Quantitative Trait Loci Information Research Articles

A high-density genetic map and growth related QTL mapping in bighead carp (Hypophthalmichthys nobilis)

Growth related traits in fish are controlled by quantitative trait loci (QTL), but no QTL for growth have been detected in bighead carp (Hypophthalmichthys nobilis) due to the lack of high-density genetic map. In this study, an ultra-high density genetic map was constructed with 3,121 SNP markers by sequencing 117 individuals in a F1 family using 2b-RAD technology. The total length of the map was 2341.27 cM, with an average marker interval of 0.75 cM. A high level of genomic synteny between our map and zebrafish was detected. Based on this genetic map, one genome-wide significant and 37 suggestive QTL for five growth-related traits were identified in 6 linkage groups (i.e. LG3, LG11, LG15, LG18, LG19, LG22). The phenotypic variance explained (PVE) by these QTL varied from 15.4% to 38.2%. Marker within the significant QTL region was surrounded by CRP1 and CRP2, which played an important role in muscle cell division. These high-density map and QTL information provided a solid base for QTL fine mapping and comparative genomics in bighead carp.

Increased prediction accuracy using a genomic feature model including prior information on quantitative trait locus regions in purebred Danish Duroc pigs.

BackgroundIn animal breeding, genetic variance for complex traits is often estimated using linear mixed models that incorporate information from single nucleotide polymorphism (SNP) markers using a realized genomic relationship matrix. In such models, individual genetic markers are weighted equally and genomic variation is treated as a “black box.” This approach is useful for selecting animals with high genetic potential, but it does not generate or utilise knowledge of the biological mechanisms underlying trait variation. Here we propose a linear mixed-model approach that can evaluate the collective effects of sets of SNPs and thereby open the “black box.” The described genomic feature best linear unbiased prediction (GFBLUP) model has two components that are defined by genomic features.ResultsWe analysed data on average daily gain, feed efficiency, and lean meat percentage from 3,085 Duroc boars, along with genotypes from a 60 K SNP chip. In addition information on known quantitative trait loci (QTL) from the animal QTL database was integrated in the GFBLUP as a genomic feature. Our results showed that the most significant QTL categories were indeed biologically meaningful. Additionally, for high heritability traits, prediction accuracy was improved by the incorporation of biological knowledge in prediction models. A simulation study using the real genotypes and simulated phenotypes demonstrated challenges regarding detection of causal variants in low to medium heritability traits.ConclusionsThe GFBLUP model showed increased predictive ability when enough causal variants were included in the genomic feature to explain over 10 % of the genomic variance, and when dilution by non-causal markers was minimal. In the observed data set, predictive ability was increased by the inclusion of prior QTL information obtained outside the training data set, but only for the trait with highest heritability.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0322-9) contains supplementary material, which is available to authorized users.

Yield QTLome distribution correlates with gene density in maize

The genetic control of yield and related traits in maize has been addressed by many quantitative trait locus (QTL) studies, which have produced a wealth of QTL information, also known as QTLome. In this study, we assembled a yield QTLome database and carried out QTL meta-analysis based on 44 published studies, representing 32 independent mapping populations and 49 parental lines. A total of 808 unique QTLs were condensed to 84 meta-QTLs and were projected on the 10 maize chromosomes. Seventy-four percent of QTLs showed a proportion of phenotypic variance explained (PVE) smaller than 10% confirming the high genetic complexity of grain yield. Yield QTLome projection on the genetic map suggested pericentromeric enrichment of QTLs. Conversely, pericentromeric depletion of QTLs was observed when the physical map was considered, suggesting gene density as the main driver of yield QTL distribution on chromosomes. Dominant and overdominant yield QTLs did not distribute differently from additive effect QTLs.

