Page Grier P, George Varghese, Go Rodney C, Page Patricia Z, Allison David B
Am J Hum Genet. 2003 Oct;73(4):711-9. doi: 10.1086/378900. Epub 2003 Sep 17.
Although mathematical relationships can be proven by deductive logic, biological relationships can only be inferred from empirical observations. This is a distinct disadvantage for those of us who strive to identify the genes involved in complex diseases and quantitative traits. If causation cannot be proven, however, what does constitute sufficient evidence for causation? The philosopher Karl Popper said, "Our belief in a hypothesis can have no stronger basis than our repeated unsuccessful critical attempts to refute it." We believe that to establish causation, as scientists, we must make a serious attempt to refute our own hypotheses and to eliminate all known sources of bias before association becomes causation. In addition, we suggest that investigators must provide sufficient data and evidence of their unsuccessful efforts to find any confounding biases. In this editorial, we discuss what "causation" means in the context of complex diseases and quantitative traits, and we suggest guidelines for steps that may be taken to address possible confounders of association before polymorphisms may be called "causative."
虽然数学关系可以通过演绎逻辑来证明,但生物学关系只能从实证观察中推断出来。这对我们这些致力于识别复杂疾病和数量性状相关基因的人来说是一个明显的劣势。然而,如果因果关系无法得到证明,那么究竟什么才构成因果关系的充分证据呢?哲学家卡尔·波普尔说过:“我们对一个假设的信念,没有比我们反复进行的、未能成功反驳它的批判性尝试更坚实的基础了。”我们认为,作为科学家,要确立因果关系,就必须认真尝试反驳我们自己的假设,并在关联成为因果关系之前消除所有已知的偏差来源。此外,我们建议研究人员必须提供足够的数据和证据,证明他们为寻找任何混杂偏差所做的努力是不成功的。在这篇社论中,我们讨论了在复杂疾病和数量性状的背景下“因果关系”的含义,并提出了一些指导方针,说明在多态性被称为“致病因素”之前,为解决可能的关联混杂因素可以采取哪些步骤。