Commentary: thoughts on assessing evidence for gene by environment interaction.
Commentary: thoughts on assessing evidence for gene by environment interaction.
复制标题
评论：通过环境相互作用评估基因证据的想法。
DOI：
10.1093/ije/dys048
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发表时间：
2012
影响因子：
7.7
通讯作者：
Weinberg,ClariceR
中科院分区：
文献类型：
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作者：
Weinberg,ClariceR
Boffetta等人在本期的评论中提出了一个具有挑战性的问题：鉴于遗传因素G和环境因素E都可能影响癌症风险，我们如何系统地评估累积的已发表证据，以证明它们之间存在相互作用？大多数复杂的疾病，不仅仅是癌症，可能是通过遗传和环境因素的网络产生的，因此这个问题成为流行病学病因学研究的核心问题之一。Boffetta等人开始对关于评估相互作用的方法和偏倚来源的文献进行了全面的回顾，给出了一个对读者非常有用的概述。然后，他们提出了一个评分系统，将与可接受性相关的先前生物学知识（例如，暴露和遗传因素参与同一途径的知识）与主要影响和相互作用的现有证据结合起来。虽然作者们应该受到赞扬，因为他们承担了一个令人生畏的问题，并对生物学给予了应有的关注，但我对拟议中的企业有一些问题。我将从提供一些补充观点开始，并以一个更大的概念问题结束。首先，所提供的建议是针对癌症的，但同样的总体策略也适用于其他复杂的表型。鉴于读者希望更广泛地应用所提出的原则，似乎值得我们扩大对它们的考虑。作者提到了病例对照设计之外的一些设计，包括病例父母三人组设计，他们说这种设计“很少使用”，但可以用于测试。确实，病例-父代设计在癌症研究中并不典型，因为大多数诊断发生在晚年。然而，病例-父母三联体设计已被广泛用于非癌性复发性疾病的研究。一项正在进行的“两姐妹研究”使用了一种针对非典型性乳腺癌的家庭设计。虽然病例-父母方法无法评估暴露的主要影响（除非也研究未受影响的兄弟姐妹），但该设计在某些方面非常适合研究出生缺陷，妊娠并发症和表型，如哮喘，精神分裂症和自闭症，这些都是在年轻时诊断的。由于其推论依赖于等位基因从父母到后代的传递，在病例-父母设计下，与遗传基因型相关的相对风险是可估计的，而无需考虑遗传群体分层。然而，这并不是它的主要卖点，因为良好的基因组控制统计方法可用于病例对照研究（前提是提供祖先信息的标记可用）。家族设计还可以抵抗由于病例和对照的自我选择而产生的偏倚（不需要基于人群的对照）。它们还抵抗由于母体基因组引起或修饰的产前经历而引起的混淆偏倚，这是一种对发病条件特别重要的机制，并且可以扭曲基于病例对照设计的推断，包括与GxE相互作用相关的推断。从核心家庭的数据，人们也可以确定和研究的致病基因，受到父母的起源效应，一种现象，不能使用病例对照设计进行探讨。根据冰岛的家庭数据，最近有报道称这种基因与乳腺癌有关。鉴于以家庭为基础的设计提供了一些优势，病例对照设计的研究条件，发病早在生活中，我们最近表明，3，4，虽然基因型的主要影响，他们完全抵制偏见，由于人口分层，无论是病例...
The commentary in this issue by Boffetta et al. 1 addresses a challenging problem: given that a genetic factor, G, and an environmental factor, E, might both influence risk of cancer, how can we systematically assess the accumulated published evidence for the existence of interaction between them? Most complex diseases, not just cancer, probably arise through a web of factors that are both genetic and environmental, so this question arises as one of central relevance to the epidemiological study of aetiology. Boffetta et al. begin with a comprehensive review of the literature on approaches and sources of bias for assessments of interaction, giving an overview that should be highly useful to readers. They then suggest a scoring system to combine prior biological knowledge related to plausibility, for example knowledge that both the exposure and the genetic factor are involved in the same pathway, with available evidence for main effects and for interaction. Whereas the authors deserve credit for taking on a daunting problem and paying due attention to the biology, I have some issues with the proposed enterprise. I will start by offering some supplemental points and end with a bigger-picture conceptual issue. First, the recommendations provided were represented as specific to cancer, but the same overall strategies should apply to other complex phenotypes as well. Given that readers will want to apply the proposed principles more broadly, it seems worthwhile to broaden our consideration of them. The authors mention some designs other than the case–control design, including the case-parent trio design, which they say is ‘rarely used’but can be used for testing. It is true that case-parent designs are not typically appropriate in cancer research, because most diagnoses occur in later life. Nevertheless, the case-parent triad design has been extensively used in studies of non-cancerous young-onset conditions. An ongoing ‘Two Sister Study’uses a family design for young-onset breast cancer. Whereas a case-parent approach cannot assess main effects of exposures (unless unaffected siblings are also studied), the design is in some ways ideal for the study of birth defects, pregnancy complications and phenotypes such as asthma, schizophrenia and autism, which are diagnosed at a young age. As its inference relies on transmissions of alleles from parents to offspring, under a case-parent design relative risks associated with inherited genotypes are estimable without concern for genetic population stratification. However, this is not its major selling point, as good statistical methods for genomic control are available for case–control studies (provided ancestry-informative markers are available). Family designs also resist bias due to self-selection of cases and controls (no populationbased controls are needed). They also resist confounding bias due to prenatal experiences that are caused or modified by the maternal genome, a mechanism that can be particularly important for young-onset conditions and can distort inference based on case–control designs, including inference related to GxE interaction. With data from nuclear families, one can also identify and study causative genes that are subject to parent-of-origin effects, a phenomenon that cannot be probed using a case–control design. Such a gene was recently reported for breast cancer 2 based on Icelandic family data.A word of caution is needed. Whereas family-based designs offer some advantages over a case–control design for studying conditions with onset early in life, we have recently shown 3, 4 that, although for genotype main effects they fully resist bias due to population stratification, neither case …