Genome-Wide Association Studies in Rice - Revision history

Zhennan at 02:14, 28 June 2016

2016-06-28T02:14:26Z

Zhennan at 09:30, 27 June 2016

2016-06-27T09:30:35Z

Zhennan at 09:10, 27 June 2016

2016-06-27T09:10:50Z

Xysj2012: /* Future applications of GWAS */

2016-06-24T01:54:44Z

‎Future applications of GWAS

Xysj2012: /* The challenge in biological follow-up */

2016-06-24T01:54:21Z

‎The challenge in biological follow-up

Xysj1988: /* Projects List */

2016-06-22T13:23:32Z

‎Projects List

Xysj1987: Created page with "==What is GWAS? == * A genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an examination of many common ..."

2016-06-21T04:20:44Z

Created page with "==What is GWAS? == * A genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an examination of many common ..."

New page

==What is GWAS? ==
* A genome-wide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS), is an examination of many common genetic variants in different individuals to see if any variant is associated with a trait. Once new genetic associations are identified, researchers can use the information to develop better strategies to detect, treat and prevent the disease or develop high-efficiency method for crops breeding. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits.Such studies are particularly useful in finding genetic variations that contribute to common, complex diseases, such as asthma, cancer, diabetes, heart disease and mental illnesses<ref name="ref1"/>.
<br>
[[File: IC4R-GWAS-overview-1.png|center|thumb|870px|'''Figure 1. Cartoon view of bar-coded multiplex sequencing approach in rice resequencing. Three-base indexed DNA samples of different rice accessions were combined and sequenced together. Sequence reads were sorted based on the index (in green) and aligned with reference genome sequence to identify the SNPs and call genotypes (in red or blue).''']]

==The challenge in biological follow-up==
* GWAS on the agronomic traits provides valuable infor- mation and data that can be immediately followed up in rice breeding (e.g. by marker assisted selection on the associated loci for the traits of interest), even for some without the information of causal genes and causal variants [37–39]. Direct biological follow-up, including the gene cloning and studies on molecular mechanisms, is still challenging in rice GWAS. Rice has a long LD that means an associated locus contains several candidate genes. The toolkits from rice functional genomics studies are helpful. For example, tissue-specific expression pat- tern and differential response to treatments such as biotic or abiotic stress can help to identify the candidate genes around the associated loci underlying related traits. Genetic transformation, T-DNA mutants and TILLING mutants are key to validating the gene function [40], while de novo assembling and extensive annotations of full genome sequences of diverse rice varieties can provide the information on causative mutations [41]. We believe that the integrated approach has the potential to help dissect agronomic traits and identify the important gene alleles in rice.
[[File: IC4R-GWAS-overview-2.png|center|thumb|870px|'''Figure 1. Cartoon view of bar-coded multiplex sequencing approach in rice resequencing. Three-base indexed DNA samples of different rice accessions were combined and sequenced together. Sequence reads were sorted based on the index (in green) and aligned with reference genome sequence to identify the SNPs and call genotypes (in red or blue).''']]

