A reaction norm perspective on reproducibility.

IF 1.3 4区 生物学 Q3 BIOLOGY
Theory in Biosciences Pub Date : 2021-06-01 Epub Date: 2021-03-25 DOI:10.1007/s12064-021-00340-y
Bernhard Voelkl, Hanno Würbel
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引用次数: 0

Abstract

Reproducibility in biomedical research, and more specifically in preclinical animal research, has been seriously questioned. Several cases of spectacular failures to replicate findings published in the primary scientific literature have led to a perceived reproducibility crisis. Diverse threats to reproducibility have been proposed, including lack of scientific rigour, low statistical power, publication bias, analytical flexibility and fraud. An important aspect that is generally overlooked is the lack of external validity caused by rigorous standardization of both the animals and the environment. Here, we argue that a reaction norm approach to phenotypic variation, acknowledging gene-by-environment interactions, can help us seeing reproducibility of animal experiments in a new light. We illustrate how dominating environmental effects can affect inference and effect size estimates of studies and how elimination of dominant factors through standardization affects the nature of the expected phenotype variation through the reaction norms of small effect. Finally, we discuss the consequences of reaction norms of small effect for statistical analysis, specifically for random effect latent variable models and the random lab model.

Abstract Image

Abstract Image

从反应规范角度看可重复性。
生物医学研究,尤其是临床前动物研究的可重复性一直受到严重质疑。一些在主要科学文献中发表的研究结果惊人地无法复制,这导致了人们认为的可重复性危机。人们提出了对可重复性的各种威胁,包括缺乏科学严谨性、统计能力低、发表偏差、分析灵活性和欺诈。通常被忽视的一个重要方面是动物和环境的严格标准化所导致的外部有效性的缺乏。在此,我们认为,表型变异的反应规范方法承认基因与环境之间的相互作用,有助于我们从新的角度看待动物实验的可重复性。我们说明了主导环境效应如何影响推论和研究的效应大小估计,以及通过标准化消除主导因素如何通过小效应的反应规范影响预期表型变异的性质。最后,我们讨论了小效应反应规范对统计分析的影响,特别是对随机效应潜变量模型和随机实验室模型的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Theory in Biosciences
Theory in Biosciences 生物-生物学
CiteScore
2.70
自引率
9.10%
发文量
21
审稿时长
3 months
期刊介绍: Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.
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