Biological interactions are prevalent in the functioning organisms. Correspondingly, statistical geneticists developed various models to identify genetic interactions through genotype-phenotype association mapping. The current standard protocols in practice test single variants or single regions (that contain multiple local variants) sequentially along the genome, followed by functional annotations that involve various aspects including interactions. The testing of genetic interactions upfront is rare in practice due to the burden of testing a huge number of combinations, which lead to the multiple-test problem and the risk of overfitting. In this work, we developed interaction-integrated linear mixed model (ILMM), a novel model that integrates a priori knowledge into linear mixed models. ILMM enables statistical integration of genetic interactions upfront and overcomes the problems associated with combination searching.

中文翻译：

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将相互作用整合到基因型-表型关联映射中的统计模型：揭示自闭症基础的3D遗传基础的应用程序

生物相互作用在功能性生物中普遍存在。相应地，统计遗传学家开发了各种模型以通过基因型-表型关联作图来鉴定遗传相互作用。在实践中，当前的标准协议实际上是沿着基因组依次测试单个变体或单个区域（包含多个局部变体），然后是涉及包括相互作用在内的各个方面的功能注释。由于测试大量组合的负担，在实践中很少预先进行遗传相互作用的测试，这会导致多重测试问题和过度拟合的风险。在这项工作中，我们开发了交互集成线性混合模型（ILMM），该模型将先验知识集成到线性混合模型中。