Literature DB >> 33510469

Prediction of complex phenotypes using the Drosophila melanogaster metabolome.

Palle Duun Rohde1, Torsten Nygaard Kristensen2,3, Pernille Sarup4,5, Joaquin Muñoz2, Anders Malmendal6.   

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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Year:  2021        PMID: 33510469      PMCID: PMC8102504          DOI: 10.1038/s41437-021-00404-1

Source DB:  PubMed          Journal:  Heredity (Edinb)        ISSN: 0018-067X            Impact factor:   3.821


  68 in total

1.  H NMR-based metabolite profiling as a potential selection tool for breeding passive resistance against Fusarium head blight (FHB) in wheat.

Authors:  Roy A Browne; Kevin M Brindle
Journal:  Mol Plant Pathol       Date:  2007-07       Impact factor: 5.663

2.  Metabolite identification via the Madison Metabolomics Consortium Database.

Authors:  Qiu Cui; Ian A Lewis; Adrian D Hegeman; Mark E Anderson; Jing Li; Christopher F Schulte; William M Westler; Hamid R Eghbalnia; Michael R Sussman; John L Markley
Journal:  Nat Biotechnol       Date:  2008-02       Impact factor: 54.908

3.  Central dogma of molecular biology.

Authors:  F Crick
Journal:  Nature       Date:  1970-08-08       Impact factor: 49.962

4.  Transcriptome-Based Prediction of Complex Traits in Maize.

Authors:  Christina B Azodi; Jeremy Pardo; Robert VanBuren; Gustavo de Los Campos; Shin-Han Shiu
Journal:  Plant Cell       Date:  2019-10-22       Impact factor: 11.277

5.  The genetic architecture of maize flowering time.

Authors:  Edward S Buckler; James B Holland; Peter J Bradbury; Charlotte B Acharya; Patrick J Brown; Chris Browne; Elhan Ersoz; Sherry Flint-Garcia; Arturo Garcia; Jeffrey C Glaubitz; Major M Goodman; Carlos Harjes; Kate Guill; Dallas E Kroon; Sara Larsson; Nicholas K Lepak; Huihui Li; Sharon E Mitchell; Gael Pressoir; Jason A Peiffer; Marco Oropeza Rosas; Torbert R Rocheford; M Cinta Romay; Susan Romero; Stella Salvo; Hector Sanchez Villeda; H Sofia da Silva; Qi Sun; Feng Tian; Narasimham Upadyayula; Doreen Ware; Heather Yates; Jianming Yu; Zhiwu Zhang; Stephen Kresovich; Michael D McMullen
Journal:  Science       Date:  2009-08-07       Impact factor: 47.728

Review 6.  Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology.

Authors:  Magda Bictash; Timothy M Ebbels; Queenie Chan; Ruey Leng Loo; Ivan K S Yap; Ian J Brown; Maria de Iorio; Martha L Daviglus; Elaine Holmes; Jeremiah Stamler; Jeremy K Nicholson; Paul Elliott
Journal:  J Clin Epidemiol       Date:  2010-01-08       Impact factor: 6.437

Review 7.  Systems genetics approaches to understand complex traits.

Authors:  Mete Civelek; Aldons J Lusis
Journal:  Nat Rev Genet       Date:  2013-12-03       Impact factor: 53.242

8.  The road less traveled: from genotype to phenotype in flies and humans.

Authors:  Robert R H Anholt; Trudy F C Mackay
Journal:  Mamm Genome       Date:  2017-10-20       Impact factor: 2.957

9.  Application of genomics tools to animal breeding.

Authors:  Jack C M Dekkers
Journal:  Curr Genomics       Date:  2012-05       Impact factor: 2.236

10.  The UK Biobank resource with deep phenotyping and genomic data.

Authors:  Clare Bycroft; Colin Freeman; Desislava Petkova; Gavin Band; Lloyd T Elliott; Kevin Sharp; Allan Motyer; Damjan Vukcevic; Olivier Delaneau; Jared O'Connell; Adrian Cortes; Samantha Welsh; Alan Young; Mark Effingham; Gil McVean; Stephen Leslie; Naomi Allen; Peter Donnelly; Jonathan Marchini
Journal:  Nature       Date:  2018-10-10       Impact factor: 49.962

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  2 in total

1.  Rapid Adjustments in Thermal Tolerance and the Metabolome to Daily Environmental Changes - A Field Study on the Arctic Seed Bug Nysius groenlandicus.

Authors:  Natasja Krog Noer; Mathias Hamann Sørensen; Hervé Colinet; David Renault; Simon Bahrndorff; Torsten Nygaard Kristensen
Journal:  Front Physiol       Date:  2022-02-16       Impact factor: 4.566

2.  Metabolomic spectra for phenotypic prediction of malting quality in spring barley.

Authors:  Xiangyu Guo; Ahmed Jahoor; Just Jensen; Pernille Sarup
Journal:  Sci Rep       Date:  2022-05-12       Impact factor: 4.996

  2 in total

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