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      Predicting Phenotypic Diversity from Molecular and Genetic Data

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      Genetics

      Genetics Society of America

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          Abstract

          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.

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          Author and article information

          Journal
          Genetics
          Genetics
          Genetics Society of America
          0016-6731
          1943-2631
          July 27 2019
          : genetics.302463.2019
          Article
          10.1534/genetics.119.302463
          6727812
          31352366
          © 2019

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