Systems genetics of the <i>Drosophila</i> metabolome

Zhou, Shanshan; Morgante, Fabio; Geisz, Matthew; Ma, Junwu; Anholt, Robert R. H.; Mackay, Trudy F. C.

doi:10.1101/gr.243030.118

Cited by 21 publications

(30 citation statements)

References 52 publications

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“…2), displaying same level of genetically determined variability as the Drosophila transcriptome. 30 It has previously been shown that metabolome variation appears to have a genetic signature, 18 but also that the metabolome is highly variable among sexes 18,49 and change with age. [50][51][52] .…”

Section: Discussionmentioning

confidence: 99%

“…[19][20][21][22] The metabolome is closer to the functional phenotype than transcriptomics and proteomics and recent study based on investigating 453 metabolites in 40 DGRP lines suggest that the metabolome might constitute a reliable predictor of organismal phenotypes and that the metabolome provide novel insights into the underpinnings of complex traits and its genetic basis. 18 Here we elaborate on the findings by Zhou et al melanogaster. 23,24 NMR metabolomics constitute a highly reproducible technique that in contrast to mass spectrometry allows for metabolic profiling of the total complement of metabolites in a sample.…”

Section: Introductionmentioning

confidence: 89%

“…Thus, endophenotypes have been proposed to constitute proximal links between variation at the genome level and the organismal phenotype and they may provide more accurate predictors of the functional phenotype compared to the genotype. 15,18 We hypothesize, that the predictive value of endophenotypes for organismal functional phenotypes is linked to the proximity of the endophenotype to the organismal phenotype. Accepting this premise information on the metabolome level should provide more accurate prediction than e.g.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

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Understanding the genotype -phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins, metabolites. This facilitates our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We do this by performing NMR metabolomics on four replicate pools of male flies from each of 170 isogenic lines. We show first that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, we show that for four of five traits related to behaviour and stress resistance the metabolome predicts phenotypes more accurately than genomic data, and that the metabolites driving the increased accuracy was trait specific. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding has strong applied importance e.g. in human medicine and animal and plant breeding.1 is a design matrix linking the genomic ( 1 ) and metabolomic ( 1 ) effects to the phenotypes. The random genomic effects are 1~( , [1,1] ! " ), and the metabolomic effects are 1~( , [1,1] 5 " ), where is the additive genomic relationship matrix as specified previously, and is the metabolomic relationship matrix.The metabolomic relationship matrix was computed as = ′ NMR ⁄ , where is a × NMR matrix of *77 18* T 6 , N 6 ), and was compared (using paired t-test corrected for multiple testing by a false discovery rate (FDR) of <0.05) within and across 9/ clusters to identify the NMR features resulting in the largest prediction accuracy. We only considered Drosophila melanogaster Genetic Reference Panel lines.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of complex phenotypes using theDrosophilametabolome

Rohde

Kristensen

Sarup

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…eQTLs with both cis-and trans-effects can be assembled into directed transcriptional networks of regulator and target genes (Liu et al 2008;Bryois et al 2014;Fagny et al 2017). Elucidating such regulatory transcriptional networks will facilitate understanding how the effects of individual variants propagate through the network and how multiple variants together regulate gene expression and affect complex traits (Liu et al 2008;Nicolae et al 2010;Bryois et al 2014;Fagny et al 2017) and will improve genomic prediction (Zhou et al 2020).…”

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confidence: 99%

Gene expression networks in the Drosophila Genetic Reference Panel

et al. 2020

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A major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. This question is best addressed in a genetic mapping population in which all molecular polymorphisms are known and for which molecular endophenotypes and complex traits are assessed on the same genotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences and for which phenotypes of many quantitative traits have been evaluated. We mapped expression quantitative trait loci for annotated genes, novel transcribed regions, transposable elements, and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, as well as genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

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“…Combining eQTL studies with GWAS revealed that significant hits for many diseases are likely to be eQTLs, meaning that disease-associated SNPs presumably act by altering gene expression levels ( Nicolae et al 2010 ). As other types of omic data became available, studies mapping protein QTL (pQTL) ( Chick et al 2016 ) and metabolic QTL (mQTL) ( Kraus et al 2015 ; Zhou et al 2020 ) have helped elucidate how the effect of genetic variation percolates through intermediate molecular layers before affecting phenotypes.…”

mentioning

confidence: 99%

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

Morgante

Huang

Sørensen

et al. 2020

G3 Genes|Genomes|Genetics

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The ability to accurately predict complex trait phenotypes from genetic and genomic data is critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ~200 inbred lines of the Drosophilamelanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

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Systems genetics of the Drosophila metabolome

Cited by 21 publications

References 52 publications

Prediction of complex phenotypes using theDrosophilametabolome

Prediction of complex phenotypes using theDrosophilametabolome

Gene expression networks in the Drosophila Genetic Reference Panel

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

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