). In this population, analysis of 127 SNPs identified rs9271069, a tag for HLA-DRB1*15:01, as the strongest risk variant (OR = 2.77), with higher frequency on the islands than on the mainland; however, given the small populations of Orkney (22,000) and Shetland (23,000), these allele-frequency differences explain only about two excess MS cases per archipelago [37].

Gontika et al. [38] examined HLA-DRB1 alleles in pediatric-onset MS (POMS) and adult-onset MS (AOMS), finding DRB1*15 and DRB1*03 (Table 3) as the most frequent genotypes in POMS and a significantly lower frequency of DRB1*11 in patients (OR = 0.09; p = 3.3 × 10^–2), suggesting a protective effect; in AOMS, DRB1*15 was even more frequent (31.9%) but did not reach statistical significance.

In Belgium, Derdelinckx et al. [39] reported that 40% of patients, versus about 10% of controls, carried the HLA-DR15 haplotype (DRB1*15:01~DQA1*01:02~DQB1*06:02), but CD4+ T-cell responses to myelin peptides did not differ between carriers and non-carriers, suggesting that this haplotype contributes to susceptibility but does not directly shape peripheral myelin-specific reactivity.

Africa

The DRB1*15:01 allele, associated with an increased MS risk (OR up to 3) in Caucasian populations, is relatively rare in African populations, where the DRB1*15:03 allele predominates. This allele is also associated with MS in admixed populations in Latin America and the Middle East [40].

In a cohort of healthy individuals studied by Fguirouche et al. [29], no statistically significant differences were observed between class I HLA alleles and MS susceptibility. Regarding class II HLA, the alleles considered predisposing to MS had the following frequencies: DRB1*03 at 19.2%, DRB1*15 at 13.3%, DRB1*13 at 15.8%, and DRB1*04 at 12.7% [29].

America

Mack et al. [41] and Creary et al. [42] conducted studies in American cohorts of European ancestry, and both analyses showed that HLA-DRB1*15:01 conferred a very high disease risk (Table 3). Additionally, the haplotypes DRB1*15:01~DQB1*06:02 (OR = 3.98; p = 2.22 × 10^–16), DRB1*03:01~DQB1*02:01 (OR = 1.63; p = 1.41 × 10^–3), C*07:02~B*07:02 (OR = 1.99; p = 8.8 × 10^–7), and the A*30:02 allele (OR = 3.32; p = 1.37 × 10^–2) were also associated with disease susceptibility [41, 42].

In an African American cohort, Beecham et al. [31] identified the HLA-DRB1*15:01 allele (Table 3) and the HLA-A*02:01 allele (additive protection; OR = 0.68; p = 5.07 × 10^–5). Conserved extended haplotype analyses showed a strong association for the European DRB1*15:01~DQB1*06:02 haplotype (Table 3) and variable effects for the African DRB1*15:03~DQB1*06:02 [32].

In Brazil, the extended DR15 haplotype was frequent and correlated with European ancestry; DQB1*06:02 and DRB1*15:01 were enriched among cases [43].

Asia

Iranian studies led to mixed SNP associations [27, 44]. In Jordan, DRB1*03:01 and DRB1*04:01 increased risk (Table 3), whereas DRB1*04:03 and DQB1*06:03 were protective [30].

In Japan, the most significant association was with the HLA-DRB1*15:01 allele (Table 3). In addition, the alleles HLA-DQB1*06:02 (Table 3), HLA-B*15:01 (OR = 2.95; p = 2.2 × 10^–4), DRB1*04:05 (OR = 2.00; p < 1 × 10^–3), and DPB1*03:01 (Table 3) were associated with disease susceptibility. On the other hand, the alleles DRB1*09:01 (OR = 0.47; p < 1 × 10^–3), DRB1*01:01 (OR = 0.38; p < 1 × 10^–3), DRB1*13:02 (OR = 0.22; p < 1 × 10^–3), and DPB1*04:01 (OR = 0.35; p < 1 × 10^–3) were identified as protective [45, 46].

Oceania

In an Australian and New Zealand cohort of European ancestry, Burnard et al. [47] used elastic net penalized regression to identify MS-associated SNPs that standard GWAS might miss. The strongest signal was rs9271366, a tag for the HLA-DRB1*15 haplotype, with additional robust hits in tight linkage disequilibrium (e.g., rs2395182, rs3117098, rs3129941, rs6903608, rs9267992) and independent effects for rs2299851 and rs3819721 [47].