Systems Genetics of Complex Traits in Drosophila melanogaster

By Julien F. Ayroles, M. A. Carbone, Eric A. Stone, K. Jordan, R. F. Lyman, M. Magwire, S. Rollmann, Laura H. Duncan, Faye Lawrence, R. Anholt and 1 more. Nature Genetics, 2009

535 citations recorded by the source corpus.

Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits.

Subfields: Cellular and Molecular Neuroscience, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4bbf3387b100e5b9888cc0b07a37cc4e1bf1016f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

J. G. Sørensen

In Genetics

In Molecular Biology

Citations in the corpus

Systems genetics approaches to understand complex traits (2013, background): “It is clear from studies using experimental organisms that natural phenotypic variation usually results from many interacting alleles that show context-dependent and environmentally sensitive effects, and there is a reason to believe that common diseases will be similarly complex 2–6 .”
The genetics of quantitative traits: challenges and prospects (2009, background): “Although many thousands of transcripts are genetically variable, they are not independent: the levels of expression of many transcripts co-vary between individuals in the mapping populatio”
The Drosophila melanogaster Genetic Reference Panel (2012, unknown): “…for systems genetics analyses of the relationship between molecular variation, causal molecular networks, and genetic variation for complex traits 4,39,46 , and will anchor evolutionary studies in comparison with sequenced Drosophila species to assess to what extent variation within a species…”
Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines (2014, background): “The DGRP is an ideal resource for systems genetics (Ayroles et al., 2009; Massouras et al., 2012) and epistatic interaction network analyses (Yamamoto et al., 2009; Huang et al., 2012; Swarup et al., 2013) of molecular and complex organismal traits.”
Genotype-by-Environment Interaction and Plasticity: Exploring Genomic Responses of Plants to the Abiotic Environment (2013, background): “Ayroles et al. (2009) reported significant genetic variation in transcript abundance for over 68% of expressed transcripts in a set of 40 inbred fruit fly lines.”
Application of heat shock protein expression for detecting natural adaptation and exposure to stress in natural populations (2010, background): “However, regardless of body size different tissues might show different sensitivity and expression of genes and proteins, which can be expected to differ to a large extent (Lakhotia and Prasanth, 2002; Ayroles et al., 2009).”
Comparative Analysis of Proteome and Transcriptome Variation in Mouse (2011, result): “Consistent with previous reports [16], we detected a broad spectrum of heritability estimates for the transcript levels”