Article

Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases.

Research Center, Université de Montréal and Montreal Heart Institute, Montreal, QC, Canada.
Proceedings of the National Academy of Sciences (impact factor: 9.68). 11/2009; 106(44):18680-5. DOI:10.1073/pnas.0909307106 pp.18680-5
Source: PubMed

ABSTRACT The human MHC represents the strongest susceptibility locus for autoimmune diseases. However, the identification of the true predisposing gene(s) has been handicapped by the strong linkage disequilibrium across the region. Furthermore, most studies to date have been limited to the examination of a subset of the HLA and non-HLA genes with a marker density and sample size insufficient for mapping all independent association signals. We genotyped a panel of 1,472 SNPs to capture the common genomic variation across the 3.44 megabase (Mb) classic MHC region in 10,576 DNA samples derived from patients with systemic lupus erythematosus, Crohn's disease, ulcerative colitis, rheumatoid arthritis, myasthenia gravis, selective IgA deficiency, multiple sclerosis, and appropriate control samples. We identified the primary association signals for each disease and performed conditional regression to identify independent secondary signals. The data demonstrate that MHC associations with autoimmune diseases result from complex, multilocus effects that span the entire region.

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  • Article: A statistical method for predicting classical HLA alleles from SNP data.
    Stephen Leslie, Peter Donnelly, Gil McVean
    [show abstract] [hide abstract]
    ABSTRACT: Genetic variation at classical HLA alleles is a crucial determinant of transplant success and susceptibility to a large number of infectious and autoimmune diseases. However, large-scale studies involving classical type I and type II HLA alleles might be limited by the cost of allele-typing technologies. Although recent studies have shown that some common HLA alleles can be tagged with small numbers of markers, SNP-based tagging does not offer a complete solution to predicting HLA alleles. We have developed a new statistical methodology to use SNP variation within the region to predict alleles at key class I (HLA-A, HLA-B, and HLA-C) and class II (HLA-DRB1, HLA-DQA1, and HLA-DQB1) loci. Our results indicate that a single panel of approximately 100 SNPs typed across the region is sufficient for predicting both rare and common HLA alleles with up to 95% accuracy in both African and non-African populations. Furthermore, we show that HLA alleles can be successfully predicted by using previously genotyped SNPs that are within the MHC and that had not been chosen for their ability to predict HLA alleles, such as those included on genome-wide products. These results indicate that our methodology, combined with an extended database of reference haplotypes, will facilitate large-scale experiments, including disease-association studies and vaccine trials, in which detailed information about HLA type is valuable.
    The American Journal of Human Genetics 02/2008; 82(1):48-56. · 10.60 Impact Factor
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Keywords

appropriate control samples
 
common genomic variation
 
conditional regression
 
entire region
 
HLA
 
independent association signals
 
independent secondary signals
 
multiple sclerosis
 
non-HLA genes
 
patients
 
rheumatoid arthritis
 
selective IgA deficiency
 
strong linkage disequilibrium
 
strongest susceptibility locus
 
systemic lupus erythematosus
 
ulcerative colitis