Accurate HLA type inference using a weighted similarity graph.
Accurate HLA type inference using a weighted similarity graph.
复制标题
使用加权相似度图进行准确的 HLA 类型推断
DOI：
10.1186/1471-2105-11-s11-s10
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发表时间：
2010-12-14
影响因子：
3
通讯作者：
Jiang T
中科院分区：
文献类型：
--
作者：
Xie M;Li J;Jiang T
人类白细胞抗原系统（HLA）包含许多高度可变的基因。HLA基因在人体免疫系统中发挥着重要作用，HLA基因匹配对于人体器官移植的成功至关重要。大量研究表明，HLA基因的变异与许多自身免疫性、炎症性和感染性疾病有关。然而，通过血清学或PCR分型HLA基因耗时且昂贵，这限制了涉及HLA基因的大规模研究。由于获得单核苷酸多态性（SNP）基因型数据更加容易和便宜，因此需要精确的计算算法来从SNP基因型数据推断HLA基因类型。从SNP基因型推断HLA型，第一步是从基因型推断SNP单倍型。然而，对于同一SNP基因型数据集，不同方法推断的单倍型配置往往不一致，往往难以判断哪一种是正确的。结果利用来自家谱的SNP基因型数据、部分个体已知的HLA基因型以及推断的SNP单倍型与HLA基因型的关系，设计了一种准确的HLA基因型推断算法。该算法首先根据一个由多个家系组成的群体的基因型推断出一组单倍型，然后基于新的单倍型相似性度量构建加权相似图，并从已知的HLA基因型中导出约束边。该算法根据不同HLA基因等位基因具有不同背景单倍型的原则，对所有HLA基因类型未知的单倍型进行最优标记，使相同HLA基因类型的总权重最大化。为了处理模棱两可的单倍型解决方案，我们使用遗传算法来选择倾向于最大化相同优化标准的单倍型配置。我们对先前的HapMap数据子集进行的实验表明，该算法具有很高的准确性，在“遗漏”测试中，HLA-A基因的准确率为96%，HLA-B基因的准确率为95%，HLA-C基因的准确率为97%，HLA-DRB1基因的准确率为84%，HLA-DQA1基因的准确率为98%，HLA-DQB1基因的准确率为97%。结论sour算法可以从邻近SNP基因型数据中准确推断出HLA基因型。在相同的输入数据上，与最近的一种方法相比，我们的算法取得了更高的准确率。我们的算法代码是免费提供给公众，要求通讯作者。
BackgroundThe human leukocyte antigen system (HLA) contains many highly variable genes. HLA genes play an important role in the human immune system, and HLA gene matching is crucial for the success of human organ transplantations. Numerous studies have demonstrated that variation in HLA genes is associated with many autoimmune, inflammatory and infectious diseases. However, typing HLA genes by serology or PCR is time consuming and expensive, which limits large-scale studies involving HLA genes. Since it is much easier and cheaper to obtain single nucleotide polymorphism (SNP) genotype data, accurate computational algorithms to infer HLA gene types from SNP genotype data are in need. To infer HLA types from SNP genotypes, the first step is to infer SNP haplotypes from genotypes. However, for the same SNP genotype data set, the haplotype configurations inferred by different methods are usually inconsistent, and it is often difficult to decide which one is true.ResultsIn this paper, we design an accurate HLA gene type inference algorithm by utilizing SNP genotype data from pedigrees, known HLA gene types of some individuals and the relationship between inferred SNP haplotypes and HLA gene types. Given a set of haplotypes inferred from the genotypes of a population consisting of many pedigrees, the algorithm first constructs a weighted similarity graph based on a new haplotype similarity measure and derives constraint edges from known HLA gene types. Based on the principle that different HLA gene alleles should have different background haplotypes, the algorithm searches for an optimal labeling of all the haplotypes with unknown HLA gene types such that the total weight among the same HLA gene types is maximized. To deal with ambiguous haplotype solutions, we use a genetic algorithm to select haplotype configurations that tend to maximize the same optimization criterion. Our experiments on a previously typed subset of the HapMap data show that the algorithm is highly accurate, achieving an accuracy of 96% for gene HLA-A, 95% for HLA-B, 97% for HLA-C, 84% for HLA-DRB1, 98% for HLA-DQA1 and 97% for HLA-DQB1 in a leave-one-out test.ConclusionsOur algorithm can infer HLA gene types from neighboring SNP genotype data accurately. Compared with a recent approach on the same input data, our algorithm achieved a higher accuracy. The code of our algorithm is available to the public for free upon request to the corresponding authors.