Smart Algos, Berlin, Germany.
PLoS One. 2013 Aug 5;8(8):e70151. doi: 10.1371/journal.pone.0070151. Print 2013.
The identification of disease-causing mutations in next-generation sequencing (NGS) data requires efficient filtering techniques. In patients with rare recessive diseases, compound heterozygosity of pathogenic mutations is the most likely inheritance model if the parents are non-consanguineous. We developed a web-based compound heterozygous filter that is suited for data from NGS projects and that is easy to use for non-bioinformaticians. We analyzed the power of compound heterozygous mutation filtering by deriving background distributions for healthy individuals from different ethnicities and studied the effectiveness in trios as well as more complex pedigree structures. While usually more then 30 genes harbor potential compound heterozygotes in single exomes, this number can be markedly reduced with every additional member of the pedigree that is included in the analysis. In a real data set with exomes of four family members, two sisters affected by Mabry syndrome and their healthy parents, the disease-causing gene PIGO, which harbors the pathogenic compound heterozygous variants, could be readily identified. Compound heterozygous filtering is an efficient means to reduce the number of candidate mutations in studies aiming at identifying recessive disease genes in non-consanguineous families. A web-server is provided to make this filtering strategy available at www.gene-talk.de.
在下一代测序 (NGS) 数据中识别致病突变需要有效的过滤技术。如果父母非近亲结婚,则患有罕见隐性疾病的患者最有可能的遗传模式是复合杂合致病性突变。我们开发了一种基于网络的复合杂合过滤器,适用于 NGS 项目的数据,并且易于非生物信息学家使用。我们通过从不同种族的健康个体中推导出背景分布来分析复合杂合突变过滤的功效,并研究了在三核苷酸组以及更复杂的家系结构中的效果。虽然通常在单个外显子中会有超过 30 个基因携带潜在的复合杂合子,但随着分析中包含的家系的每个额外成员,这个数字可以显著减少。在一个包含四个家庭成员外显子的真实数据集,即患有 Mabry 综合征的两个姐妹及其健康父母中,容易识别出携带致病复合杂合变异的 PIGO 基因,该基因是致病基因。复合杂合过滤是一种有效的方法,可以减少在非近亲结婚家庭中识别隐性疾病基因的研究中的候选突变数量。提供了一个网络服务器,以便在 www.gene-talk.de 上提供这种过滤策略。