Evidence map›Paper›PMID 24832509›Full record

ArticleBiology2012

The Population Genomics of Sunflowers and Genomic Determinants of Protein Evolution Revealed by RNAseq.

Sébastien Renaut, Christopher J Grassa, Brook T Moyers, Nolan C Kane, Loren H Rieseberg

Open access · goldAbstract read
In one paragraph

Article in Biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

10 citing papers in PubMed, 39 citations in OpenAlex.

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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

5 authors at 2 institutions in 2 countries.

Sébastien RenautDepartment of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada. sebastien.renaut@gmail.com.
Christopher J GrassaDepartment of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.
Brook T MoyersDepartment of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.
Nolan C KaneDepartment of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.
Loren H RiesebergDepartment of Botany, University of British Columbia, 3529-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.
University of British Columbia · CAIndiana University Bloomington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Few studies have investigated the causes of evolutionary rate variation among plant nuclear genes, especially in recently diverged species still capable of hybridizing in the wild. The recent advent of Next Generation Sequencing (NGS) permits investigation of genome wide rates of protein evolution and the role of selection in generating and maintaining divergence. Here, we use individual whole-transcriptome sequencing (RNAseq) to refine our understanding of the population genomics of wild species of sunflowers (Helianthus spp.) and the factors that affect rates of protein evolution. We aligned 35 GB of transcriptome sequencing data and identified 433,257 polymorphic sites (SNPs) in a reference transcriptome comprising 16,312 genes. Using SNP markers, we identified strong population clustering largely corresponding to the three species analyzed here (Helianthus annuus, H. petiolaris, H. debilis), with one distinct early generation hybrid. Then, we calculated the proportions of adaptive substitution fixed by selection (alpha) and identified gene ontology categories with elevated values of alpha. The "response to biotic stimulus" category had the highest mean alpha across the three interspecific comparisons, implying that natural selection imposed by other organisms plays an important role in driving protein evolution in wild sunflowers. Finally, we examined the relationship between protein evolution (dN/dS ratio) and several genomic factors predicted to co-vary with protein evolution (gene expression level, divergence and specificity, genetic divergence [FST], and nucleotide diversity pi). We find that variation in rates of protein divergence was correlated with gene expression level and specificity, consistent with results from a broad range of taxa and timescales. This would in turn imply that these factors govern protein evolution both at a microevolutionary and macroevolutionary timescale. Our results contribute to a general understanding of the determinants of rates of protein evolution and the impact of selection on patterns of polymorphism and divergence.

Identifiers

PMID24832509
PMCPMC4009819
OpenAlexW2050047813

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.