Difference between revisions of "IC4R002-GWAS-2011-21915109"
(Created page with "== Project Title == ''' Genetic Architecture of Aluminum Tolerance in Rice( Oryza sativa ) Determined through Genome-Wide Association Analysis and QTL Mapping ''' ==The Back...") |
(→The Background of This Project) |
||
| (13 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
== Project Title == | == Project Title == | ||
| − | ''' | + | ''' Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa ''' |
==The Background of This Project== | ==The Background of This Project== | ||
| − | + | * 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 identifi ed dozens of common variants infl uencing numerous complex traits. Signifi cant 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. | |
| − | * | ||
==Plant Culture & Treatment== | ==Plant Culture & Treatment== | ||
| − | * | + | * The Rice Diversity Panel consists of 413 Asian rice ( O. sativa ) cultivars, including many landraces, which originated from 82 countries, representing all the major rice-growing regions of the world. The panel contains 87 indica , 57 aus , 96 temperate japonica , 97 tropical japonica , 14 groupV/aromatic, and 62 highly admixed accessions. All accessions were purifi ed for two generations(single seed descent) before DNA extraction. In all, 20 of these 413 accessions were purifi ed as part of the Oryza SNP project. Six cultivars (Azucena, Moroberekan, Nipponbare, Dom-Sofi d, IR64, M-202) were purifi ed separately, once by Ali et al. and once as part of the Oryza SNP panel. Further information for each accession(accession name, accession number, country of origin and subpopulation ancestry based on PCA) is given in Supplementary Data 1 . |
==Research Findings== | ==Research Findings== | ||
| − | * | + | * A rice diversity panel consisting of 413 inbred accessions of O. sativa collected from 82 countries was genotyped using an Aff ymetrix single nucleotide polymorphism (SNP) array containing 44,100 SNPs (hereaft er referred to as the 44 K chip). With a genome size of ~ 380 Mb (ref. 13), this custom-designed genotyping chip provides high quality data (less than 4.5 % missing data), with ~ 1 SNP per 10 kb across the 12 chromosomes of rice. The diversity panel was evaluated for 34 traits related to plant morphology, grain quality, plant development and agronomic performance using fi eld-grown plants with replications within and between years.<br><br> |
| − | * | + | * Using principle component analysis (PCA) 14 to summarize global genetic variation in the diversity panel, we observed clear, deep subpopulation structure in this collection of germplasm( Figure. 1 ). The top four principal components (PCs) explained almost half of the genetic variation ( Fig. 1b ). Th e fi ve subpopulations indica , aus , temperate japonica , tropical japonica and aromatic formed clear clusters based on the top four PCs, and were well diff erentiated from each other, with pairwise Fst (F-statistic) values ranging from 0.23 – 0.53. Th is is in agreement with previous fi ndings where global germplasm collections have been used in combination with much smaller numbers of SNP or simple sequence repeat(SSR) genotypes 8,15 – 17 . Because the array was designed to assay variation in all O. sativa groups, most SNPs are shared or polymorphic across subpopulations. |
| − | * | + | [[File:IC4R002-GWAS-2011-21915109-1.PNG|1000px|thumb|center|'''Figure 1''' ''Principal component analysis was used to provide a statistical summary of the genetic data, and the top four principle components are illustrated in the bottom panels.'']] |
| − | [[File: | + | * The phenotypes we examined in our GWAS can be classifi ed broadly into six categories: plant morphology-related traits; yield-related traits; seed and grain morphology-related traits; stress-related phenotypes; cooking, eating and nutritional-quality-related traits; and plant development, represented by fl owering time, which we measured in three geographic locations that diff ered in day-length and ambient temperature. Canonical correlation analysis demonstrated that phenotypes within a category are oft en correlated, ranging from a low of − 0.41 between brown rice seed width and brown rice seed length, to a high of 0.9 between hulled and dehulled seed morphology. |
| + | * The results of our genome-wide association scans are summarized in Supplementary Figures S3 – S36 where we show SNP-trait associations discovered in the diversity panel as a whole, as well as in each subpopulation individually. As can be seen in the quantile–quantile plots ( Figure. 3 ), the distribution of observed − log10 P -values from the na ï ve analysis (no population structure adjustment) departed quite far from the expected distribution under a model of no association (that is, the P -values should lie on the diagonal line), with signifi cant infl ation of nominal P -values leading to a high level of false positive signals. Use of a modifi ed mixed model strategy 22 – 24 allowed us to consider diff erent levels of population structure and relatedness in our diversity panel. Th is eff ectively eliminated the excess of low P -values for most traits, but it also likely eliminated true positives. Th is is a common problem seen in other systems as well; for example, geographic coordinates correlate closely with fl owering time in plants 24 . For this reason, we believe a combination of na ï ve and population structure-adjusted hits, coupled with subpopulation-specifi c analyses in rice, is the most thoughtful way to identify potential variants for follow up. | ||
| + | [[File:IC4R002-GWAS-2011-21915109-2.PNG|500px|thumb|right|'''Figure 2''' ''Quantile – Quantile plots for both na ï ve and mixed model for plant height in all samples. '']] | ||
== Labs working on this Project == | == Labs working on this Project == | ||
| − | * Department of | + | * Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850, USA |
| − | * Department of | + | * Department of Genetics, Stanford University, Stanford, California 94305, USA |
| − | * | + | * Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14850, USA |
| + | * USDA ARS,Dale Bumpers National Rice Research Center, Stuttgart, Arkansas 72160, USA | ||
| + | * Rice Research and Extension Center, University of Arkansas, Stuttgart, Arkansas 72160, USA | ||
| + | * Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK | ||
| + | * Department of Soil Science,Bangladesh Agricultural University, Mymensingh 2202, Bangladesh. Correspondence and requests for materials should be addressed to S.R.M. | ||
==Corresponding Author== | ==Corresponding Author== | ||
| − | ''' Susan R. McCouch'''(srm4@cornell.edu) | + | * '''Susan R. McCouch'''(srm4@cornell.edu) |
| + | * '''Carlos D. Bustamante'''(cdbustam@stanford.edu) | ||
Latest revision as of 10:16, 21 June 2016
Contents
Project Title
Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa
The Background of This Project
- 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 identifi ed dozens of common variants infl uencing numerous complex traits. Signifi cant 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.
