We introduce a method to draw causal inferences—inferences immune to all possible confounding—from genetic data that include parents and offspring. Causal conclusions are possible with these data because the natural randomness in meiosis can be viewed as a high-dimensional randomized experiment. We make this observation actionable by developing a conditional independence test that identifies regions of the genome containing distinct causal variants. The proposed digital twin test compares an observed offspring to carefully constructed synthetic offspring from the same parents to determine statistical significance, and it can leverage any black-box multivariate model and additional nontrio genetic data to increase power. Crucially, our inferences are based only on a well-established mathematical model of recombination and make no assumptions about the relationship between the genotypes and phenotypes. We compare our method to the widely used transmission disequilibrium test and demonstrate enhanced power and localization.

我们引入了一种从包括父母和后代的遗传数据中得出因果推论的方法，即不受所有可能的混杂因素影响的推论。利用这些数据可以得出因果结论，因为减数分裂的自然随机性可以被视为高维随机实验。我们通过开发条件独立性测试来识别包含不同因果变异的基因组区域，从而使这一观察变得可行。所提出的数字双胞胎测试将观察到的后代与来自同一父母的精心构建的合成后代进行比较，以确定统计显着性，并且它可以利用任何黑盒多元模型和额外的非三体遗传数据来提高功效。至关重要的是，我们的推论仅基于完善的重组数学模型，并且没有对基因型和表型之间的关系做出任何假设。我们将我们的方法与广泛使用的传输不平衡测试进行比较，并证明了增强的能力和定位。