IC4R002-Microarray-2011-21915109 - Revision history

Xysj1988: /* Research Findings */

2016-06-22T11:56:06Z

‎Research Findings

Xysj1988: /* Plant Materials & Treatment */

2016-06-22T11:55:14Z

‎Plant Materials & Treatment

Xysj1988 at 11:53, 22 June 2016

2016-06-22T11:53:48Z

Xysj1988: /* The Background of This Project */

2016-06-22T11:52:49Z

‎The Background of This Project

Xysj1988: /* The Background of This Project */

2016-06-22T11:51:07Z

‎The Background of This Project

Xysj1988: /* The Background of This Project */

2016-06-22T11:50:43Z

‎The Background of This Project

Xysj1988: /* The Background of This Project */

2016-06-22T11:50:30Z

‎The Background of This Project

Xysj1988: /* Research Findings */

2016-06-22T11:43:52Z

‎Research Findings

Xysj1988: /* Research Findings */

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‎Research Findings

Xysj1988: /* Plant Materials & Treatment */

2016-06-22T11:36:25Z

‎Plant Materials & Treatment

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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 ( Fig. 1a ). Th e 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 vari- ation in all O. sativa groups, most SNPs are shared or polymorphic across subpopulations.
 * We examined allele sharing across the panel by calculating 'identity by state' coefficients among all pairs of accessions ( Fig. 2a ). The researchers find that whereas allele sharing clearly tracks subpopulation ancestry as identifi ed by the PCA analysis, there is also a substantial number of admixed accessions, highlighting the complex history of rice varieties grown throughout the world 16 . Excluding the small sample of aromatic accessions, the mean observed identical by state (IBS) sharing is greatest between the closely related tropical japonica and temperate japonica accessions (0.80), followed by indica and aus (0.64), with relatively little IBS sharing between the two major subspecies, Indica and Japonica (0.47) ( Fig. 2a ). Th e fact that most of the admixture occurs within (rather than between) subspecies underscores the existence of genetic and cultural barriers to genetic exchange between these two major groups of Asian rice, despite documented cases of targeted Japonica-Indica introgression medi- ated by artifi cial selection.
 * 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 flowering 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 (Fig. 2b). For all the phenotypes evaluated in this study, we observed global similarities among members of the same subpopulation, consistent with the domestication and breeding history of these varieties. Correlation coeffi cients between accession pairs across all phenotypes were signifi cantly higher for accession pairs from the same subpopulation than from diff erent subpopulations ( P < 2.2e − 16, one-sided Mann – Whitney U -test) (lower triangle of Fig. 2a ). Consistent with this observation, the top four PCs (based on the 44 K SNPs men- tioned above) explained a large proportion of phenotypic variation, with values ranging from 20-40 %. In the case of rice grain, morphological and cooking-quality traits are key to varietal identity and have been under strong diversifying selection by humans in diff erent parts of the world 18 – 21. Physical grain characteristics in rice are salient because they serve as indicators of local and regional eating prefe rences in a crop that, unlike wheat or maize, is consumed largely as whole kernel. Traits such as fl owering time and disease resistance are also strongly correlated with region and environment, meaning that genotypic, phenotypic and environmental variation in O. sativa are all correlated to some degree, posing signifi cant challenges for GWAS.

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 [[File:IC4R002-Microarray-2011-21915109-3.png|center|thumb|870px|]]
 ==Plant Materials & 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 15 . Th e 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 6 . Six cultivars (Azucena, Moroberekan, Nipponbare, Dom-Sofi d, IR64, M-202) were purifi ed separately,and once as part of the Oryza SNP panel.
 ==Research Findings==
 * A rice diversity panel consisting of 413 inbred accessions of O. sativa collected from 82 countries ( Fig. 1 ; Supplementary Data 1 ) 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. Th e diversity panel was evaluated for 34 traits related to plant morphology, grain quality, plant develop- ment and agronomic performance using fi eld-grown plants with replications within and between years.

