Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa

By Keyan Zhao, Chih-Wei Tung, G. Eizenga, Mark H. Wright, M. L. Ali, Alkes L Price, G. Norton, S. Islam, Andy Reynolds, Jason G. Mezey and 3 more. Nature Communications, 2011

1,320 citations recorded by the source corpus.

Asian rice, Oryza sativa is a cultivated, inbreeding species that feeds over half of the world's population. Understanding the genetic basis of diverse physiological, developmental, and morphological traits provides the basis for improving yield, quality and sustainability of rice. Here we show the results of a genome-wide association study based on genotyping 44,100 SNP variants across 413 diverse accessions of O. sativa collected from 82 countries that were systematically phenotyped for 34 traits. Using cross-population-based mapping strategies, we identified dozens of common variants influencing numerous complex traits. Significant heterogeneity was observed in the genetic architecture associated with subpopulation structure and response to environment. This work establishes an open-source translational research platform for genome-wide association studies in rice that directly links molecular variation in genes and metabolic pathways with the germplasm resources needed to accelerate varietal development and crop improvement.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/201e342a6831886045545e4a5dbcbd3faccac1e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. G. Eizenga
2. M. Thomson
3. A. McClung
4. I. Mackay
5. Marco Scutari
6. Bin Han
7. G. Brown-Guedira
8. Xuehui Huang

In Plant Science

1. G. Eizenga
2. M. Thomson
3. S. Fondevilla
4. A. McClung
5. S. Chao
6. Diego Rubiales
7. J. Dubcovsky
8. Karam B. Singh

Citations in the corpus

• Achievements and Challenges in Legume Breeding for Pest and Disease Resistance (2015, background): “…offers now the opportunity of developing genome wide association mapping not only to find associations between traits and DNA polymorphisms but also to unravel the origin of genetic correlations among phenotypic traits, that is, pleiotropy versus genetically linked genes (Zhao et al., 2011).”
• Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array (2014, methodology): “High-density SNP arrays have been developed for a number of economically important crops and animals (Ganal et al., 2011; Sim et al., 2012; Song et al., 2013; Wiedmann et al., 2008; Zhao et al., 2011) and successfully used for genetic studies.”
• High-Throughput SNP Genotyping to Accelerate Crop Improvement (2014, methodology): “2013c) and an Affymetrix 44K SNP chip (Zhao et al. 2011).”
• Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement (2013, background): “…when working with traits with low heritability and complex inheritance (Calus et al. 2008; Guo et al. 2012; Hayes et al. 2009; Heffner et al. 2010; Jia and Jannink 2012; Kumar et al. 2012; Nakaya and Isobe 2012; Munoz et al. 2011; Resende et al. 2012; Zhao et al. 2011b; Zhong et al. 2009).”
• High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform (2011, methodology): “…chip with 44,100 SNPs has also been developed using OryzaSNP loci in combination with SNPs from bacterial artificial chromosome (BAC)-end sequences from the Oryza Map Alignment Project (OMAP), of which over 90% passed quality criteria when genotyped on over 400 rice accessions (Zhao et al. 2011).”
• Natural variations and genome-wide association studies in crop plants. (2014, methodology): “Microarraybased genotyping has been applied in crops such as rice and maize (26, 61, 72, 136).”
• Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, background): “It has been shown that the growth of Arabidopsis plants follows the logistic model (Paul-Victor et al., 2010; Züst et al., 2011; Tessmer et al., 2013), while the growth of maize ( Zea mays ) kernels prefers to fi t the Gompertz model (Meade et al., 2013).”
• LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms (2015, methodology): “Arrays are widely used for GWA studies in humans (Stranger et al. 2011) and have also been developed for several agricultural species [ e.g. , cattle (Matukumalli et al. 2009), rice (Zhao et al. 2011), grape (Myles et al. 2010), and apple (Chagné et al. 2012)].”
• Worldwide Genetic Diversity for Mineral Element Concentrations in Rice Grain (2015, neutral): “Among the five subgroup or ancestral lineages of rice (Garris et al., 2005; Zhao et al., 2011; Ren et al., 2013), this study included 578 accessions of the indica (IND) accessions, 412 temperate japonica (TEJ) accessions, 358 tropical japonicas (TRJ), 169 aus (AUS), 63 aromatic (ARO) accessions, and”
• Using Genetic Distance to Infer the Accuracy of Genomic Prediction (2015, background): “Predictions will be poor in practice if the target and the training populations are too genetically distinct; an example are rice subspecies [17], which have been subject to intensive inbreeding.”