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A Genome-Wide Association Study for Culm Cellulose Content in Barley Reveals Candidate Genes Co-Expressed with Members of the CELLULOSE SYNTHASE A Gene Family.

Houston K, Burton RA, Sznajder B, Rafalski AJ, Dhugga KS, Mather DE, Taylor J, Steffenson BJ, Waugh R, Fincher GB - PLoS ONE (2015)

Bottom Line: This provided us with gene annotations and associated expression data across multiple tissues, which allowed us to formulate a supported list of candidate genes that regulate cellulose biosynthesis.In addition, genes that have been previously linked with cellulose synthesis by biochemical methods, such as HvCOBRA, a gene of unknown function, were also associated with cellulose levels in the association panel.Our analyses provide new insights into the genes that contribute to cellulose content in cereal culms and to a greater understanding of the interactions between them.

View Article: PubMed Central - PubMed

Affiliation: The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, United Kingdom.

ABSTRACT
Cellulose is a fundamentally important component of cell walls of higher plants. It provides a scaffold that allows the development and growth of the plant to occur in an ordered fashion. Cellulose also provides mechanical strength, which is crucial for both normal development and to enable the plant to withstand both abiotic and biotic stresses. We quantified the cellulose concentration in the culm of 288 two--rowed and 288 six--rowed spring type barley accessions that were part of the USDA funded barley Coordinated Agricultural Project (CAP) program in the USA. When the population structure of these accessions was analysed we identified six distinct populations, four of which we considered to be comprised of a sufficient number of accessions to be suitable for genome-wide association studies (GWAS). These lines had been genotyped with 3072 SNPs so we combined the trait and genetic data to carry out GWAS. The analysis allowed us to identify regions of the genome containing significant associations between molecular markers and cellulose concentration data, including one region cross-validated in multiple populations. To identify candidate genes we assembled the gene content of these regions and used these to query a comprehensive RNA-seq based gene expression atlas. This provided us with gene annotations and associated expression data across multiple tissues, which allowed us to formulate a supported list of candidate genes that regulate cellulose biosynthesis. Several regions identified by our analysis contain genes that are co-expressed with cellulose synthase A (HvCesA) across a range of tissues and developmental stages. These genes are involved in both primary and secondary cell wall development. In addition, genes that have been previously linked with cellulose synthesis by biochemical methods, such as HvCOBRA, a gene of unknown function, were also associated with cellulose levels in the association panel. Our analyses provide new insights into the genes that contribute to cellulose content in cereal culms and to a greater understanding of the interactions between them.

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Related in: MedlinePlus

Genotypic data and population structure analysis.(A) Principal coordinates analysis of all lines phenotyped, colour coded by breeding program. (B) STRUCTURE bar plot for K = 6 based on bOPA 1&2 genotyping data for spring barley lines ordered by breeding programs, but colour coded by K value. Please note colours in A. are independent to those in B. Breeding program 1 = Washington (WA), 2 = Montana (MT), 3 = 2- row North Dakota (N2), 4 = Utah (UT), 5 = 6-row North Dakota (N6), and 6 = Minnesota (MN). Colours represent subpopulation defined by shared genetic ancestry. Q value represents proportion of ancestry to a given subpopulation. (C) Output from Structure Harvester showing K as calculated based on ΔK method, in this case K = 6. L(K) represents the likelihood distribution of K, and L”(K) represents the second order rate of change from L(K).
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pone.0130890.g001: Genotypic data and population structure analysis.(A) Principal coordinates analysis of all lines phenotyped, colour coded by breeding program. (B) STRUCTURE bar plot for K = 6 based on bOPA 1&2 genotyping data for spring barley lines ordered by breeding programs, but colour coded by K value. Please note colours in A. are independent to those in B. Breeding program 1 = Washington (WA), 2 = Montana (MT), 3 = 2- row North Dakota (N2), 4 = Utah (UT), 5 = 6-row North Dakota (N6), and 6 = Minnesota (MN). Colours represent subpopulation defined by shared genetic ancestry. Q value represents proportion of ancestry to a given subpopulation. (C) Output from Structure Harvester showing K as calculated based on ΔK method, in this case K = 6. L(K) represents the likelihood distribution of K, and L”(K) represents the second order rate of change from L(K).

Mentions: The first axis of the PCoA explained 58% of variation within this dataset, clearly separating lines based on row type (Fig 1). Axis 2 and 3 explained a further 15.6% and 11.5% respectively. The PCoA identified between 5 and 9 putative subpopulations. Structure analysis showed that there was clearly admixture in some subpopulations, and ΔK indicated that the most likely number of subpopulations within the dataset was 6 (Fig 1, S1 Fig). Setting the shared ancestry threshold, Q, to 0.6 allocated individual lines into the populations summarised in S2 Table, with more detail provided in S3 Table. Lines that did not meet these criteria were excluded from further analysis. Intra chromosomal LD analysis revealed that the average extent of LD across all chromosomes varied between subpopulations when they were analysed separately (pop2 = 21.2cM, pop 3 = 10.8cM, pop 4 = 7.5cM, and pop5 = 13.2 cM). The average extent of LD was much smaller when all lines were analysed together (5.5cM).


