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

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.

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).

Figure 2

A QTL detail page. An example QTL detail page, e.g., for putative 'Fruit Area' QTL (see figure 1), showing the analysis details, links to detail pages of QTL flanking and peak markers, the QTL's linkage group page and Comparative Map Viewer page (see Figure 3), and genome positions of the markers (see Figure 4).

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.

Figure 4

Linking predicted QTLs to an annotated genome. An example of a 20 kbp segment on the tomato genome matching a QTL marker (eg. TO581, peak marker for a putative 'Fruit Area' QTL, generated after following a link from the QTL detail page (see Figure 2)). Within the 20 kbp of the marker are annotations including gene and protein models with their GO annotations, ESTs, cDNAs and markers.