IC4R007-GWAS-2015-25785447 - Revision history

Zhennan: /* Research Findings */

2016-06-22T02:07:05Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-22T02:05:26Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-22T02:04:23Z

‎Research Findings

Zhennan: /* The Background of This Project */

2016-06-22T02:04:09Z

‎The Background of This Project

Zhennan: /* Research Findings */

2016-06-22T02:03:44Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-22T02:01:43Z

‎Research Findings

Zhennan: /* The Background of This Project */

2016-06-22T02:01:14Z

‎The Background of This Project

Zhennan: /* The Background of This Project */

2016-06-22T02:00:08Z

‎The Background of This Project

Zhennan: /* Research Findings */

2016-06-21T16:08:13Z

‎Research Findings

Zhennan: /* Research Findings */

2016-06-21T16:07:40Z

‎Research Findings

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 [[File:IC4R007-GWAS-2015-25785447-2.PNG|1000px|thumb|center|'''Figure 2''' '' Physical map of significant GWAS QTL. '']]
 * Performing association mapping on a panel of adapted breeding lines rather than on a diversity panel provides the opportunity to apply the results directly to breeding programs. Unlike the results from studies using diversity panels, our association mapping results can be readily used to identify favorable or unfavorable haplotypes that are currently segregating in our elite breeding material. These haplotypes could be used to determine the most suitable parents for crossing in order to exploit transgressive segregation and/or to increase the frequency with which favorable haplotypes appear in the progeny. MAS for favorable haplotypes among the progeny would allow us to increase breeding efficiency and decrease cost by reducing the number of plants advanced to the next generation of breeding or that need to be phenotyped. In this way, we aim to increase the rate of genetic improvement by increasing gain from selection.
 == Labs working on this Project ==

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 * A total of 52 QTL were identified for 11 of the 19 agronomic traits evaluated in this study. Peaks for the other eight traits did not pass the significance threshold as determined by a false discovery rate (FDR) of 0.1; all traits were evaluated during both the 2012 dry and wet seasons in Los Baños, Philippines ('''Figures. 1–2''') (materials and methods). The Manhattan plots for flowering time (FLW), plant height (PH), and length-breadth ratio (LBR) in the dry season and yield (YLD) in the wet season are shown in '''Figure. 1'''.
 [[File:IC4R007-GWAS-2015-25785447-2.PNG|1000px|thumb|center|'''Figure 2''' '' Physical map of significant GWAS QTL. '']]
+* Performing association mapping on a panel of adapted breeding lines rather than on a diversity panel provides the opportunity to apply the results directly to breeding programs. Unlike the results from studies using diversity panels, our association mapping results can be readily used to identify favorable or unfavorable haplotypes that are currently segregating in our elite breeding material. These haplotypes could be used to determine the most suitable parents for crossing in order to exploit transgressive segregation and/or to increase the frequency with which favorable haplotypes appear in the progeny. MAS for favorable haplotypes among the progeny would allow us to increase breeding efficiency and decrease cost by reducing the number of plants advanced to the next generation of breeding or that need to be phenotyped. In this way, we aim to increase the rate of genetic improvement by increasing gain from selection.
 == Labs working on this Project ==

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 ==Research Findings==
 * A total of 52 QTL were identified for 11 of the 19 agronomic traits evaluated in this study. Peaks for the other eight traits did not pass the significance threshold as determined by a false discovery rate (FDR) of 0.1; all traits were evaluated during both the 2012 dry and wet seasons in Los Baños, Philippines ('''Figures. 1–2''') (materials and methods). The Manhattan plots for flowering time (FLW), plant height (PH), and length-breadth ratio (LBR) in the dry season and yield (YLD) in the wet season are shown in '''Figure. 1'''.
-[[File:IC4R007-GWAS-2015-25785447-2.PNG|500px|thumb|center|'''Figure 2''' '' Physical map of significant GWAS QTL. '']]
+[[File:IC4R007-GWAS-2015-25785447-2.PNG|1000px|thumb|center|'''Figure 2''' '' Physical map of significant GWAS QTL. '']]
+*

