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Genetics. 2019 Sep;213(1):297-311. doi: 10.1534/genetics.119.302463. Epub 2019 Jul 27.

Predicting Phenotypic Diversity from Molecular and Genetic Data.

Author information

1
School of Molecular Cell Biology and Biotechnology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, 6997801 Israe.
2
School of Molecular Cell Biology and Biotechnology, The George S. Wise Faculty of Life Sciences, Tel Aviv University, 6997801 Israe iritgv@tauex.tau.ac.il.

Abstract

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.

KEYWORDS:

Genomic Prediction, GenPred, Shared Data Resources; complex traits; computational modeling; gene expression; genetics

PMID:
31352366
PMCID:
PMC6727812
[Available on 2020-09-01]
DOI:
10.1534/genetics.119.302463

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