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 <br>
 * '''In this project, the researchers  present a genome-wide association study in a global collection of 413 diverse rice ( O. sativa ) varieties from 82 countries using a high-quality custom-designed 44,100 oligonucleotide genotyping array. For these varieties, we systematically phenotyped 34 morphological, developmental and agronomic traits over two consecutive fi eld seasons. Our mapping strategy evaluated variation both within and among four of the major subgroups of rice, revealing significant heterogeneity of genetic architecture among groups, as well as gene-by-environment eff ects. Unlike previous GWAS studies in rice 5 , purifi ed seed stocks of the rice strains and all the genotypic and phenotypic information generated over th course of this study are publicly available, creating a valuable, open source translational research platform that can be rapidly expanded through community participation to enhance the power and resolution of GWAS in rice.'''
+[[File:IC4R002-Microarray-2011-21915109-3.png|center|thumb|870px|]]
 ==Plant Materials & 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 15 . Th e 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 6 . Six cultivars (Azucena, Moroberekan, Nipponbare, Dom-Sofi d, IR64, M-202) were purifi ed separately,and once as part of the Oryza SNP panel.

@@ Line 5: / Line 5: @@
 * Understanding the genetic basis of physiological, developmental and morphological variation in domesticated Asian rice ( Oryza sativa ) is critical for improving the quality, safety, reliability and sustainability of the world ’ s food supply. Human population growth, particularly in developing countries where rice is the main source of caloric intake 1 , coupled with climate change and the intensive water, land and labour requirements of rice cultivation 2 , creates a pressing and continuous global need for new, stress tolerant, resource-use effi cient, and highly productive rice varieties. To assist in this endeavour, the scientifi c community has created a wealth of genomic and plant breeding resources, including high-quality genome sequences 3,4 , dense SNP maps 5-7 ,extensive germplasm collections 6,8,9 and public databases of genomic information.
 <br>
-[[File:IC4R002-Microarray-2011-21915109-1.png|center|thumb|870px|'''Figure 1 Population structure in O. sativa . ( a ) The large pie chart summarizes the distribution of subpopulations in the 413 O. sativa samples in our diversity panel, and the smaller pie charts on the world map correspond to the country-specifi c distribution of subpopulations sampled (note: large countries such as China, India and the US were divided into several major rice growing regions). The size of the pie chart is proportional to the sample size and colours within each pie chart are refl ective of the percentage of samples in each subpopulation. Seeds representing each subpopulation are displayed with and without hull in the centre, with 1 cm scale bar.''']]
+[[File:IC4R002-Microarray-2011-21915109-1.png|center|thumb|870px|'''Figure 1-a. Population structure in O. sativa . ( a ) The large pie chart summarizes the distribution of subpopulations in the 413 O. sativa samples in our diversity panel, and the smaller pie charts on the world map correspond to the country-specifi c distribution of subpopulations sampled (note: large countries such as China, India and the US were divided into several major rice growing regions). The size of the pie chart is proportional to the sample size and colours within each pie chart are refl ective of the percentage of samples in each subpopulation. Seeds representing each subpopulation are displayed with and without hull in the centre, with 1 cm scale bar.''']]
 <br>
 * Despite the availability of these scientifi c resources, most of what we know about the genetic architecture of complex traits in rice is based on traditional quantitative trait locus (QTL) linkage mapping using bi-parental populations. While providing valuable insights 12 , the QTL approach is clearly not ‘ scalable ’ to investigate the genomic potential and tremendous phenotypic variation of the more than 120,000 accessions available in public germplasm reposi- tories. Genome-wide association study (GWAS) mapping makes it possible to simultaneously screen a very large number of accessions for genetic variation underlying diverse complex traits. An extra advantage of the GWAS design for rice is the homozygous nature of most rice varieties, which makes it possible to employ a genotype or sequence once and phenotype many times over strategy, whereby once the lines are genomically characterized, the genetic data can be reused many times over across diff erent phenotypes and environments.
 * '''In this project, the researchers  present a genome-wide association study in a global collection of 413 diverse rice ( O. sativa ) varieties from 82 countries using a high-quality custom-designed 44,100 oligonucleotide genotyping array. For these varieties, we systematically phenotyped 34 morphological, developmental and agronomic traits over two consecutive fi eld seasons. Our mapping strategy evaluated variation both within and among four of the major subgroups of rice, revealing significant heterogeneity of genetic architecture among groups, as well as gene-by-environment eff ects. Unlike previous GWAS studies in rice 5 , purifi ed seed stocks of the rice strains and all the genotypic and phenotypic information generated over th course of this study are publicly available, creating a valuable, open source translational research platform that can be rapidly expanded through community participation to enhance the power and resolution of GWAS in rice.'''

