To assess the association of established multiple sclerosis (MS) risk variants in 3,254 African Americans (1,162 cases and 2,092 controls).
Human leukocyte antigen (HLA)-DRB1, HLA-DQB1, and HLA-A alleles were typed by molecular techniques. Single nucleotide polymorphism (SNP) genotyping was conducted for 76 MS-associated SNPs and 52 ancestry informative marker SNPs selected throughout the genome. Self-declared ancestry was refined by principal component analysis of the ancestry informative marker SNPs. An ancestry-adjusted multivariate model was applied to assess genetic associations.
The following major histocompatibility complex risk alleles were replicated: HLA-DRB1*15:01 (odds ratio [OR] = 2.02 [95% confidence interval: 1.54-2.63], p = 2.50e-07), HLA-DRB1*03:01 (OR = 1.58 [1.29-1.94], p = 1.11e-05), as well as HLA-DRB1*04:05 (OR = 2.35 [1.26-4.37], p = 0.007) and the African-specific risk allele of HLA-DRB1*15:03 (OR = 1.26 [1.05-1.51], p = 0.012). The protective association of HLA-A*02:01 was confirmed (OR = 0.72 [0.55-0.93], p = 0.013). None of the HLA-DQB1 alleles were associated with MS. Using a significance threshold of p < 0.01, outside the major histocompatibility complex region, 8 MS SNPs were also found to be associated with MS in African Americans.
MS genetic risk in African Americans only partially overlaps with that of Europeans and could explain the difference of MS prevalence between populations.
"DBR1*15:01 is an allele of human leukocyte antigen (HLA) complex that encodes a MHC class II cell surface receptor. The association of this allele with MS appears to be consistent with many previous reports on situations other than vaccine adverse event [35,36]. "
[Show abstract][Hide abstract] ABSTRACT: BackgroundDue to human variations in genetic susceptibility, vaccination often triggers adverse events in a small population of vaccinees. Based on our previous work on ontological modeling of genetic susceptibility to disease, we developed an Ontology of Genetic Susceptibility Factors (OGSF), a biomedical ontology in the domain of genetic susceptibility and genetic susceptibility factors. The OGSF framework was then applied in the area of vaccine adverse events (VAEs).ResultsOGSF aligns with the Basic Formal Ontology (BFO). OGSF defines ‘genetic susceptibility’ as a subclass of BFO:disposition and has a material basis ‘genetic susceptibility factor’. The ‘genetic susceptibility to pathological bodily process’ is a subclasses of ‘genetic susceptibility’. A VAE is a type of pathological bodily process. OGSF represents different types of genetic susceptibility factors including various susceptibility alleles (e.g., SNP and gene). A general OGSF design pattern was developed to represent genetic susceptibility to VAE and associated genetic susceptibility factors using experimental results in genetic association studies. To test and validate the design pattern, two case studies were populated in OGSF. In the first case study, human gene allele DBR*15:01 is susceptible to influenza vaccine Pandemrix-induced Multiple Sclerosis. The second case study reports genetic susceptibility polymorphisms associated with systemic smallpox VAEs. After the data of the Case Study 2 were represented using OGSF-based axioms, SPARQL was successfully developed to retrieve the susceptibility factors stored in the populated OGSF. A network of data from the Case Study 2 was constructed by using ontology terms and individuals as nodes and ontology relations as edges. Different social network analys
is (SNA) methods were then applied to verify core OGSF terms. Interestingly, a SNA hub analysis verified all susceptibility alleles of SNPs and a SNA closeness analysis verified the susceptibility genes in Case Study 2. These results validated the proper OGSF structure identified different ontology aspects with SNA methods.ConclusionsOGSF provides a verified and robust framework for representing various genetic susceptibility types and genetic susceptibility factors annotated from experimental VAE genetic association studies. The RDF/OWL formulated ontology data can be queried using SPARQL and analyzed using centrality-based network analysis methods.
[Show abstract][Hide abstract] ABSTRACT: A wealth of data confirms that genetic variation is an important determinant of multiple sclerosis (MS) risk. Population, family and molecular studies provide strong empirical support for a polygenic model of inheritance, driven primarily by allelic variants relatively common in the general population. The major histocompatibility complex (MHC) in chromosome 6p21.3 represents by far the strongest MS susceptibility locus genome-wide and was unambiguously identified in all studied populations. The primary signal arises from the HLA-DRB1 gene in the Class II segment of the locus, with hierarchical allelic and haplotypic effects. Independent protective signals in the telomeric Class I region of the locus have been described as well. Over the last 6 years, large multicenter DNA collections have thrived and the development of new laboratory and analytical approaches has matured at a remarkable pace, allowing pursuit of comprehensive 'agnostic' genome-wide association studies to identify and characterise the non-MHC genetic component of MS. Taken together, the results have provided unambiguous evidence for the association of over 100 non-MHC loci with disease susceptibility. Follow-up experiments refined some of the association signals (IL2RA and CD58), identified gene-gene interactions (HLA-DRB1/EVI5) and revealed mechanistic insights into the functional consequences of the identified gene variants, most notably an increase in the soluble to membrane-bound ratio for IL-7, IL-2 and TNF receptors and a tyrosine-protein kinase 2-mediated immune deviation. These results significantly broaden our understanding of disease pathogenesis and permit, for the first time, modeling an individual's disease risk within the context of his or her familial history. Progress in identifying additional risk alleles is likely to be rapid in the near future. Although the effect of any given predisposing variant is modest, the possibility exists that multifaceted gene-gene and/or gene-environment interactions could substantially increase the contribution of some variants to the overall genetic risk. In addition, susceptibility genes may be subject to epigenetic modifications, which greatly increase the complexity of MS inheritance. Despite these remarkable advances, the knowledge of MS genetics remains incomplete. For example, a key but unresolved question is whether genetic variants influence disease trajectory. Ongoing efforts to fully characterize the repertoire of genes that predispose to MS and modulate its presentation is discussed. Functional characterization of even a moderate genetic effect on MS pathogenesis by a known gene or group of genes can assist in elucidating fundamental mechanisms of disease expression and yield important therapeutic opportunities.
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