IC4R002-GWAS-2011-21915109 - Revision history

Zhennan: /* The Background of This Project */

2016-06-21T10:16:50Z

‎The Background of This Project

Zhennan: /* Research Findings */

2016-06-21T10:16:03Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T10:09:50Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T10:07:46Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T10:07:21Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T10:06:36Z

‎Research Findings

Zhennan: /* Corresponding Author */

2016-06-21T09:48:24Z

‎Corresponding Author

Zhennan: /* Corresponding Author */

2016-06-21T09:48:04Z

‎Corresponding Author

Zhennan: /* Corresponding Author */

2016-06-21T09:46:40Z

‎Corresponding Author

Zhennan: /* Labs working on this Project */

2016-06-21T09:45:57Z

‎Labs working on this Project

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 ==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.
+* 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==

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 [[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.'']]
 * 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.
 == Labs working on this Project ==

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 * 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.'']]
+* 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.
 == Labs working on this Project ==

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 ==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
+* 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.'']]

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 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|left|'''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: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.'']]
+*

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 ==Corresponding Author==
-'''Susan R. McCouch'''(srm4@cornell.edu)
+* '''Susan R. McCouch'''(srm4@cornell.edu)
-'''Carlos D. Bustamante'''(cdbustam@stanford.edu)
+* '''Carlos D. Bustamante'''(cdbustam@stanford.edu)

← Older revision		Revision as of 09:48, 21 June 2016
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	==Corresponding Author==		==Corresponding Author==
−	'''Carlos D. Bustamante '''(~~srm4~~@~~cornell~~.edu)	+	'''Susan R. McCouch'''(srm4@cornell.edu)
		+	'''Carlos D. Bustamante'''(cdbustam@stanford.edu)

← Older revision		Revision as of 09:46, 21 June 2016
Line 23:		Line 23:

	==Corresponding Author==		==Corresponding Author==
−	''' '''(srm4@cornell.edu)	+	'''Carlos D. Bustamante '''(srm4@cornell.edu)

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 == Labs working on this Project ==
+* Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14850, USA
 ==Corresponding Author==
 ''' '''(srm4@cornell.edu)