Research article

Natural genetic variation in transcriptome reflects network structure inferred with major effect mutations: insulin/TOR and associated phenotypes in Drosophila melanogaster

Sergey V Nuzhdin¹ , Jennifer A Brisson¹ , Andrew Pickering¹ , Marta L Wayne² , Lawrence G Harshman³ and Lauren M McIntyre²

¹  Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA

²  University of Florida Genetics Institute, University of Florida, Gainesville FL 32610-36103, USA

³  School of Biological Sciences, University of Nebraska at Lincoln, Lincoln, NE 68588, USA

author email corresponding author email

BMC Genomics 2009, 10:124doi:10.1186/1471-2164-10-124

Published:	24 March 2009

Abstract

Background

A molecular process based genotype-to-phenotype map will ultimately enable us to predict how genetic variation among individuals results in phenotypic alterations. Building such a map is, however, far from straightforward. It requires understanding how molecular variation re-shapes developmental and metabolic networks, and how the functional state of these networks modifies phenotypes in genotype specific way. We focus on the latter problem by describing genetic variation in transcript levels of genes in the InR/TOR pathway among 72 Drosophila melanogaster genotypes.

Results

We observe tight co-variance in transcript levels of genes not known to influence each other through direct transcriptional control. We summarize transcriptome variation with factor analyses, and observe strong co-variance of gene expression within the dFOXO-branch and within the TOR-branch of the pathway. Finally, we investigate whether major axes of transcriptome variation shape phenotypes expected to be influenced through the InR/TOR pathway. We find limited evidence that transcript levels of individual upstream genes in the InR/TOR pathway predict fly phenotypes in expected ways. However, there is no evidence that these effects are mediated through the major axes of downstream transcriptome variation.

Conclusion

In summary, our results question the assertion of the 'sparse' nature of genetic networks, while validating and extending candidate gene approaches in the analyses of complex traits.