Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands.
Hum Mol Genet. 2012 Oct 15;21(R1):R29-36. doi: 10.1093/hmg/dds384. Epub 2012 Sep 12.
The major histocompatibility complex (MHC) region on the short arm of chromosome 6 harbors the largest number of replicated associations across the human genome for a wide range of diseases, but the functional basis for these associations is still poorly understood. One fundamental challenge in fine-mapping associations to functional alleles is the enormous sequence diversity and broad linkage disequilibrium of the MHC, both of which hamper the cost-effective interrogation in large patient samples and the identification of causal variants. In this review, we argue that there is now a valuable opportunity to leverage existing genome-wide association study (GWAS) datasets for in-depth investigation to identify independent effects in the MHC. Application of imputation to GWAS data facilitates comprehensive interrogation of the classical human leukocyte antigen (HLA) loci. These datasets are, in many cases, sufficiently large to give investigators the ability to disentangle effects at different loci. We also explain how querying variation at individual amino acid positions for association can be powerful and expand traditional analyses that focus only on the classical HLA types.
主要组织相容性复合体（MHC）位于 6 号染色体短臂上，在人类基因组中拥有数量最多的与多种疾病相关的重复关联，但这些关联的功能基础仍知之甚少。精细映射关联到功能等位基因的一个基本挑战是 MHC 的巨大序列多样性和广泛的连锁不平衡，这两者都阻碍了在大型患者样本中进行具有成本效益的检测以及确定因果变体。在这篇综述中，我们认为现在有一个有价值的机会，可以利用现有的全基因组关联研究（GWAS）数据集进行深入调查，以确定 MHC 中的独立效应。将 imputation 应用于 GWAS 数据有助于全面研究经典的人类白细胞抗原（HLA）基因座。在许多情况下，这些数据集足够大，使研究人员能够在不同的基因座上区分效应。我们还解释了如何查询关联的个体氨基酸位置的变异，这可以增强传统仅关注经典 HLA 类型的分析。
