Uncovering relationships between molecular and phenotypic diversity presents a substantial challenge. Harel et al. devised InPhenotype, a computational approach that combines gene-expression and genotype data to predict quantitative traits. The key advance... Despite the importance of complex phenotypes, an in-depth understanding of the combined molecular and genetic effects on a phenotype has yet to be achieved. Here, we introduce InPhenotype, a novel computational approach for complex phenotype prediction, where gene-expression data and genotyping data are integrated to yield quantitative predictions of complex physiological traits. Unlike existing computational methods, InPhenotype makes it possible to model potential regulatory interactions between gene expression and genomic loci without compromising the continuous nature of the molecular data. We applied InPhenotype to synthetic data, exemplifying its utility for different data parameters, as well as its superiority compared to current methods in both prediction quality and the ability to detect regulatory interactions of genes and genomic loci. Finally, we show that InPhenotype can provide biological insights into both mouse and yeast datasets.

中文翻译：

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从分子和遗传数据预测表型多样性。


揭示分子和表型多样性之间的关系提出了巨大的挑战。哈雷尔等人。设计了 InPhenotype，一种结合基因表达和基因型数据来预测数量性状的计算方法。关键进展...尽管复杂表型很重要，但尚未深入了解分子和遗传对表型的综合影响。在这里，我们介绍 InPhenotype，这是一种用于复杂表型预测的新型计算方法，其中基因表达数据和基因分型数据被集成以产生复杂生理特征的定量预测。与现有的计算方法不同，InPhenotype 可以模拟基因表达和基因组位点之间潜在的调控相互作用，而不会影响分子数据的连续性。我们将 InPhenotype 应用于合成数据，举例说明了它对不同数据参数的实用性，以及与现有方法相比在预测质量和检测基因和基因组位点调控相互作用的能力方面的优越性。最后，我们证明 InPhenotype 可以提供对小鼠和酵母数据集的生物学见解。