@@ Line 4: / Line 4: @@
 ==The Background of This Project==
 * Understanding the genetic basis of physiological, developmental and morphological variation in domesticated Asian rice ( Oryza sativa ) is critical for improving the quality, safety, reliability and sustainability of the world ’ s food supply. Human population growth, particularly in developing countries where rice is the main source of caloric intake 1 , coupled with climate change and the intensive water, land and labour requirements of rice cultivation 2 , creates a pressing and continuous global need for new, stress tolerant, resource-use effi cient, and highly productive rice varieties. To assist in this endeavour, the scientifi c community has created a wealth of genomic and plant breeding resources, including high-quality genome sequences 3,4 , dense SNP maps 5-7 ,extensive germplasm collections 6,8,9 and public databases of genomic information.
 [[File:IC4R002-Microarray-2011-21915109-1.png|center|thumb|870px|'''Figure 1 Population structure in O. sativa . ( a ) The large pie chart summarizes the distribution of subpopulations in the 413 O. sativa samples in our diversity panel, and the smaller pie charts on the world map correspond to the country-specifi c distribution of subpopulations sampled (note: large countries such as China, India and the US were divided into several major rice growing regions). The size of the pie chart is proportional to the sample size and colours within each pie chart are refl ective of the percentage of samples in each subpopulation. Seeds representing each subpopulation are displayed with and without hull in the centre, with 1 cm scale bar.''']]
 * Despite the availability of these scientifi c resources, most of what we know about the genetic architecture of complex traits in rice is based on traditional quantitative trait locus (QTL) linkage mapping using bi-parental populations. While providing valuable insights 12 , the QTL approach is clearly not ‘ scalable ’ to investigate the genomic potential and tremendous phenotypic variation of the more than 120,000 accessions available in public germplasm reposi- tories. Genome-wide association study (GWAS) mapping makes it possible to simultaneously screen a very large number of accessions for genetic variation underlying diverse complex traits. An extra advantage of the GWAS design for rice is the homozygous nature of most rice varieties, which makes it possible to employ a genotype or sequence once and phenotype many times over strategy, whereby once the lines are genomically characterized, the genetic data can be reused many times over across diff erent phenotypes and environments.
 * '''In this project, the researchers  present a genome-wide association study in a global collection of 413 diverse rice ( O. sativa ) varieties from 82 countries using a high-quality custom-designed 44,100 oligonucleotide genotyping array. For these varieties, we systematically phenotyped 34 morphological, developmental and agronomic traits over two consecutive fi eld seasons. Our mapping strategy evaluated variation both within and among four of the major subgroups of rice, revealing significant heterogeneity of genetic architecture among groups, as well as gene-by-environment eff ects. Unlike previous GWAS studies in rice 5 , purifi ed seed stocks of the rice strains and all the genotypic and phenotypic information generated over th course of this study are publicly available, creating a valuable, open source translational research platform that can be rapidly expanded through community participation to enhance the power and resolution of GWAS in rice.'''