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通过全基因组测序发现人类生殖细胞诱变剂:功效计算的见解揭示了控制家庭间变异性的重要性。

Discovering human germ cell mutagens with whole genome sequencing: Insights from power calculations reveal the importance of controlling for between-family variability.

作者信息

Webster R J, Williams A, Marchetti F, Yauk C L

机构信息

Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada.

Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada.

出版信息

Mutat Res Genet Toxicol Environ Mutagen. 2018 Jul;831:24-32. doi: 10.1016/j.mrgentox.2018.04.004. Epub 2018 Apr 20.

Abstract

Mutations in germ cells pose potential genetic risks to offspring. However, de novo mutations are rare events that are spread across the genome and are difficult to detect. Thus, studies in this area have generally been under-powered, and no human germ cell mutagen has been identified. Whole Genome Sequencing (WGS) of human pedigrees has been proposed as an approach to overcome these technical and statistical challenges. WGS enables analysis of a much wider breadth of the genome than traditional approaches. Here, we performed power analyses to determine the feasibility of using WGS in human families to identify germ cell mutagens. Different statistical models were compared in the power analyses (ANOVA and multiple regression for one-child families, and mixed effect model sampling between two to four siblings per family). Assumptions were made based on parameters from the existing literature, such as the mutation-by-paternal age effect. We explored two scenarios: a constant effect due to an exposure that occurred in the past, and an accumulating effect where the exposure is continuing. Our analysis revealed the importance of modeling inter-family variability of the mutation-by-paternal age effect. Statistical power was improved by models accounting for the family-to-family variability. Our power analyses suggest that sufficient statistical power can be attained with 4-28 four-sibling families per treatment group, when the increase in mutations ranges from 40 to 10% respectively. Modeling family variability using mixed effect models provided a reduction in sample size compared to a multiple regression approach. Much larger sample sizes were required to detect an interaction effect between environmental exposures and paternal age. These findings inform study design and statistical modeling approaches to improve power and reduce sequencing costs for future studies in this area.

摘要

生殖细胞中的突变会给后代带来潜在的遗传风险。然而,新生突变是罕见事件,分散在整个基因组中,难以检测。因此,该领域的研究通常缺乏足够的效力,尚未鉴定出任何人类生殖细胞诱变剂。有人提出对人类家系进行全基因组测序(WGS),作为克服这些技术和统计挑战的一种方法。与传统方法相比,WGS能够分析更广泛的基因组范围。在此,我们进行了功效分析,以确定在人类家系中使用WGS识别生殖细胞诱变剂的可行性。在功效分析中比较了不同的统计模型(独子家庭采用方差分析和多元回归,每个家庭有两到四个兄弟姐妹的采用混合效应模型抽样)。基于现有文献中的参数做出假设,例如突变与父亲年龄的效应。我们探讨了两种情况:过去发生的暴露产生的恒定效应,以及暴露持续存在的累积效应。我们的分析揭示了对突变与父亲年龄效应的家庭间变异性进行建模的重要性。考虑家庭间变异性的模型提高了统计功效。我们的功效分析表明,当每个治疗组的突变增加分别为40%至10%时,每个治疗组有4 - 28个有四个兄弟姐妹的家庭就可以获得足够的统计功效。与多元回归方法相比,使用混合效应模型对家庭变异性进行建模可减少样本量。检测环境暴露与父亲年龄之间的相互作用效应需要大得多的样本量。这些发现为研究设计和统计建模方法提供了参考,以提高功效并降低该领域未来研究的测序成本。

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