Genetic structure, linkage disequilibrium and association mapping of salt tolerance in japonica rice germplasm at the seedling stage

Soil salinity is a major constraint to rice production. Understanding the genetic basis of salt tolerance is crucial for the improvement of salt tolerance through breeding. Previous quantitative trait locus (QTL) studies for salt tolerance were mainly derived from bi-parental segregating populations and relatively little is known about the results from natural populations. Understanding the genetic diversity, population structure and linkage disequilibrium (LD) in an association panel can effectively avoid spurious associations in association mapping. In this study, 341 japonica rice (Oryza sativa L. subsp. japonica) accessions worldwide were genotyped with 160 simple sequence repeat (SSR) markers to identify marker-trait associations with salt tolerance at the seedling stage. Salt tolerance was evaluated by survival days of seedlings and shoot K+/Na+ ratio. A total of 872 alleles ranging from 2 to 9 per locus were identified from all collections. Population structure analysis identified three main subpopulations for the accessions. Of the SSR pairs in these accessions, 40.05 % marker pairs showed significant LD (P < 0.01). The LD level for linked markers is significantly higher than that for unlinked markers, and LD level was elevated when the panel was classified into subpopulations. The LD decayed to the background at approximately 20–50 cM within the total panel and each subpopulation. A total of ten marker loci associated with salt tolerance were identified using MLM (Q + K) models in TASSEL 3.0. Among which nine marker loci confirmed or narrowed the genomic region reported to harbor QTLs for salt tolerance by linkage mapping in previous reports, and four salt tolerance-related genes were located in the QTL regions in the present study. According to phenotypic effects for alleles of the detected QTLs, favorable alleles were mined. These favorable alleles could be used to design parental combinations and the expected results would be obtained by pyramiding or substituting the favorable alleles per QTL (apart from possible epistatic effects). Our results demonstrate that association mapping can complement and enhance previous QTL information for marker-assisted selection and breeding by design.

Identification of QTL for seed coat colour and oil content inBrassica napusby association mapping using SSR markers

Qu, C., Hasan, M., Lu, K., Liu, L., Zhang, K., Fu, F., Wang, M., Liu, S., Bu, H., Wang, R., Xu, X., Chen, L. and Li, J. 2015. Identification of QTL for seed coat colour and oil content in Brassica napus by association mapping using SSR markers. Can. J. Plant Sci. 95: 387–395. Association mapping identifies quantitative trait loci (QTL) based on the strength of linkage disequilibrium (LD) between markers and functional polymorphisms across a set of diverse germplasms. In this study, we used association mapping to detect QTL and genome-wide simple sequence repeat (SSR) markers linked to seed coat colour and oil content in a population of 217 oilseed rape (Brassica napus L.) accessions. We corrected for the population structure of B. napus using 389 genome-wide SSR markers. In total, 25 and 11 SSR markers linked to seed coat colour and oil content were detected, respectively, and these two sets of markers were in different linkage groups. Nine of these markers for seed coat colour spanned the major QTL region for seed coat colour, and been mapped to chromosome A9. Six of these markers showed high levels of association with both seed coat colour and oil content, and markers H081N08.8 and KS20291 were mapped to the major QTL region for seed coat colour on chromosome A9. Another marker, CB10364, was in high LD with all determined seed coat colour and oil content traits, and was mapped to the co-localized QTL region for them on chromosome A8. These data indicate that seed coat colour was found to be an important contributor to seed oil content. Further, we show that association mapping using a heterogeneous set of genotypes is a suitable approach for complementing and enhancing previously obtained QTL information for marker-assisted selection.

QTLMiner: QTL database curation by mining tables in literature.

Figures and tables in biomedical literature record vast amounts of important experiment results. In scientific papers, for example, quantitative trait locus (QTL) information is usually presented in tables. However, most of the popular text-mining methods focus on extracting knowledge from unstructured free text. As far as we know, there are no published works on mining tables in biomedical literature. In this article, we propose a method to extract QTL information from tables and plain text found in literature. Heterogeneous and complex tables were converted into a structured database, combined with information extracted from plain text. Our method could greatly reduce labor burdens involved with database curation. We applied our method on a soybean QTL database curation, from which 2278 records were extracted from 228 papers with a precision rate of 96.9% and a recall rate of 83.3%, F value for the method is 89.6%.

Quantitative trait locus analysis of leaf morphological characters, yield-related traits, and secondary metabolite contents in Eucommia ulmoides.