Plant Culture & Treatment
- The Rice Diversity Panel consists of 413 Asian rice ( O. sativa ) cultivars, including many landraces, which originated from 82 countries, representing all the major rice-growing regions of the world. The panel contains 87 indica , 57 aus , 96 temperate japonica , 97 tropical japonica , 14 groupV/aromatic, and 62 highly admixed accessions. All accessions were purifi ed for two generations(single seed descent) before DNA extraction. In all, 20 of these 413 accessions were purifi ed as part of the Oryza SNP project. Six cultivars (Azucena, Moroberekan, Nipponbare, Dom-Sofi d, IR64, M-202) were purifi ed separately, once by Ali et al. and once as part of the Oryza SNP panel. Further information for each accession(accession name, accession number, country of origin and subpopulation ancestry based on PCA) is given in Supplementary Data 1 .
Research Findings
- A rice diversity panel consisting of 413 inbred accessions of O. sativa collected from 82 countries was genotyped using an Aff ymetrix single nucleotide polymorphism (SNP) array containing 44,100 SNPs (hereaft er referred to as the 44 K chip). With a genome size of ~ 380 Mb (ref. 13), this custom-designed genotyping chip provides high quality data (less than 4.5 % missing data), with ~ 1 SNP per 10 kb across the 12 chromosomes of rice. The diversity panel was evaluated for 34 traits related to plant morphology, grain quality, plant development and agronomic performance using fi eld-grown plants with replications within and between years.
- Using principle component analysis (PCA) 14 to summarize global genetic variation in the diversity panel, we observed clear, deep subpopulation structure in this collection of germplasm( Figure. 1 ). The top four principal components (PCs) explained almost half of the genetic variation ( Fig. 1b ). Th e fi ve subpopulations indica , aus , temperate japonica , tropical japonica and aromatic formed clear clusters based on the top four PCs, and were well diff erentiated from each other, with pairwise Fst (F-statistic) values ranging from 0.23 – 0.53. Th is is in agreement with previous fi ndings where global germplasm collections have been used in combination with much smaller numbers of SNP or simple sequence repeat(SSR) genotypes 8,15 – 17 . Because the array was designed to assay variation in all O. sativa groups, most SNPs are shared or polymorphic across subpopulations.
- The phenotypes we examined in our GWAS can be classifi ed broadly into six categories: plant morphology-related traits; yield-related traits; seed and grain morphology-related traits; stress-related phenotypes; cooking, eating and nutritional-quality-related traits; and plant development, represented by fl owering time, which we measured in three geographic locations that diff ered in day-length and ambient temperature. Canonical correlation analysis demonstrated that phenotypes within a category are oft en correlated, ranging from a low of − 0.41 between brown rice seed width and brown rice seed length, to a high of 0.9 between hulled and dehulled seed morphology.
- The results of our genome-wide association scans are summarized in Supplementary Figures S3 – S36 where we show SNP-trait associations discovered in the diversity panel as a whole, as well as in each subpopulation individually. As can be seen in the quantile–quantile plots ( Figure. 3 ), the distribution of observed − log10 P -values from the na ï ve analysis (no population structure adjustment) departed quite far from the expected distribution under a model of no association (that is, the P -values should lie on the diagonal line), with signifi cant infl ation of nominal P -values leading to a high level of false positive signals. Use of a modifi ed mixed model strategy 22 – 24 allowed us to consider diff erent levels of population structure and relatedness in our diversity panel. Th is eff ectively eliminated the excess of low P -values for most traits, but it also likely eliminated true positives. Th is is a common problem seen in other systems as well; for example, geographic coordinates correlate closely with fl owering time in plants 24 . For this reason, we believe a combination of na ï ve and population structure-adjusted hits, coupled with subpopulation-specifi c analyses in rice, is the most thoughtful way to identify potential variants for follow up.
Labs working on this Project
- Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850, USA
- Department of Genetics, Stanford University, Stanford, California 94305, USA
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14850, USA
- USDA ARS,Dale Bumpers National Rice Research Center, Stuttgart, Arkansas 72160, USA
- Rice Research and Extension Center, University of Arkansas, Stuttgart, Arkansas 72160, USA
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen AB24 3UU, UK
- Department of Soil Science,Bangladesh Agricultural University, Mymensingh 2202, Bangladesh. Correspondence and requests for materials should be addressed to S.R.M.
Corresponding Author
- Susan R. McCouch(srm4@cornell.edu)
- Carlos D. Bustamante(cdbustam@stanford.edu)
