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Status and prospects of genome‐wide association studies in plants
Open accessAbstract
Genome‐wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome‐wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare‐variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
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Cited by
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Metadata
- Authors
- Tibbs Cortes, L., Zhang, Z., & Yu, J.
- Venue
- The Plant Genome
- Publisher
- Wiley
- Published
- 2021-01-13
- Volume
- 14
- Issue
- 1
- Pages
- e20077-e20077
- ISSN
- 1940-3372 (ISSN-L)
- OpenAlex
- W3120279605
- Cited by
- 470
- References
- 121
- FWCI
- 38.65
- 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
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.