* GWAS belongs to a category of studies referred to as association mapping, which were initially developed in areas in which significant limitations exist to introducing artificial mutations, such as in human genetics [2,3] or in breeding studies of animals [4] and crop plants [5,6]. Association mapping studies commonly assess the statistical significance of the association between quantitative differences of a phenotype and certain genetic polymorphisms in a set of genetically distinct individuals or isogenic strains. They unfold their true power as genome wide approaches (GWAS), which utilize polymorphisms that are broadly and densely distributed throughout the genome including Single Nucleotide Polymorphism (SNP) [7–9]. GWAS enables the identification of causal loci at high-resolution in comparison to classical Quanti- tative Trait Locus (QTL) analyses including linkage analysis. The high-resolution confers GWAS an advantage in identifying causal genes that underlie phenotypes.
== Future applications of GWAS ==
* Despite the relatively recent emergence of GWAS, these studies have already proven to be immensely powerful in identifying genes that underlie variation of processes related to plant growth and development. Interestingly, the genes that were discovered using GWAS approaches contain various genes which are not canonical components of pathways previously identified using mutant screening approaches. This is not unexpected since mutant screens are often biased towards the largest effect mutations [76], which would often be detrimental to plants growing in the wild, while alleles with similar detrimental consequences would likely not be present at a detectable frequency in natural populations.
* GWAS on multiple traits can uncover linked traits, as well as potential trade-offs between traits. For instance, a tight link between meristem length and mature cell length in the root and a trade-off between development and pathogen resistance traits in natural accessions were revealed in GWAS studies. Overall, this highlights the great potential of superimposing GWAS results on different traits to systematically understand trait relations and identify key genes that link these traits.
[[File: IC4R-GWAS-overview-3.png|center|thumb|870px|'''Figure 3. A schematic view of sequencing-based GWAS in rice. The diverse rice accessions are sequenced with low-genome-coverage, and the sequence reads are aligned with rice reference sequence for SNP identification and genotype calling. Candidate genes can be identified through detailed annotation, expression profiling and genic variation detection. The functional genomics approach, such as genetic transformation and T-DNA mutant screens, can be used to validate the effect of the genes.''']]

==Projects List==
{|class="wikitable sortable" style="width:100%;text-align:center"
|-
!Project Title
!Species
!Published years
!Academic Journal
!RiceWiki Project ID
|-
|'''Genetic Architecture of Aluminum Tolerance in Rice (Oryza sativa) Determined through Genome-Wide Association Analysis and QTL Mapping '''
|''Oryza sativa''
|2011
|PLoS Genetics
|[[IC4R001-GWAS-2011-21829395]]
|-
|'''Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa'''
|''Oryza sativa''
|2011
|Nature Communications
|[[IC4R002-GWAS-2011-21915109]]
|-
|'''Genetic dissection of ozone tolerance in rice (Oryza sativa L.) by a genome-wide association study'''
|''Oryza sativa''
|2015
|Journal of Experimental Botany
|[[IC4R004-GWAS-2015-25371505]]
|-
|'''Genome-wide association mapping of salinity tolerance in rice'''
|''Oryza sativa''
|2015
|DNA Research
|[[IC4R005-GWAS-2015-25627243]]
|-
|'''Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines'''
|''Oryza sativa'' L. ssp. ''Japnoica''
|2015
|BMC Plant Biology
|[[IC4R006-GWAS-2015-25689273]]
|-
|'''Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa)'''
|''Oryza sativa''
|2016
|PLoS ONE
|[[IC4R007-GWAS-2015-25785447]]
|}
==References==
<references>
<ref name="ref1">
https://en.wikipedia.org/wiki/Genome-wide_association_study
</ref>
</references>

@@ Line 87: / Line 87: @@
 |''Oryza sativa''
 |2016
-|J Exp Bot
+|Journal of Experimental Botany
 |'''[[IC4R012-GWAS-2016-26552884]]'''
 |}

@@ Line 60: / Line 60: @@
 |'''[[IC4R007-GWAS-2015-25785447]]'''
 |-
-|''''''
+|'''Genome-Wide Association Study for Traits Related to Plant and Grain Morphology, and Root Architecture in Temperate Rice Accessions'''
 |''Oryza sativa''
 |2016
+|PLoS One
-|'''[[IC4R007-GWAS--]]'''
+|'''[[IC4R008-GWAS-2016-27228161]]'''
 |-
-|''''''
+|'''Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement'''
-|''''
+|''Oryza sativa''
+|2016
+|Heredity (Edinb)
-|'''[[IC4R007-GWAS--]]'''
+|'''[[IC4R009-GWAS-2016-26860200]]'''
 |-
-|''''''
+|'''Uncovering novel loci for mesocotyl elongation and shoot length in indica rice through genome-wide association mapping'''
-|''''
+|''Oryza sativa''
+|2016
+|Planta
-|'''[[IC4R007-GWAS--]]'''
+|'''[[IC4R010-GWAS-2016-26612069]]'''
 |-
-|''''''
+|'''New insights into the genetic basis of natural chilling and cold shock tolerance in rice by genome-wide association analysis '''
-|''''
+|''Oryza sativa''
+|2016
+|Plant Cell Environ
-|'''[[IC4R007-GWAS--]]'''
+|'''[[IC4R011-GWAS-2016-26381647]]'''
 |-
-|''''''
+|'''A genome-wide association study of a global rice panel reveals resistance in Oryza sativa to root-knot nematodes'''
-|''''
+|''Oryza sativa''
+|2016
+|J Exp Bot
-|'''[[IC4R007-GWAS--]]'''
+|'''[[IC4R012-GWAS-2016-26552884]]'''
 |}