Eucommia ulmoides is cultivated for the production of eucommia rubber and Chinese herbal drugs. Molecular breeding methods, such as marker-assisted selection (MAS), have the potential to improve the efficiency of E. ulmoides breeding. Quantitative trait locus (QTL) analysis was applied to identify marker-trait associations for E. ulmoides using an F1 mapping population of 152 individuals derived from a cross between the wild genotype Xiaoye and the cultivar Qinzhong No. 1. A total of 78 QTLs were identified for 12 leaf traits involving morphology, yield, and secondary metabolites. Phenotypic variance explained by individual QTLs ranged from 10.4 to 53.3%. Fifteen QTL clusters, each harboring loci controlling at least two leaf traits, were detected across nine linkage groups. Co-location of these QTLs may be due to pleiotropy or linkage. Three main QTL regions for secondary metabolites were consistently identified each year. QTL information from this study furthers our understanding of the genetic architecture of these economically important traits and of MAS in E. ulmoides breeding.

Inheritance and quantitative trait locus analysis of low-light tolerance in cucumber (Cucumis sativus L.).

The low-light tolerance index was investigated in a set of 123 F2:3 lines during the seedling stage across 2 seasons, and the heredity of low-light tolerance was assessed via different ge-netic analysis methods. The results of the classical analysis showed that low-light tolerance is controlled by an additive-dominant poly-gene, and the polygenic inheritance rate of separate generations was >30%. In addition, 5 quantitative trait loci (QTLs) exhibited a low-light tolerance index across both seasons, including 2 QTLs (Llti1.1 and Llti1.2) on the 1st linkage group (variances of 6.0 and 9.5%) and 3 QTLs (Llti2.1, Llti2.1, and Llti2.1) on the 2nd linkage group (variances of 10.1-14.0%). The classical analysis method and QTL information on the heredity of low-light tolerance showed that it is controlled by several major genes and a mini-polygene. The results will facilitate the breeding of resistance to low-light stress in cucumber.

Predicting maize kernel number using QTL information

Most maize yield variations are explained by changes in the number of established kernels. Kernel number is, in turn, highly dependent upon ear biomass accumulation around flowering. Both are quantitative traits highly influenced by the environment. Determining the genetic basis of quantitative traits is complex because of usual genetic×environment interactions (GxE). Crop physiology models are proposed to help overcome this problem, as they are structured to predict consequences of GxE interactions based on dynamic responses. We studied the genetic basis of maize kernel number determination at the plant level by conducting two quantitative trait loci (QTL) analysis: (i) on final traits per se (kernel number per plant, KNP, and ear biomass per plant, EB) and (ii) on specific model parameters of well-documented curves describing KNP and EB response to plant growth around flowering. Quantitative trait loci for KNP, EB and model parameters relating KNP and EB to plant growth were determined for 125 RILs of the IBM Syn4 (B73×Mo17) at two environments. We later grew several of these RILs and others from the same population not included in the QTL analysis and attempted to predict EB and KNP based on QTL information coming from each analysis. We hypothesized that doing the QTL analysis on crop physiology model parameters that describe the response of KNP and EB to plant growth is better than using direct QTL information.All traits showed significant variation, and both analyses detected several QTL for the studied traits. Associated QTL for EB and KNP per se did not localize with QTL detected for model parameters. This is the first report describing genomic regions for key physiological traits related to maize biomass partitioning around flowering and kernel set efficiency per unit of accumulated EB 15 days after anthesis. Quantitative trait loci information of model parameters helped to predict accumulated EB and KNP with higher accuracy (r2=0.13 and 0.12, p<0.001, for EB and KNP, respectively) than trying to predict EB and KNP based on QTL detected on final traits per se (r2<0.01 and <0.01, p>0.10, for EB and KNP, respectively). However, predictions using an average crop physiology model parameter across genotypes and individual RIL plant growth gave the highest accuracy (r2=0.46 and 0.37, p<0.001, for EB and KNP, respectively). As such, we identified chromosome areas including potentially relevant genes involved in maize KNP determination, but this information helped to predict KNP at different environments only partially, suggesting other approaches might be needed.

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.).

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold cross-validations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays.

Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.

