Evidence map›Paper›PMID 30874558›Full record

ArticleNature communications2019

Pervasive function and evidence for selection across standing genetic variation in S. cerevisiae.

Christopher M Jakobson, Richard She, Daniel F Jarosz

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

3 authors.

Christopher M JakobsonDepartment of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Richard SheDepartment of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Daniel F JaroszDepartment of Chemical & Systems Biology, Stanford University School of Medicine, Stanford, CA, 94305, USA. jarosz@stanford.edu.ORCID http://orcid.org/0000-0003-3497-5888

Funding

Protein-based Molecular Memories in Gene Regulation, Disease, and DevelopmentDP2GM119140 · NIGMS · STANFORD UNIVERSITY · PI JAROSZ, DANIEL · 2015 to 2015
$2.4M
Using self-templating proteins to spatiotemporally organize biochemistryF32GM125162 · NIGMS · STANFORD UNIVERSITY · PI JAKOBSON, CHRISTOPHER MATTHEW · 2017 to 2019
$179k
NIGMS NIH HHS DP2 GM119140NIGMS NIH HHS F32 GM125162
6 · The paper itself

Abstract

Quantitative genetics aims to map genotype to phenotype, often with the goal of understanding how organisms evolved. However, it remains unclear whether the genetic variants identified are exemplary of evolution. Here we analyzed progeny of two wild Saccharomyces cerevisiae isolates to identify 195 loci underlying complex metabolic traits, resolving 107 to single polymorphisms with diverse molecular mechanisms. More than 20% of causal variants exhibited patterns of emergence inconsistent with neutrality. Moreover, contrary to drift-centric expectation, variation in diverse wild yeast isolates broadly exhibited this property: over 30% of shared natural variants exhibited phylogenetic signatures suggesting that they are not neutral. This pattern is likely attributable to both homoplasy and balancing selection on ancestral polymorphism. Variants that emerged repeatedly were more likely to have done so in isolates from the same ecological niche. Our results underscore the power of super-resolution mapping of ecologically relevant traits in understanding adaptation and evolution.

Indexed as

Selection, GeneticAdaptation, BiologicalComputational BiologyEcosystemGenotypePhenotypePhylogenyPolymorphism, Single NucleotideQuantitative Trait LociSaccharomyces cerevisiae

Identifiers

PMID30874558
PMCPMC6420628

What OpenQuestion holds

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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.