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Research article
Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods
Zhaohua Peng
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6047-8434
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Plant Biology.
Background
Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers.
Results
To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value <1.53×10-8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~20%) of the 250 QTLs were coinciding with previous reported QTLs/genes, about 201 (~80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods.
Conclusions
While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.
Keywords
rice, ionomic traits, agronomic traits, multivariate GWAS
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CURRENT STATUS: Published
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Posted 14 Aug, 2020
Published
On 24 Sep, 2020
No community comments so far
Editorial decision: Accept
On 14 Aug, 2020
Editor assigned
On 12 Aug, 2020
Submission checks complete
On 11 Aug, 2020
Editor invited
On 11 Aug, 2020
No comments provided
Review #2 received
Received 28 Jun, 2020
Editorial decision: Minor revision
On 28 Jun, 2020
Review #1 received
Received 15 May, 2020
Reviewers invited
Invitations sent on 23 Apr, 2020
Reviewer #1 agreed
On 23 Apr, 2020
Reviewer #2 agreed
On 23 Apr, 2020
Editor assigned
On 22 Apr, 2020
Submission checks complete
On 21 Apr, 2020
Editor invited
On 21 Apr, 2020
Version (no preprint)
Posted 05 May, 2020
Review #2 received
Received 03 Aug, 2020
Editorial decision: Minor revision
On 03 Aug, 2020
Review #1 received
Received 02 Aug, 2020
Reviewer #2 agreed
On 16 Jul, 2020
Reviewer #1 agreed
On 15 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Editor assigned
On 12 Jul, 2020
Submission checks complete
On 11 Jul, 2020
Editor invited
On 11 Jul, 2020
This version was not preprinted
No comments provided
Review #1 received
Received 03 Mar, 2020
Editorial decision: Major revision
On 03 Mar, 2020
Reviewer #2 agreed
On 21 Feb, 2020
Review #2 received
Received 21 Feb, 2020
Reviewers invited
Invitations sent on 17 Feb, 2020
Reviewer #1 agreed
On 17 Feb, 2020
Editor assigned
On 20 Jan, 2020
Submission checks complete
On 19 Jan, 2020
Editor invited
On 19 Jan, 2020
First submitted
On 16 Jan, 2020
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This preprint is under consideration at BMC Plant Biology. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Genome-wide association studies of ionomic and agronomic traits in USDA mini core collection of rice and comparative analyses of different mapping methods
Zhaohua Peng
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6047-8434
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 3
Posted 14 Aug, 2020
Published
On 24 Sep, 2020
No community comments so far
Editorial decision: Accept
On 14 Aug, 2020
Editor assigned
On 12 Aug, 2020
Submission checks complete
On 11 Aug, 2020
Editor invited
On 11 Aug, 2020
No comments provided
Review #2 received
Received 28 Jun, 2020
Editorial decision: Minor revision
On 28 Jun, 2020
Review #1 received
Received 15 May, 2020
Reviewers invited
Invitations sent on 23 Apr, 2020
Reviewer #1 agreed
On 23 Apr, 2020
Reviewer #2 agreed
On 23 Apr, 2020
Editor assigned
On 22 Apr, 2020
Submission checks complete
On 21 Apr, 2020
Editor invited
On 21 Apr, 2020
Version (no preprint)
Posted 05 May, 2020
Review #2 received
Received 03 Aug, 2020
Editorial decision: Minor revision
On 03 Aug, 2020
Review #1 received
Received 02 Aug, 2020
Reviewer #2 agreed
On 16 Jul, 2020
Reviewer #1 agreed
On 15 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Editor assigned
On 12 Jul, 2020
Submission checks complete
On 11 Jul, 2020
Editor invited
On 11 Jul, 2020
This version was not preprinted
No comments provided
Review #1 received
Received 03 Mar, 2020
Editorial decision: Major revision
On 03 Mar, 2020
Reviewer #2 agreed
On 21 Feb, 2020
Review #2 received
Received 21 Feb, 2020
Reviewers invited
Invitations sent on 17 Feb, 2020
Reviewer #1 agreed
On 17 Feb, 2020
Editor assigned
On 20 Jan, 2020
Submission checks complete
On 19 Jan, 2020
Editor invited
On 19 Jan, 2020
First submitted
On 16 Jan, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Plant Biology.
Background
Rice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers.
Results
To identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value <1.53×10-8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~20%) of the 250 QTLs were coinciding with previous reported QTLs/genes, about 201 (~80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods.
Conclusions
While 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5