The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
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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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
Self-citationPrincipal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
TASSEL: software for association mapping of complex traits in diverse samples
Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., & Buckler, E. S. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm308 · Cited by 7,759
Self-citationdbSNP: the NCBI database of genetic variation
Sherry, S. T. (2001) · Nucleic Acids Research · doi:10.1093/nar/29.1.308 · Cited by 7,502
The International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057
Cited by
Atta, K., Mondal, S., Gorai, S., Singh, A. P., Kumari, A., Ghosh, T., Roy, A., Hembram, S., Gaikwad, D. J., Mondal, S., et al. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1241736 · Cited by 523
DNA language models are powerful predictors of genome-wide variant effects
Benegas, G., Batra, S. S., & Song, Y. S. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311219120 · Cited by 157
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
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