Evidence map›Paper›PMID 30400815›Full record

ArticleBMC genetics2018

Exploring the genetic basis of gene transcript abundance and metabolite levels in loblolly pine (Pinus taeda L.) using association mapping and network construction.

Mengmeng Lu, Candace M Seeve, Carol A Loopstra, Konstantin V Krutovsky

Open access · diamondAbstract read
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Article in BMC genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.6field-weighted citation impact, top 33% 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

5 citing papers in PubMed, 15 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

4 authors at 3 institutions in 4 countries.

Mengmeng LuDepartment of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA.
Candace M SeeveUSDA-ARS Midwest Area, Columbia, MO, 65211, USA.
Carol A LoopstraDepartment of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA.
Konstantin V KrutovskyDepartment of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX, 77843-2138, USA. konstantin.krutovsky@forst.uni-goettingen.de.ORCID 0000-0002-8819-7084
Texas A&M University · USUniversity of Calgary · CAVavilov Institute of General Genetics · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying genetic variations that shape important complex traits is fundamental to the genetic improvement of important forest tree species, such as loblolly pine (Pinus taeda L.), which is one of the most commonly planted forest tree species in the southern U.S. Gene transcripts and metabolites are important regulatory intermediates that link genetic variations to higher-order complex traits such as wood development and drought response. A few prior studies have associated intermediate phenotypes including mRNA expression and metabolite levels with a limited number of molecular markers, but the identification of genetic variations that regulate intermediate phenotypes needs further investigation.

resultsWe identified 1841 single nucleotide polymorphisms (SNPs) associated with 191 gene expression mRNA phenotypes and 524 SNPs associated with 53 metabolite level phenotypes using 2.8 million exome-derived SNPs. The identified SNPs reside in genes with a wide variety of functions. We further integrated the identified SNPs and the associated expressed genes and metabolites into networks. We described the SNP-SNP interactions that significantly impacted the gene transcript abundance and metabolite level in the networks. Key loci and genes in the wood development and drought response networks were identified and analyzed.

conclusionsThis work provides new candidate genes for research on the genetic basis of gene expression and metabolism linked to wood development and drought response in loblolly pine and highlights the efficiency of using association-mapping-based networks to discover candidate genes with important roles in complex biological processes.

Indexed as

Gene Expression Regulation, PlantMetabolomeDroughtsGene Regulatory NetworksGenotypeLinkage DisequilibriumPhenotypePinus taedaPlant ProteinsPolymorphism, Single NucleotideTranscription FactorsPlant ProteinsTranscription FactorsEpistasisGene expressionMetabolismSNPStress responseWood development

Identifiers

PMID30400815
PMCPMC6219081
OpenAlexW2899943227

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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.