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 ==The Background of This Project==
 * Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations.
 * Developing new rice varieties that yield well with fewer inputs and under more stressful and unpredictable climatic conditions is essential for the future of food security, and is the major challenge for today's rice breeders. Fortunately, the rapid development of new sequencing technologies has created the opportunity to enhance our understanding of the genetic basis of crop productivity. The utilization of this genetic information offers the plant breeding community a range of modern tools and methods for addressing these challenges.
 * Genome wide association studies (GWAS) have been widely used to identify QTL underlying quantitative traits in humans and animals, and has recently also become a popular method of mapping QTL in plants. Association mapping identifies QTL based on the historic recombination in a panel of diverse germplasm via the presence of linkage disequilibrium (LD) between SNPs and QTL, i.e., the non-random association of alleles. A high density marker panel that covers the genome is required in order to monitor the density of recombination breakpoints in the population.
 * Genome-wide prediction, or genomic selection (GS), refers to the process of using genome-wide DNA markers to predict which individuals in a breeding population are most valuable as parents of the next generation of offspring. GS takes the same inputs as GWAS, a phenotype dataset and genotype dataset on a population of lines of interest to plant breeders. As such, it is possible to perform GWAS and GS on the same population, where all that is needed is additional computation analysis. Such an undertaking has clear advantages. The genetic architecture revealed by association mapping can be used to inform the GS models—for example, if highly significant SNPs are revealed by a GWAS, these SNPs could be fit as fixed effects in a GS model, and experimenting with different types of genomic selection statistical methods (i.e., linear versus non-linear, additive versus non-additive) can corroborate inferences about the genetic architecture of a trait.
 ==Plant Culture & Treatment==

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 ==Research Findings==
 * A total of 52 QTL were identified for 11 of the 19 agronomic traits evaluated in this study. Peaks for the other eight traits did not pass the significance threshold as determined by a false discovery rate (FDR) of 0.1; all traits were evaluated during both the 2012 dry and wet seasons in Los Baños, Philippines ('''Figures. 1–2''') (materials and methods). The Manhattan plots for flowering time (FLW), plant height (PH), and length-breadth ratio (LBR) in the dry season and yield (YLD) in the wet season are shown in '''Figure. 1'''.
-[[File:IC4R006-GWAS-2015-25689273-1.PNG|500px|thumb|center|'''Table 1''' ''Narrow-sense heritabilities (h 2 ) for the two validation season, 2012 dry season (DS 2012) and the 2012 wet season (WS 2012). YLD = grain yield, FL = days to 50% flowering, PH = plant height. '']]
+[[File:IC4R007-GWAS-2015-25785447-2.PNG|500px|thumb|center|'''Figure 2''' '' Physical map of significant GWAS QTL. '']]
+*

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 ==Research Findings==
-* A total of 52 QTL were identified for 11 of the 19 agronomic traits evaluated in this study('''Table 1'''). Peaks for the other eight traits did not pass the significance threshold as determined by a false discovery rate (FDR) of 0.1; all traits were evaluated during both the 2012 dry and wet seasons in Los Baños, Philippines ('''Figures. 1–2''') (materials and methods). The Manhattan plots for flowering time (FLW), plant height (PH), and length-breadth ratio (LBR) in the dry season and yield (YLD) in the wet season are shown in Figure. 1.
+* A total of 52 QTL were identified for 11 of the 19 agronomic traits evaluated in this study. Peaks for the other eight traits did not pass the significance threshold as determined by a false discovery rate (FDR) of 0.1; all traits were evaluated during both the 2012 dry and wet seasons in Los Baños, Philippines ('''Figures. 1–2''') (materials and methods). The Manhattan plots for flowering time (FLW), plant height (PH), and length-breadth ratio (LBR) in the dry season and yield (YLD) in the wet season are shown in Figure. 1.
 [[File:IC4R006-GWAS-2015-25689273-1.PNG|500px|thumb|center|'''Table 1''' ''Narrow-sense heritabilities (h 2 ) for the two validation season, 2012 dry season (DS 2012) and the 2012 wet season (WS 2012). YLD = grain yield, FL = days to 50% flowering, PH = plant height. '']]
+*