Predicting phenotypic diversity from molecular and genetic data

Tom Harel, Naama Peshes-Yaloz, Eran Bacharach, Irit Gat-Viks*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)297-311
Number of pages15
Issue number1
StatePublished - 2019


  • Complex traits
  • Computational modeling
  • GenPred
  • Gene expression
  • Genetics
  • Genomic Prediction
  • Shared Data Resources


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