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Disease association tests by inferring ancestral haplotypes using a hidden markov model

机译：通过使用隐马尔可夫模型推断祖先单元型来进行疾病关联测试

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Motivation: Most genome-wide association studies rely on single nucleotide polymorphism (SNP) analyses to identify causal loci. The increased stringency required for genome-wide analyses (with per-SNP significance threshold typically 10(7)) means that many real signals will be missed. Thus it is still highly relevant to develop methods with improved power at low type I error. Haplotype-based methods provide a promising approach; however, they suffer from statistical problems such as abundance of rare haplotypes and ambiguity in defining haplotype block boundaries. Results: We have developed an ancestral haplotype clustering (AncesHC) association method which addresses many of these problems. It can be applied to biallelic or multiallelic markers typed in haploid, diploid or multiploid organisms, and also handles missing genotypes. Our model is free from the assumption of a rigid block structure but recognizes a block-like structure if it exists in the data. We employ a Hidden Markov Model (HMM) to cluster the haplotypes into groups of predicted common ancestral origin. We then test each cluster for association with disease by comparing the numbers of cases and controls with 0, 1 and 2 chromosomes in the cluster. We demonstrate the power of this approach by simulation of case-control status under a range of disease models for 1500 outcrossed mice originating from eight inbred lines. Our results suggest that AncesHC has substantially more power than single-SNP analyses to detect disease association, and is also more powerful than the cladistic haplotype clustering method CLADHC.
机译：动机：大多数全基因组关联研究都依靠单核苷酸多态性（SNP）分析来确定因果基因座。全基因组分析所需的严格性提高（每个SNP的显着性阈值通常为10（7））意味着将错过许多真实信号。因此，在低I型误差下开发具有改进功率的方法仍然非常重要。基于单体型的方法提供了一种有前途的方法。但是，它们遭受统计问题的困扰，例如大量的稀有单倍型和在定义单倍型块边界时的模棱两可。结果：我们已经开发了一种祖先单体型聚类（AncesHC）关联方法，可以解决许多这些问题。它可以应用于在单倍体，二倍体或多倍体生物体中键入的双等位基因或多等位基因标记，还可以处理缺失的基因型。我们的模型没有假设采用刚性块结构，但可以识别出数据中是否存在块状结构。我们采用隐马尔可夫模型（HMM）将单倍型聚类为可预测的共同祖先群体。然后，我们通过比较簇中0、1、2条染色体的病例和对照的数量，测试每个簇是否与疾病相关。我们通过模拟病例对照状态在一系列疾病模型范围内对源自八个自交系的1500只异交小鼠的方法证明了这种方法的力量。我们的结果表明，AncesHC比单SNP分析具有更大的检测疾病关联的功能，并且比单倍型聚类分析方法CLADHC更强大。

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