@@ Line 59: / Line 59: @@
 |PLoS ONE
 |'''[[IC4R007-GWAS-2015-25785447]]'''
 |}

@@ Line 12: / Line 12: @@
 == Future applications of GWAS ==
 * Despite the relatively recent emergence of GWAS, these studies have already proven to be immensely powerful in identifying genes that underlie variation of processes related to plant growth and development. Interestingly, the genes that were discovered using GWAS approaches contain various genes which are not canonical components of pathways previously identified using mutant screening approaches. This is not unexpected since mutant screens are often biased towards the largest effect mutations [76], which would often be detrimental to plants growing in the wild, while alleles with similar detrimental consequences would likely not be present at a detectable frequency in natural populations.
-* GWAS on multiple traits can uncover linked traits, as well as potential trade-offs between traits. For instance, a tight link between meristem length and mature cell length in the root and a trade-off between development and  pathogen resistance traits in natural accessions were revealed in GWAS studies. Overall, this highlights the great potential of superimposing GWAS results on different traits to systematically understand trait relations and identify key genes that link these traits.
+* GWAS on multiple traits can uncover linked traits, as well as potential trade-offs between traits. For instance, a tight link between meristem length and mature cell length in the root and a trade-off between development and  pathogen resistance traits in natural accessions were revealed in GWAS studies. Overall, this highlights the great potential of superimposing GWAS results on different traits to systematically understand trait relations and identify key genes that link these traits.<br><br>
 [[File: IC4R-GWAS-overview-3.png|center|thumb|870px|'''Figure 3. A schematic view of sequencing-based GWAS in rice. The diverse rice accessions are sequenced with low-genome-coverage, and the sequence reads are aligned with rice reference sequence for SNP identification and genotype calling. Candidate genes can be identified through detailed annotation, expression profiling and genic variation detection. The functional genomics approach, such as genetic transformation and T-DNA mutant screens, can be used to validate the effect of the genes.''']]

