The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals
Open accessAbstract
The plant metabolome is characterized by extensive diversity and is often regarded as a bridge between genome and phenome. Here we report metabolic and phenotypic genome-wide studies (mGWAS and pGWAS) in rice grain that, in addition to previous metabolic GWAS in rice leaf and maize kernel, show both distinct and overlapping aspects of genetic control of metabolism within and between species. We identify new candidate genes potentially influencing important metabolic and/or morphological traits. We show that the differential genetic architecture of rice metabolism between different tissues is in part determined by tissue specific expression. Using parallel mGWAS and pGWAS we identify new candidate genes potentially responsible for variation in traits such as grain colour and size, and provide evidence of metabotype-phenotype linkage. Our study demonstrates a powerful strategy for interactive functional genomics and metabolomics in plants, especially the cloning of minor QTLs for complex phenotypic traits.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines
Atwell, S., Huang, Y. S., Vilhjálmsson, B. J., Willems, G., Horton, M., Li, Y., Meng, D., Platt, A., Tarone, A. M., Hu, T. T., et al. (2010) · Nature · doi:10.1038/nature08800 · Cited by 1,780
FaST linear mixed models for genome-wide association studies
Lippert, C., Listgarten, J., Liu, Y., Kadie, C. M., Davidson, R. I., & Heckerman, D. (2011) · Nature Methods · doi:10.1038/nmeth.1681 · Cited by 1,209
Human metabolic individuality in biomedical and pharmaceutical research
CARDIoGRAM, Suhre, K., Shin, S.-Y., Petersen, A.-K., Mohney, R. P., Meredith, D., Wägele, B., Altmaier, E., Deloukas, P., Erdmann, J., et al. (2011) · Nature · doi:10.1038/nature10354 · Cited by 1,017
Li, M.-X., Yeung, J. M. Y., Cherny, S. S., & Sham, P. C. (2012) · Human Genetics · doi:10.1007/s00439-011-1118-2 · Cited by 962
Metabolomics-assisted breeding: a viable option for crop improvement?
Fernie, A. R., & Schauer, N. (2009) · Trends in Genetics · doi:10.1016/j.tig.2008.10.010 · Cited by 550
The genetics of plant metabolism
Keurentjes, J. J. B., Fu, J., de Vos, C. H. R., Lommen, A., Hall, R. D., Bino, R. J., van der Plas, L. H. W., Jansen, R. C., Vreugdenhil, D., & Koornneef, M. (2006) · Nature Genetics · doi:10.1038/ng1815 · Cited by 499
Genetical metabolomics: closing in on phenotypes
Keurentjes, J. J. (2009) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2008.12.003 · Cited by 139
Cited by
Jošt, M., Kegelmann, L., Korte, L., & Albrecht, S. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201904102 · Cited by 538
Status and prospects of genome‐wide association studies in plants
Tibbs Cortes, L., Zhang, Z., & Yu, J. (2021) · The Plant Genome · doi:10.1002/tpg2.20077 · Cited by 470
Metabolomics for Plant Improvement: Status and Prospects
Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01302 · Cited by 328
Metabolomics: A Way Forward for Crop Improvement
Razzaq, A., Sadia, B., Raza, A., Khalid Hameed, M., & Saleem, F. (2019) · Metabolites · doi:10.3390/metabo9120303 · Cited by 253
Metadata
- Authors
- Chen, W., Wang, W., Peng, M., Gong, L., Gao, Y., Wan, J., Wang, S., Shi, L., Zhou, B., Li, Z., Peng, X., Yang, C., Qu, L., Liu, X., & Luo, J.
- Venue
- Nature Communications
- Published
- 2016-10-04
- Volume
- 7
- Issue
- 1
- Pages
- 12767-12767
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2529689461
- Cited by
- 291
- References
- 7
- FWCI
- 11.74
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. https://app.cochat.ai/papers/10.1038/ncomms12767
- The paper
- Chen, W., Wang, W., Peng, M., Gong, L., Gao, Y., Wan, J., Wang, S., Shi, L., Zhou, B., Li, Z., Peng, X., Yang, C., Qu, L., Liu, X., & Luo, J. (2016). Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals. Nature Communications. https://doi.org/10.1038/ncomms12767
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
