Evidence map›Paper›PMID 30755686›Full record

ArticleScientific reports2019

Metabotyping as a Stopover in Genome-to-Phenome Mapping.

Pubudu P Handakumbura, Bryan Stanfill, Albert Rivas-Ubach, Dan Fortin, John P Vogel, Christer Jansson

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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  12. Genetic Variation for Seed Metabolite Levels inInternational journal of molecular sciences · 2019
    Article
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pubudu P HandakumburaThe Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Washington, WA, 99352, USA. pubudupinipa.handakumbura@pnnl.gov.
Bryan StanfillAdvanced Computing, Computing and Analytics Division, PNNL, Richland, WA, 99352, USA.ORCID 0000-0003-0612-5333
Albert Rivas-UbachThe Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Washington, WA, 99352, USA.
Dan FortinAdvanced Computing, Computing and Analytics Division, PNNL, Richland, WA, 99352, USA.
John P VogelUS Department of Energy (DOE) Joint Genome Institute (JGI), Walnut Creek, CA, 94598, USA.ORCID 0000-0003-1786-2689
Christer JanssonThe Environmental Molecular Sciences Laboratory (EMSL), Pacific Northwest National Laboratory (PNNL), Washington, WA, 99352, USA. christer.jansson@pnnl.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting phenotypic expression from genomic and environmental information is arguably the greatest challenge in today's biology. Being able to survey genomic content, e.g., as single-nucleotide polymorphism data, within a diverse population and predict the phenotypes of external traits, represents the holy grail across genome-informed disciplines, from personal medicine and nutrition to plant breeding. In the present study, we propose a two-step procedure in bridging the genome to phenome gap where external phenotypes are viewed as emergent properties of internal phenotypes, such as molecular profiles, in interaction with the environment. Using biomass accumulation and shoot-root allometry as external traits in diverse genotypes of the model grass Brachypodium distachyon, we established correlative models between genotypes and metabolite profiles (metabotypes) as internal phenotypes, and between metabotypes and external phenotypes under two contrasting watering regimes. Our results demonstrate the potential for employing metabotypes as an integrator in predicting external phenotypes from genomic information.

Indexed as

Chromosome MappingGenome, PlantGenotypePhenotypeAlgorithmsBiomassBrachypodiumGenetic Association StudiesGenomicsMass SpectrometryMetabolomicsPlant RootsPlant ShootsPolymorphism, Single NucleotidePrincipal Component Analysis

Identifiers

PMID30755686
PMCPMC6372633

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.