BackgroundMastitis is a major disease of dairy cattle occurring in response to environmental exposure to infective agents with a great economic impact on dairy industry. Somatic cell count (SCC) and its log transformation in somatic cell score (SCS) are traits that have been used as indirect measures of resistance to mastitis for decades in selective breeding. A selective DNA pooling (SDP) approach was applied to identify Quantitative Trait Loci (QTL) for SCS in Valdostana Red Pied cattle using the Illumina Bovine HD BeadChip.ResultsA total of 171 SNPs reached the genome-wide significance for association with SCS. Fifty-two SNPs were annotated within genes, some of those involved in the immune response to mastitis. On BTAs 1, 2, 3, 4, 9, 13, 15, 17, 21 and 22 the largest number of markers in association to the trait was found. These regions identified novel genomic regions related to mastitis (1-Mb SNP windows) and confirmed those already mapped. The largest number of significant SNPs exceeding the threshold for genome-wide significant signal was found on BTA 15, located at 50.43-51.63 Mb.ConclusionsThe genomic regions identified in this study contribute to a better understanding of the genetic control of the mastitis immune response in cattle and may allow the inclusion of more detailed QTL information in selection programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-014-0106-7) contains supplementary material, which is available to authorized users.

A new method to infer causal phenotype networks using QTL and phenotypic information.

In the context of genetics and breeding research on multiple phenotypic traits, reconstructing the directional or causal structure between phenotypic traits is a prerequisite for quantifying the effects of genetic interventions on the traits. Current approaches mainly exploit the genetic effects at quantitative trait loci (QTLs) to learn about causal relationships among phenotypic traits. A requirement for using these approaches is that at least one unique QTL has been identified for each trait studied. However, in practice, especially for molecular phenotypes such as metabolites, this prerequisite is often not met due to limited sample sizes, high noise levels and small QTL effects. Here, we present a novel heuristic search algorithm called the QTL+phenotype supervised orientation (QPSO) algorithm to infer causal directions for edges in undirected phenotype networks. The two main advantages of this algorithm are: first, it does not require QTLs for each and every trait; second, it takes into account associated phenotypic interactions in addition to detected QTLs when orienting undirected edges between traits. We evaluate and compare the performance of QPSO with another state-of-the-art approach, the QTL-directed dependency graph (QDG) algorithm. Simulation results show that our method has broader applicability and leads to more accurate overall orientations. We also illustrate our method with a real-life example involving 24 metabolites and a few major QTLs measured on an association panel of 93 tomato cultivars. Matlab source code implementing the proposed algorithm is freely available upon request.

GEPdb: a database for investigating the ternary association of genotype, gene expression and phenotype.

GEPdb integrates both genome-wide association studies and expression quantitative trait loci information, the two primary sources of genome-wide mapping for genotype-phenotype and genotype-expression associations together with phenotype-associated gene lists. The GEPdb provides simultaneous interpretation of both genetic risks and potential gene regulatory pathways toward phenotypic outcome by establishing the ternary relationship of genotype-expression-phenotype (GEP). The analytic scope is further extended by linkage disequilibrium from five different populations of the international HapMap Project. http://ercsbweb.ewha.ac.kr/gepdb.

Genetic diversity and association mapping of seed vigor in rice (Oryza sativa L.)

Seed vigor is closely related to direct seeding in rice (Oryza sativa L.). Previous quantitative trait locus (QTL) studies for seed vigor were mainly derived from bi-parental segregating populations and no report from natural populations. In this study, association mapping for seed vigor was performed on a selected sample of 540 rice cultivars (419 from China and 121 from Vietnam). Population structure was estimated on the basis of 262 simple sequence repeat (SSR) markers. Seed vigor was evaluated by root length (RL), shoot length (SL) and shoot dry weight in 2011 and 2012. Abundant phenotypic and genetic diversities were found in the studied population. The population was divided into seven subpopulations, and the levels of linkage disequilibrium (LD) ranged from 10 to 80 cM. We identified 27 marker-trait associations involving 18 SSR markers for three traits. According to phenotypic effects for alleles of the detected QTLs, elite alleles were mined. These elite alleles could be used to design parental combinations and the expected results would be obtained by pyramiding or substituting the elite alleles per QTL (apart from possible epistatic effects). Our results demonstrate that association mapping can complement and enhance previous QTL information for marker-assisted selection and breeding by design.

