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Prediction of complex phenotypes using the Drosophila melanogaster metabolome

Abstract

Understanding the genotype–phenotype map and how variation at different levels of biological organization is 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 and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show 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, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. 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 is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.

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Fig. 1: Conceptual illustration of the NMR cluster-guided phenotypic predictions.
Fig. 2: Genetic variation for the D. melanogaster metabolome.
Fig. 3: Prediction accuracies using genomic and metabolomic data.
Fig. 4: Contributions to predictive clusters from the metabolite NMR spectra at the Kcl = 200 level.

Data availability

The DGRP genotypes, chromosomal inversions, Wolbachia infection status, and the phenotypic values for startle response, starvation resistance and chill coma recovery can be obtained from http://dgrp2.gnets.ncsu.edu/, and the locomotor activity measurements can be obtained from the original publication (Rohde et al. 2019). The raw metabolomic data can be obtained from MetaboLights under accession number MTBLS2060.

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Acknowledgements

The DGRP lines were obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537, http://flystocks.bio.indiana.edu). We thank Helle Blendstrup, Susan Marie Hansen and Michael Ørsted from Aalborg University for assistance in fly maintenance and sample collection. All of the computing for this project was performed on the GenomeDK cluster. We would like to thank GenomeDK and Aarhus University for providing computational resources and support that enabled us to perform the analyses presented in the paper. The authors thank Anders Pedersen at the Swedish NMR Center at the University of Gothenburg for help with sample preparation and experimental setup and for access to the 800 MHz spectrometer. The study was supported by the Danish Natural Science Research Council through a grant to TNK (DFF-8021-00014B), and by a grant from the Lundbeck Foundation to PDR (R287-2018-735).

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Correspondence to Palle Duun Rohde or Anders Malmendal.

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Rohde, P.D., Kristensen, T.N., Sarup, P. et al. Prediction of complex phenotypes using the Drosophila melanogaster metabolome. Heredity 126, 717–732 (2021). https://doi.org/10.1038/s41437-021-00404-1

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