|| Normal lower risk of Multiple Sclerosis.
|| Increased risk of Multiple Sclerosis.
|| 3.3x increased risk for multiple sclerosis
|?|| (C;C) (C;T) (T;T) ||28|
is a SNP near the HLA-class II region that may be associated with several autoimmune diseases.
In a study of two cohorts (Spanish and American multiple sclerosis patients, each numbering several hundred), rs3129934 was the SNP most associated with increased risk for multiple sclerosis. The odds ratio was 3.3 (CI: 2.3 - 4.9, p = 9 x 10e-11). Seven other SNPs were also identified in this study as associated with increased risk for MS.[PMID 18941528]
[PMID 17554260] associated with type-1 diabetes. 1.6x risk of type-1 diabetes?
23andMe uses rs9273363 instead of rs3129934 in its reports on type 1 diabetes risk. This SNP was formerly used as a proxy for rs9272346, with this explanation:
"Note: the 2007 Wellcome Trust paper reported a strong association between type 1 diabetes and the SNP rs9272346. Our quality control process flagged data for rs9272346 as unreliable, so we instead included the SNP rs3129934, which is also highly associated with type 1 diabetes. Both are in the HLA region, although the exact linkage patterns between tagging SNPs and traditionally determined HLA haplotypes is still being worked out."
However it is apparent that the HapMap frequencies for these two snps are very different.
rs9291683, rs3129934 and rs2705293 discussed in the context of CNVs in a blog post
[PMID 22457343] A genome-wide association study in progressive multiple sclerosis
[PMID 17660530] Risk alleles for multiple sclerosis identified by a genomewide study.
[PMID 18252225] On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants.
[PMID 20369022] Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations.
[PMID 20405052] The effect of single nucleotide polymorphisms from genome wide association studies in multiple sclerosis on gene expression.
[PMID 20546594] An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.