The opaque relationship between biology and behavior is an intractable problem for psychiatry, and it increasingly challenges longstanding diagnostic categorizations. While various big data sciences have been repeatedly deployed as potential solutions, they have so far complicated more than they have managed to disentangle. Attending to categorical misalignment, this article proposes one reason why this is the case: Datasets have to instantiate clinical categories in order to make biological sense of them, and they do so in different ways. Here, I use mixed methods to examine the role of the reuse of big data in recent genomic research on autism spectrum disorder (ASD). I show how divergent regimes of psychiatric categorization are innately encoded within commonly used datasets from MSSNG and 23andMe, contributing to a rippling disjuncture in the accounts of autism that this body of research has produced. Beyond the specific complications this dynamic introduces for the category of autism, this paper argues for the necessity of critical attention to the role of dataset reuse and recombination across human genomics and beyond.

中文翻译：

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分类错位：在大数据生物样本库中制作自闭症


生物学和行为之间的不透明关系是精神病学的一个棘手问题，它越来越挑战长期存在的诊断分类。虽然各种大数据科学已被反复部署为潜在的解决方案，但到目前为止，它们的复杂性比它们设法解开的要复杂得多。关于分类错位，本文提出了发生这种情况的一个原因：数据集必须实例化临床类别才能使它们具有生物学意义，并且它们以不同的方式做到这一点。在这里，我使用混合方法来检查大数据重用在最近关于自闭症谱系障碍 （ASD） 的基因组研究中的作用。我展示了精神病学分类的不同机制是如何在 MSSNG 和 23andMe 的常用数据集中天生编码的，从而导致该研究机构产生的自闭症描述出现涟漪般的脱节。除了这种动态为自闭症类别带来的具体复杂性之外，本文还认为有必要批判性地关注数据集重用和重组在人类基因组学及其他领域中的作用。