@@ Line 5: / Line 5: @@
 ==The challenge in biological follow-up==
-* GWAS on the agronomic traits provides valuable infor- mation and data that can be immediately followed up in rice breeding (e.g. by marker assisted selection on the associated loci for the traits of interest), even for some without the information of causal genes and causal variants [37–39]. Direct biological follow-up, including the gene cloning and studies on molecular mechanisms, is still challenging in rice GWAS. Rice has a long LD that means an associated locus contains several candidate genes. The toolkits from rice functional genomics studies are helpful. For example, tissue-specific expression pat- tern and differential response to treatments such as biotic or abiotic stress can help to identify the candidate genes around the associated loci underlying related traits. Genetic transformation, T-DNA mutants and TILLING mutants are key to validating the gene function [40], while de novo assembling and extensive annotations of full genome sequences of diverse rice varieties can provide the information on causative mutations [41]. We believe that the integrated approach has the potential to help dissect agronomic traits and identify the important gene alleles in rice.
+* GWAS on the agronomic traits provides valuable infor- mation and data that can be immediately followed up in rice breeding (e.g. by marker assisted selection on the associated loci for the traits of interest), even for some without the information of causal genes and causal variants [37–39]. Direct biological follow-up, including the gene cloning and studies on molecular mechanisms, is still challenging in rice GWAS. Rice has a long LD that means an associated locus contains several candidate genes. The toolkits from rice functional genomics studies are helpful. For example, tissue-specific expression pat- tern and differential response to treatments such as biotic or abiotic stress can help to identify the candidate genes around the associated loci underlying related traits. Genetic transformation, T-DNA mutants and TILLING mutants are key to validating the gene function [40], while de novo assembling and extensive annotations of full genome sequences of diverse rice varieties can provide the information on causative mutations [41]. We believe that the integrated approach has the potential to help dissect agronomic traits and identify the important gene alleles in rice.<br><br>
 [[File: IC4R-GWAS-overview-2.png|center|thumb|870px|'''Figure 1. Cartoon view of bar-coded multiplex sequencing approach in rice resequencing. Three-base indexed DNA samples of different rice accessions were combined and sequenced together. Sequence reads were sorted based on the index (in green) and aligned with reference genome sequence to identify the SNPs and call genotypes (in red or blue).''']]
 * GWAS belongs to a category of studies referred to as association mapping, which were initially developed in areas in which significant limitations exist to introducing artificial mutations, such as in human genetics [2,3] or in breeding studies of animals [4] and crop plants [5,6]. Association mapping studies commonly assess the statistical significance of the association between quantitative differences of a phenotype and certain genetic polymorphisms in a set of genetically distinct individuals or isogenic strains. They unfold their true power as genome wide approaches (GWAS), which utilize polymorphisms that are broadly and densely distributed throughout the genome including Single Nucleotide Polymorphism (SNP) [7–9]. GWAS enables the identification of causal loci at high-resolution in comparison to classical Quanti- tative Trait Locus (QTL) analyses including linkage analysis. The high-resolution confers GWAS an advantage in identifying causal genes that underlie phenotypes.
 == Future applications of GWAS ==
 * Despite the relatively recent emergence of GWAS, these studies have already proven to be immensely powerful in identifying genes that underlie variation of processes related to plant growth and development. Interestingly, the genes that were discovered using GWAS approaches contain various genes which are not canonical components of pathways previously identified using mutant screening approaches. This is not unexpected since mutant screens are often biased towards the largest effect mutations [76], which would often be detrimental to plants growing in the wild, while alleles with similar detrimental consequences would likely not be present at a detectable frequency in natural populations.

@@ Line 27: / Line 27: @@
 |2011
 |PLoS Genetics
-|[[IC4R001-GWAS-2011-21829395]]
+|'''[[IC4R001-GWAS-2011-21829395]]'''
 |-
 |'''Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa'''
@@ Line 33: / Line 33: @@
 |2011
 |Nature Communications
-|[[IC4R002-GWAS-2011-21915109]]
+|'''[[IC4R002-GWAS-2011-21915109]]'''
 |-
 |'''Genetic dissection of ozone tolerance in rice (Oryza sativa L.) by a genome-wide association study'''
@@ Line 39: / Line 39: @@
 |2015
 |Journal of Experimental Botany
-|[[IC4R004-GWAS-2015-25371505]]
+|'''[[IC4R004-GWAS-2015-25371505]]'''
 |-
 |'''Genome-wide association mapping of salinity tolerance in rice'''
@@ Line 45: / Line 45: @@
 |2015
 |DNA Research
-|[[IC4R005-GWAS-2015-25627243]]
+|'''[[IC4R005-GWAS-2015-25627243]]'''
 |-
 |'''Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines'''
@@ Line 51: / Line 51: @@
 |2015
 |BMC Plant Biology
-|[[IC4R006-GWAS-2015-25689273]]
+|'''[[IC4R006-GWAS-2015-25689273]]'''
 |-
 |'''Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa)'''
@@ Line 57: / Line 57: @@
 |2016
 |PLoS ONE
-|[[IC4R007-GWAS-2015-25785447]]
+|'''[[IC4R007-GWAS-2015-25785447]]'''
 |}
 ==References==
 <references>