Evidence map›Paper›PMID 34018019›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2021

Genetic architecture of root and shoot ionomes in rice (Oryza sativa L.).

Joshua N Cobb, Chen Chen, Yuxin Shi, Lyza G Maron, Danni Liu, Mike Rutzke, Anthony Greenberg, Eric Craft, Jon Shaff, Edyth Paul and 6 more

Open access · hybridAbstract read
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

7 citing papers in PubMed, 12 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

16 authors at 5 institutions in 4 countries.

Joshua N CobbPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Chen ChenDepartment of Statistics, Purdue University, West Lafayette, IN, 47907-2054, USA.
Yuxin ShiPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Lyza G MaronPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Danni LiuDepartment of Statistics, Purdue University, West Lafayette, IN, 47907-2054, USA.
Mike RutzkeSoil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Anthony GreenbergPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Eric CraftSoil and Crop Sciences Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Jon ShaffRobert W. Holley Center for Agriculture and Health, United States Department of Agriculture-Agricultural Research Service, Ithaca, NY, 14853-1901, USA.
Edyth PaulGeneFlow, Inc, Centreville, VA, 20120, USA.
Kazi AktherPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Shaokui WangPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA.
Leon V KochianRobert W. Holley Center for Agriculture and Health, United States Department of Agriculture-Agricultural Research Service, Ithaca, NY, 14853-1901, USA.
Dabao ZhangDepartment of Statistics, Purdue University, West Lafayette, IN, 47907-2054, USA.
Min ZhangDepartment of Statistics, Purdue University, West Lafayette, IN, 47907-2054, USA. minzhang@purdue.edu.ORCID http://orcid.org/0000-0002-0164-1948
Susan R McCouchPlant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853-1901, USA. srm4@cornell.edu.ORCID http://orcid.org/0000-0001-9246-3106
Cornell University · USPurdue University West Lafayette · USUnited States Department of Agriculture · USGenex Systems (United States) · USSouth China Agricultural University · CN

Funding

Modeling Homeostasis of Human Blood MetabolitesR01GM131491 · NIGMS · UNIVERSITY OF WASHINGTON · PI RAFTERY, DANIEL, ZHANG, DABAO · 2020 to 2023
$2.2M
New Statistical Methods to Model Metabolite Profiles for Disease DetectionR03CA211831 · NCI · PURDUE UNIVERSITY · PI ZHANG, MIN · 2016 to 2016
$171k
Measuring Explained Variation in Survival AnalysisR03CA235363 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI ZHANG, DABAO · 2019 to 2020
$143k
National Science Foundation 0606461National Science Foundation 1026555NIGMS NIH HHS R01 GM131491NIH HHS R01GM131491NIH HHS R03CA211831NIH HHS R03CA235363U.S. Department of Agriculture 2010-65114-20453U.S. Department of Agriculture 2013-67013-21379
6 · The paper itself

Abstract

key messageAssociation analysis for ionomic concentrations of 20 elements identified independent genetic factors underlying the root and shoot ionomes of rice, providing a platform for selecting and dissecting causal genetic variants. Understanding the genetic basis of mineral nutrient acquisition is key to fully describing how terrestrial organisms interact with the non-living environment. Rice (Oryza sativa L.) serves both as a model organism for genetic studies and as an important component of the global food system. Studies in rice ionomics have primarily focused on above ground tissues evaluated from field-grown plants. Here, we describe a comprehensive study of the genetic basis of the rice ionome in both roots and shoots of 6-week-old rice plants for 20 elements using a controlled hydroponics growth system. Building on the wealth of publicly available rice genomic resources, including a panel of 373 diverse rice lines, 4.8 M genome-wide single-nucleotide polymorphisms, single- and multi-marker analysis pipelines, an extensive tome of 321 candidate genes and legacy QTLs from across 15 years of rice genetics literature, we used genome-wide association analysis and biparental QTL analysis to identify 114 genomic regions associated with ionomic variation. The genetic basis for root and shoot ionomes was highly distinct; 78 loci were associated with roots and 36 loci with shoots, with no overlapping genomic regions for the same element across tissues. We further describe the distribution of phenotypic variation across haplotypes and identify candidate genes within highly significant regions associated with sulfur, manganese, cadmium, and molybdenum. Our analysis provides critical insight into the genetic basis of natural phenotypic variation for both root and shoot ionomes in rice and provides a comprehensive resource for dissecting and testing causal genetic variants.

Indexed as

Gene Expression Regulation, PlantChromosome MappingChromosomes, PlantGenome-Wide Association StudyOryzaPhenotypePlant ProteinsPlant RootsPlant ShootsQuantitative Trait LociPlant ProteinsCandidate geneGenome-wide association (GWA)HaplotypeIonMicronutrientMulti-marker analysisQTL

Identifiers

PMID34018019
PMCPMC8277617
OpenAlexW3175915861

What OpenQuestion holds

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LicenceCC BY
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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.