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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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Comparative genetic analysis of a predicted QTL with other genetic maps. Panel (A) shows an example linkage map with a zoomed out QTL segment, generated after clicking the linkage group link on the detail page of the QTL of interest (see Figure 2). (B) shows a comparison of the QTL shown on (A) to the Tomato-EXPEN 2000 (F2.2000) consensus genetic map. The zoomed out region, generated after manually feeding the Comparative Map Viewer with the QTL marker positions on the F2.2000 map instead of their genetic positions in the Tomato-EXIMP 2008 map, displays greater number of markers from the reference map within the shared genetic segment between the two maps.
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Figure 3: Comparative genetic analysis of a predicted QTL with other genetic maps. Panel (A) shows an example linkage map with a zoomed out QTL segment, generated after clicking the linkage group link on the detail page of the QTL of interest (see Figure 2). (B) shows a comparison of the QTL shown on (A) to the Tomato-EXPEN 2000 (F2.2000) consensus genetic map. The zoomed out region, generated after manually feeding the Comparative Map Viewer with the QTL marker positions on the F2.2000 map instead of their genetic positions in the Tomato-EXIMP 2008 map, displays greater number of markers from the reference map within the shared genetic segment between the two maps.

Mentions: On the QTL detail page (Figure 2) for each QTL, the peak and flanking markers with their corresponding positions on the respective genetic map for the population are shown. The 95% QTL confidence interval is linked to the corresponding linkage group page where one can view all the markers in the linkage group with the QTL segment zoomed out. On the linkage group webpage, users can perform comparative map analysis between the QTL region of interest and physical and genetic maps from several Solanaceae species in the database using the SGN Comparative Map Viewer, as long as the QTL flanking markers are mapped to the other maps. Marker-dense genetic maps available in SGN for comparative analysis include the S. lycopersicum LA925 x S. pennellii LA716 F2.2000 map [43] which contains more than 2500 Restriction Fragment Length Polymorphism (RFLP), Cleaved Amplified Polymorphic Sequences (CAPS) and Conserved Ortholog Set (COS) markers (Figure 3). The SGN Comparative Map Viewer is described in [39] and additional help and documentation for this tool can be found at http://solgenomics.net/help/cview.pl.


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)

Comparative genetic analysis of a predicted QTL with other genetic maps. Panel (A) shows an example linkage map with a zoomed out QTL segment, generated after clicking the linkage group link on the detail page of the QTL of interest (see Figure 2). (B) shows a comparison of the QTL shown on (A) to the Tomato-EXPEN 2000 (F2.2000) consensus genetic map. The zoomed out region, generated after manually feeding the Comparative Map Viewer with the QTL marker positions on the F2.2000 map instead of their genetic positions in the Tomato-EXIMP 2008 map, displays greater number of markers from the reference map within the shared genetic segment between the two maps.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Comparative genetic analysis of a predicted QTL with other genetic maps. Panel (A) shows an example linkage map with a zoomed out QTL segment, generated after clicking the linkage group link on the detail page of the QTL of interest (see Figure 2). (B) shows a comparison of the QTL shown on (A) to the Tomato-EXPEN 2000 (F2.2000) consensus genetic map. The zoomed out region, generated after manually feeding the Comparative Map Viewer with the QTL marker positions on the F2.2000 map instead of their genetic positions in the Tomato-EXIMP 2008 map, displays greater number of markers from the reference map within the shared genetic segment between the two maps.
Mentions: On the QTL detail page (Figure 2) for each QTL, the peak and flanking markers with their corresponding positions on the respective genetic map for the population are shown. The 95% QTL confidence interval is linked to the corresponding linkage group page where one can view all the markers in the linkage group with the QTL segment zoomed out. On the linkage group webpage, users can perform comparative map analysis between the QTL region of interest and physical and genetic maps from several Solanaceae species in the database using the SGN Comparative Map Viewer, as long as the QTL flanking markers are mapped to the other maps. Marker-dense genetic maps available in SGN for comparative analysis include the S. lycopersicum LA925 x S. pennellii LA716 F2.2000 map [43] which contains more than 2500 Restriction Fragment Length Polymorphism (RFLP), Cleaved Amplified Polymorphic Sequences (CAPS) and Conserved Ortholog Set (COS) markers (Figure 3). The SGN Comparative Map Viewer is described in [39] and additional help and documentation for this tool can be found at http://solgenomics.net/help/cview.pl.

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