A Genome-Wide Association Study for Culm Cellulose Content in Barley Reveals Candidate Genes Co-Expressed with Members of the CELLULOSE SYNTHASE A Gene Family.

Houston K, Burton RA, Sznajder B, Rafalski AJ, Dhugga KS, Mather DE, Taylor J, Steffenson BJ, Waugh R, Fincher GB - PLoS ONE (2015)

Genotypic data and population structure analysis.(A) Principal coordinates analysis of all lines phenotyped, colour coded by breeding program. (B) STRUCTURE bar plot for K = 6 based on bOPA 1&2 genotyping data for spring barley lines ordered by breeding programs, but colour coded by K value. Please note colours in A. are independent to those in B. Breeding program 1 = Washington (WA), 2 = Montana (MT), 3 = 2- row North Dakota (N2), 4 = Utah (UT), 5 = 6-row North Dakota (N6), and 6 = Minnesota (MN). Colours represent subpopulation defined by shared genetic ancestry. Q value represents proportion of ancestry to a given subpopulation. (C) Output from Structure Harvester showing K as calculated based on ΔK method, in this case K = 6. L(K) represents the likelihood distribution of K, and L”(K) represents the second order rate of change from L(K).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496100&req=5

pone.0130890.g001: Genotypic data and population structure analysis.(A) Principal coordinates analysis of all lines phenotyped, colour coded by breeding program. (B) STRUCTURE bar plot for K = 6 based on bOPA 1&2 genotyping data for spring barley lines ordered by breeding programs, but colour coded by K value. Please note colours in A. are independent to those in B. Breeding program 1 = Washington (WA), 2 = Montana (MT), 3 = 2- row North Dakota (N2), 4 = Utah (UT), 5 = 6-row North Dakota (N6), and 6 = Minnesota (MN). Colours represent subpopulation defined by shared genetic ancestry. Q value represents proportion of ancestry to a given subpopulation. (C) Output from Structure Harvester showing K as calculated based on ΔK method, in this case K = 6. L(K) represents the likelihood distribution of K, and L”(K) represents the second order rate of change from L(K).
Mentions: The first axis of the PCoA explained 58% of variation within this dataset, clearly separating lines based on row type (Fig 1). Axis 2 and 3 explained a further 15.6% and 11.5% respectively. The PCoA identified between 5 and 9 putative subpopulations. Structure analysis showed that there was clearly admixture in some subpopulations, and ΔK indicated that the most likely number of subpopulations within the dataset was 6 (Fig 1, S1 Fig). Setting the shared ancestry threshold, Q, to 0.6 allocated individual lines into the populations summarised in S2 Table, with more detail provided in S3 Table. Lines that did not meet these criteria were excluded from further analysis. Intra chromosomal LD analysis revealed that the average extent of LD across all chromosomes varied between subpopulations when they were analysed separately (pop2 = 21.2cM, pop 3 = 10.8cM, pop 4 = 7.5cM, and pop5 = 13.2 cM). The average extent of LD was much smaller when all lines were analysed together (5.5cM).

Bottom Line: This provided us with gene annotations and associated expression data across multiple tissues, which allowed us to formulate a supported list of candidate genes that regulate cellulose biosynthesis.In addition, genes that have been previously linked with cellulose synthesis by biochemical methods, such as HvCOBRA, a gene of unknown function, were also associated with cellulose levels in the association panel.Our analyses provide new insights into the genes that contribute to cellulose content in cereal culms and to a greater understanding of the interactions between them.

View Article: PubMed Central - PubMed

Affiliation: The James Hutton Institute, Invergowrie, Dundee, DD2 5DA, Scotland, United Kingdom.

ABSTRACT
Cellulose is a fundamentally important component of cell walls of higher plants. It provides a scaffold that allows the development and growth of the plant to occur in an ordered fashion. Cellulose also provides mechanical strength, which is crucial for both normal development and to enable the plant to withstand both abiotic and biotic stresses. We quantified the cellulose concentration in the culm of 288 two--rowed and 288 six--rowed spring type barley accessions that were part of the USDA funded barley Coordinated Agricultural Project (CAP) program in the USA. When the population structure of these accessions was analysed we identified six distinct populations, four of which we considered to be comprised of a sufficient number of accessions to be suitable for genome-wide association studies (GWAS). These lines had been genotyped with 3072 SNPs so we combined the trait and genetic data to carry out GWAS. The analysis allowed us to identify regions of the genome containing significant associations between molecular markers and cellulose concentration data, including one region cross-validated in multiple populations. To identify candidate genes we assembled the gene content of these regions and used these to query a comprehensive RNA-seq based gene expression atlas. This provided us with gene annotations and associated expression data across multiple tissues, which allowed us to formulate a supported list of candidate genes that regulate cellulose biosynthesis. Several regions identified by our analysis contain genes that are co-expressed with cellulose synthase A (HvCesA) across a range of tissues and developmental stages. These genes are involved in both primary and secondary cell wall development. In addition, genes that have been previously linked with cellulose synthesis by biochemical methods, such as HvCOBRA, a gene of unknown function, were also associated with cellulose levels in the association panel. Our analyses provide new insights into the genes that contribute to cellulose content in cereal culms and to a greater understanding of the interactions between them.

No MeSH data available.


Related in: MedlinePlus