qtlXplorer : an online systems genetics browser in the eucalyptus genome integrative explorer (EucGenIE)

Christie, Nanette; Mannapperuma, Chanaka; Ployet, Raphael; Van der Merwe, Karen; Mahler, Niklas; Delhomme, Nicolas; Naidoo, Sanushka; Mizrachi, Eshchar; Street, Nathaniel R.; Myburg, Alexander Andrew

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qtlXplorer : an online systems genetics browser in the eucalyptus genome integrative explorer (EucGenIE)

Christie, Nanette; Mannapperuma, Chanaka; Ployet, Raphael; Van der Merwe, Karen; Mahler, Niklas; Delhomme, Nicolas; Naidoo, Sanushka; Mizrachi, Eshchar; Street, Nathaniel R.; Myburg, Alexander Andrew

URI: https://repository.up.ac.za/handle/2263/86211

Date: 2021-12-15

Abstract:

BACKGROUND : Affordable high-throughput DNA and RNA sequencing technologies are allowing genomic analysis of plant and animal populations and as a result empowering new systems genetics approaches to study complex traits. The availability of intuitive tools to browse and analyze the resulting large-scale genetic and genomic datasets remain a significant challenge. Furthermore, these integrative genomics approaches require innovative methods to dissect the flow and interconnectedness of biological information underlying complex trait variation. The Plant Genome Integrative Explorer (PlantGenIE.org) is a multi-species database and domain that houses online tools for model and woody plant species including Eucalyptus. Since the Eucalyptus Genome Integrative Explorer (EucGenIE) is integrated within PlantGenIE, it shares genome and expression analysis tools previously implemented within the various subdomains (ConGenIE, PopGenIE and AtGenIE). Despite the success in setting up integrative genomics databases, online tools for systems genetics modelling and high-resolution dissection of complex trait variation in plant populations have been lacking. RESULTS : We have developed qtlXplorer (https:// eucge nie. org/ QTLXp lorer) for visualizing and exploring systems genetics data from genome-wide association studies including quantitative trait loci (QTLs) and expression-based QTL (eQTL) associations. This module allows users to, for example, find co-located QTLs and eQTLs using an interactive version of Circos, or explore underlying genes using JBrowse. It provides users with a means to build systems genetics models and generate hypotheses from large-scale population genomics data. We also substantially upgraded the EucGenIE resource and show how it enables users to combine genomics and systems genetics approaches to discover candidate genes involved in biotic stress responses and wood formation by focusing on two multigene families, laccases and peroxidases. CONCLUSIONS : qtlXplorer adds a new dimension, population genomics, to the EucGenIE and PlantGenIE environment. The resource will be of interest to researchers and molecular breeders working in Eucalyptus and other woody plant species. It provides an example of how systems genetics data can be integrated with functional genetics data to provide biological insight and formulate hypotheses. Importantly, integration within PlantGenIE enables novel comparative genomics analyses to be performed from population-scale data.

Description:

ADDITIONAL FILE 1: TABLE S1. Metadata for RNA-seq datasets integrated in EucGenIE. In total 355 transcriptome datasets were integrated: 42 exAtlas datasets, 30 biotic interactions datasets and 283 transcriptomes from different F2 backcross individuals. TABLE S2. The 44 laccase genes detected in Eucalyptus with their corresponding BLASTP, HMMER and SignalP results. Genes encoding proteins with the three truncated domains of a canonical LAC and predicted to have a signal peptide were considered for analysis in this study. TABLE S3. Expression values in transcripts per million (TPM) of the 59 LAC/PRX genes, in the E. grandis exAtlas [35] and across biotic stress conditions [36–38]. TABLE S4. Secondary cell wall-related genes that were used for co-expression analysis. TABLE S5. Nine trait QTLs that co-locate with the eight LAC/PRX candidate genes from subnetwork 2 (Fig. 2, Table S3) or their eQTL positions. TABLE S6. Genes sharing eQTLs at the eleven eQTL positions associated with the eight LAC/PRX candidate genes from subnetwork 2 (Fig. 2, Additional file 1: Table S3). TABLE S7. Fisher’s Exact Test results for enrichment of cell wallrelated genes with eQTLs located at each of eleven eQTL positions, respectively, associated with the eight LAC/PRX candidate genes from subnetwork 2 (Fig. 2, Additional file 1: Table S3). TABLE S8. Transcription factors underlying the eleven eQTL peak positions associated with the eight LAC/PRX candidate genes from subnetwork 2 (Fig. 2, Additional file 1: Table S3). TABLE S9. Final score for ranking the eight LAC/PRX candidate genes from subnetwork 2 (Fig. 2, Additional file 1: Table S3) using multiple lines of evidence.

ADDITIONAL FILE 2: METHOD S1. Expression profiling of RNA-seq datasets. Method METHOD S2. QTL and eQTL analysis of data in qtlXplorer. METHOD S3. Overview of the EucGenIE tools. METHOD S4. Translating genes between species within PlantGenIE. METHOD S5. Case study: Laccases and peroxidases.

ADDITIONAL FILE 3: FIGURE S1. Co-expression analysis of 137 LAC/PRX genes using exNet in EucGenIE. (a) Correlation network representing 62 correlations between 42 (out of a total of 137) LAC/PRX genes, filtered at threshold 5. (b) Correlation network representing 1877 correlations between 90 LAC/PRX and 1043 other correlated genes, obtained after expanding at threshold 6. Correlation networks are based on 72 transcriptomic datasets (all exAtlas and biotic interactions datasets; sample collections 2–4 in Table 1) in EucGenIE and were visualized using the exNet tool.

ADDITIONAL FILE 4: FIGURE S2. Systems genetics analysis of the eight LAC/PRX genes associated with secondary cell wall (SCW) biosynthesis in Eucalyptus xylem formation. The network was built from data exported from qtlXplorer and follows the same structure as presented in Fig. 6. Cis- and trans-eQTL associations connecting candidate genes (blue nodes) or cell wall-related genes (green nodes) to eQTL peak positions (turquoise triangles) are represented by red and blue edges, respectively. Transcription factors (TFs) underlying eQTL peak positions are represented by grey or green squares and connected to eQTL peak positions via grey dashed line edges. Gene expression profile correlations are represented by grey solid edges, with thickness proportional to the absolute value of the correlation. Physical overlap of trait QTLs (purple nodes) with candidate gene or eQTL peak positions are represented by green dashed lines. eQTL peak position node size is proportional to the number of genes having eQTLs (cis or trans) mapped at that genomic position. Underlying TF node size is proportional to the number and average value of the correlations of TFs with genes having cis- or trans-eQTLs mapped at that position (underlying TF score; Additional file 1: Table S8). Candidate gene node size is proportional to its score for prioritization (see Additional file 2: Methods S5; Additional file 1: Table S9), taking into account (i) their correlations (number and average value) with SCW genes across the population-wide transcriptomic data, (ii) the number of physical overlaps with candidate gene/SCWrelated QTLs, (iii) the number of eQTLs mapped at SCW-enriched eQTL positions, (iv) the number of overlaps of their eQTL positions with SCW-related trait QTLs, and (v) the number of SCW-related TFs in the top 10 best candidate TFs underlying their eQTL positions.

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