A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases.
A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases.
复制标题
快速关联测试，用于识别涉及稀有疾病的致病变异。
DOI：
10.1016/j.ajhg.2017.05.015
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发表时间：
2017-07-06
影响因子：
9.8
通讯作者：
Turro E
中科院分区：
文献类型：
--
作者：
Greene D;NIHR BioResource;Richardson S;Turro E
我们提出了一个快速和强大的推理程序，用于识别位点与罕见的遗传性疾病使用贝叶斯模型比较。在基线模型下，研究中所有个体的疾病风险都是固定的。在关联模型下，疾病风险取决于罕见变异潜在的致病性和非致病性变异，每个个体携带的致病性等位基因的数量以及遗传模式。可以在贝叶斯框架中推断指示关联存在的参数和表示每个变体的致病性和遗传模式的参数。从等位基因频率数据库、结果预测算法或基因组数据集导出的变体特异性先验信息可以被整合到推断中。关联模型可以拟合到基因座中的不同变体子集，并使用模型选择程序进行比较。如果只有特定类别的变异会带来疾病风险，并且可以提出与该类别相关的特定疾病病因，则该程序可以改善推断。我们表明，我们的方法，称为BeviMed，是更强大和信息比现有的罕见变异关联方法的显性和隐性疾病的背景下。我们的算法的高计算效率使得它可行的测试协会在大的非编码部分的基因组。我们已经将BeviMed应用于来自6，586名患有各种罕见疾病的个体的全基因组测序数据。我们表明，它可以识别涉及罕见疾病的多个基因座，同时正确推断遗传模式，可能的致病变异和负责的变异类别。
We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.
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影响因子：
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