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.