Integrating genome annotation and QTL position to identify candidate genes for productivity, architecture and water-use efficiency in Populus spp

Abstract

Background: Hybrid poplars species are candidates for biomass production but breeding efforts are needed to combine productivity and water use efficiency in improved cultivars. The understanding of the genetic architecture of growth in poplar by a Quantitative Trait Loci (QTL) approach can help us to elucidate the molecular basis of such integrative traits but identifying candidate genes underlying these QTLs remains difficult. Nevertheless, the increase of genomic information together with the accessibility to a reference genome sequence (Populus trichocarpa Nisqually-1) allow to bridge QTL information on genetic maps and physical location of candidate genes on the genome. The objective of the study is to identify QTLs controlling productivity, architecture and leaf traits in a P. deltoides x P. trichocarpa F1 progeny and to identify candidate genes underlying QTLs based on the anchoring of genetic maps on the genome and the gene ontology information linked to genome annotation. The strategy to explore genome annotation was to use Gene Ontology enrichment tools to test if some functional categories are statistically over-represented in QTL regions.

Results: Four leaf traits and 7 growth traits were measured on 330 F1 P. deltoides x P. trichocarpa progeny. A total of 77 QTLs controlling 11 traits were identified explaining from 1.8 to 17.2% of the variation of traits. For 58 QTLs, confidence intervals could be projected on the genome. An extended functional annotation was built based on data retrieved from the plant genome database Phytozome and from an inference of function using homology between Populus and the model plant Arabidopsis. Genes located within QTL confidence intervals were retrieved and enrichments in gene ontology (GO) terms were determined using different methods. Significant enrichments were found for all traits. Particularly relevant biological processes GO terms were identified for QTLs controlling number of sylleptic branches: intervals were enriched in GO terms of biological process like 'ripening' and 'adventitious roots development'.

Conclusion: Beyond the simple identification of QTLs, this study is the first to use a global approach of GO terms enrichment analysis to fully explore gene function under QTLs confidence intervals in plants. This global approach may lead to identification of new candidate genes for traits of interest.

Figure 1

Framework linkage maps andQTLs from the segregationanalysis of the P. deltoides x P. trichocarpa pedigree aligned on P. trichocarpa Nisqually-1 sequence. Genome version assembly was Phytozome annotation v2.2. From the left to the right, P. deltoides female 73028–62 genetic map (in white), P. trichocarpa Nisqually-1 physical map with position of genome anchored markers (in black), and P. trichocarpa 101–74 male genetic map (in white). Scaffolds were numbered according to the v2.2 genome version. Additionally, extra scaffold (>19) containing markers mapped on the genetic maps were also shown. The length of bars is proportional to the map distance in cM or to sequence length in bp. Marker names are explained in Material and Methods. Markers in bolds are anchoring markers. Markers in bold and underlined are QTL flanking markers used for the projection of QTL confidence intervals on the physical map. QTLs were represented by vertical lines with horizontal small lines indicating start and stop of the confidence intervals and position of the LOD peak. Trait names were explained in Material and Methods.

Figure 2

Global relationship between physicaldistance (in bp) andgenetic distance (in cM). The relationship is shown for both parental maps, P. deltoides map (circle), P. trichocarpa (triangles). Each point represents a physical and a genetic interval between 2 markers within a chromosome/linkage group.

Figure 3

Comparison of enrichment analysesof GO terms usingTW or MGAS methods. Application to genes within QTLs for the number of sylleptic branches. (A) Ranked list of the 25 overrepresented terms using a Topology-Weighted (TW). (B) Ranked list of the top 25 terms identified by 20 runs of MGSA. Error bars (95 % confidence intervals) obtained with the 20 runs of MGSA. In case of MGSA each of the 25 terms was identified with a marginal value >0.5 in at least one of the 20 runs. GO:xxxxxxx: gene ontology accession; Green label and “P” prefix to GO accession refer to the ontology domain “biological process”; Yellow label and “F” : “molecular function”; magenta label and “C”: “cellular component”.