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solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database.

Tecle IY, Menda N, Buels RM, van der Knaap E, Mueller LA - BMC Bioinformatics (2010)

Bottom Line: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL).To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.

View Article: PubMed Central - HTML - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.

ABSTRACT

Background: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.

Description: The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application.

Conclusions: solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.

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Genome-wide QTL mapping output. An example of genome-wide QTL mapping output from solQTL for a trait called 'Fruit Area' (see supplemental figure 2) and legend detailing the analysis parameters (Source: http://solgenomics.net/phenome/population_indls.pl?population_id=12&cvterm_id=39945/). Clicking a linkage group with a significant QTL (eg. Chr 3) leads to the QTL detail page (see Figure 2).
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Figure 1: Genome-wide QTL mapping output. An example of genome-wide QTL mapping output from solQTL for a trait called 'Fruit Area' (see supplemental figure 2) and legend detailing the analysis parameters (Source: http://solgenomics.net/phenome/population_indls.pl?population_id=12&cvterm_id=39945/). Clicking a linkage group with a significant QTL (eg. Chr 3) leads to the QTL detail page (see Figure 2).

Mentions: The QTL analysis is performed dynamically on a trait-by-trait basis when the user visits the trait of interest on the QTL population's webpage. On the QTL population's page, under the QTL(s) heading, clicking the graph icon corresponding to the trait of interest initiates the QTL mapping analysis. The analysis output is displayed on a new trait page where one can view its QTL analysis output (Figure 1) and frequency distribution of the phenotype data for the trait. QTL mapping locations from the output of the QTL analysis are viewable for the whole genome on a per-linkage group basis. The details of the analysis are displayed in a legend including genome-wide LOD threshold significance level, if the user opted for permutation test and probability level, size of genome scan and method used for the calculation of the QTL genotype probabilities, QTL mapping method and QTL model used. Clicking a chromosome with a QTL, after visually determining the chromosome with a significant QTL relative to the LOD threshold value in the legend, takes the user to a QTL detail page (Figure 2).


solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database.

Tecle IY, Menda N, Buels RM, van der Knaap E, Mueller LA - BMC Bioinformatics (2010)

Genome-wide QTL mapping output. An example of genome-wide QTL mapping output from solQTL for a trait called 'Fruit Area' (see supplemental figure 2) and legend detailing the analysis parameters (Source: http://solgenomics.net/phenome/population_indls.pl?population_id=12&cvterm_id=39945/). Clicking a linkage group with a significant QTL (eg. Chr 3) leads to the QTL detail page (see Figure 2).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2984588&req=5

Figure 1: Genome-wide QTL mapping output. An example of genome-wide QTL mapping output from solQTL for a trait called 'Fruit Area' (see supplemental figure 2) and legend detailing the analysis parameters (Source: http://solgenomics.net/phenome/population_indls.pl?population_id=12&cvterm_id=39945/). Clicking a linkage group with a significant QTL (eg. Chr 3) leads to the QTL detail page (see Figure 2).
Mentions: The QTL analysis is performed dynamically on a trait-by-trait basis when the user visits the trait of interest on the QTL population's webpage. On the QTL population's page, under the QTL(s) heading, clicking the graph icon corresponding to the trait of interest initiates the QTL mapping analysis. The analysis output is displayed on a new trait page where one can view its QTL analysis output (Figure 1) and frequency distribution of the phenotype data for the trait. QTL mapping locations from the output of the QTL analysis are viewable for the whole genome on a per-linkage group basis. The details of the analysis are displayed in a legend including genome-wide LOD threshold significance level, if the user opted for permutation test and probability level, size of genome scan and method used for the calculation of the QTL genotype probabilities, QTL mapping method and QTL model used. Clicking a chromosome with a QTL, after visually determining the chromosome with a significant QTL relative to the LOD threshold value in the legend, takes the user to a QTL detail page (Figure 2).

Bottom Line: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL).To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.

View Article: PubMed Central - HTML - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA.

ABSTRACT

Background: A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases.

Description: The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application.

Conclusions: solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.

Show MeSH