Reader Comments
Post a new comment on this article
Post Your Discussion Comment
Please follow our guidelines for comments and review our competing interests policy. Comments that do not conform to our guidelines will be promptly removed and the user account disabled. The following must be avoided:
- Remarks that could be interpreted as allegations of misconduct
- Unsupported assertions or statements
- Inflammatory or insulting language
Thank You!
Thank you for taking the time to flag this posting; we review flagged postings on a regular basis.
closeIn a nutshell
Posted by piwdebakker on 07 Jun 2013 at 09:47 GMT
Across the human genome, the major histocompatibility complex (MHC) region harbors the largest number of validated associations between inherited DNA sequence variation and a vast range of diseases, including autoimmune and inflammatory conditions as well as cancers and neuropsychiatric disease. However, the biological basis for these associations is only poorly understood, in part because it remains very difficult to pinpoint causal genes across the MHC, a region characterized by distinctive genomic features due to natural selection. Traditionally, the focus has been on the human leukocyte antigen (HLA) genes, which encode key molecules of the immune system, but the enormous sequence diversity of these genes has made it labor-intensive and costly to generate HLA genotyping data in large numbers of samples. The recent wave of genome-wide association studies, however, makes it highly attractive to leverage these existing SNP genotype data to impute classical HLA genotypes by exploiting known correlations between SNPs and classical HLA alleles. We demonstrate that with a large reference data set with both SNP and HLA data it is possible to predict HLA genotypes at amino acid resolution with very high accuracy. With this computational method (which we have made publicly available), we hope to improve our understanding of the genetic basis of MHC associations.
Have a look here: http://www.broadinstitute...