Quantitative trait loci mapping for flag leaf traits in rice using a chromosome segment substitution line population

AbstractTo study the genetic basis of rice flag leaf morphology, quantitative genetic analysis was conducted in a population of 37 chromosome segment substitution lines (CSSLs) of indica elite variety ‘Habataki’ in the background of japonica cultivar ‘Sasanishiki’ across three different environments. The CSSLs showed normal distribution with transgressive segregation, indicating that these four traits are controlled by polygenes. Moreover, analyses of variance showed that these traits were highly influenced by the growing environment, which are typical for polygenic quantitative traits. Seven quantitative trait loci (QTLs) on four chromosomes were detected in total: four for flag leaf width, one for flag leaf area and two for flag leaf angle. Two key QTLs, qFLW4 and qFLAG5 controlling flag leaf width and angle, respectively, were identified in all three environments. These QTLs could provide useful information for marker‐assisted selection in improving the performance of plant architecture with regard to leaf angle and area. Moreover, developed CSSLs with these QTLs information are also useful research materials to reveal the importance of leaf morphology in relation to grain yield.

Major QTLs associated with green stem disorder insensitivity of soybean (Glycine max (L.) Merr.).

Green stem disorder (GSD) is one of the most serious syndromes affecting soybean (Glycine max) cultivation in Japan. In GSD, stems remain green even when pods mature. When soybean plants develop GSD, seed surfaces are soiled by tissue fluid and seed quality is deteriorated during machine harvesting. We performed quantitative trait locus (QTL) analyses for GSD insensitivity using recombinant inbred lines (RILs; n = 154) derived from a cross between an insensitive line (‘Touhoku 129’) and a sensitive leading cultivar (‘Tachinagaha’) during a 6-year evaluation. Three effective QTLs were detected. The influences of these QTLs were in the following order: qGSD1 (LG_H) > qGSD2 (LG_F) > qGSD3 (LG_L). At these three QTLs, ‘Touhoku 129’ genotypes exhibited more GSD insensitivity than ‘Tachinagaha’ genotypes. The lower incidence of GSD for ‘Touhoku129’ was attributable primarily to these three QTLs because RILs harboring a ‘Touhoku 129’ genotype at the three QTLs exhibited a GSD incidence similar to that of ‘Touhoku 129.’ Although a limitation of this study is that only one mapping population was evaluated, this QTL information and the flanking markers of these QTLs would be effective tools for resolving GSD in soybean breeding programs.

LSD 2.0: an update of the leaf senescence database

This manuscript describes an update of the leaf senescence database (LSD) previously featured in the 2011 NAR Database Issue. LSD provides comprehensive information concerning senescence-associated genes (SAGs) and their corresponding mutants. We have made extensive annotations for these SAGs through both manual and computational approaches. Recently, we updated LSD to a new version LSD 2.0 (http://www.eplantsenescence.org/), which contains 5356 genes and 322 mutants from 44 species, an extension from the previous version containing 1145 genes and 154 mutants from 21 species. In the current version, we also included several new features: (i) Primer sequences retrieved based on experimental evidence or designed for high-throughput analysis were added; (ii) More than 100 images of Arabidopsis SAG mutants were added; (iii) Arabidopsis seed information obtained from The Arabidopsis Information Resource (TAIR) was integrated; (iv) Subcellular localization information of SAGs in Arabidopsis mined from literature or generated from the SUBA3 program was presented; (v) Quantitative Trait Loci information was added with links to the original database and (vi) New options such as primer and miRNA search for database query were implemented. The updated database will be a valuable and informative resource for basic research of leaf senescence and for the manipulation of traits of agronomically important plants.

Quantitative trait loci (QTLs) analysis of palm oil fatty acid composition in an interspecific pseudo-backcross from Elaeis oleifera (H.B.K.) Cortés and oil palm (Elaeis guineensis Jacq.)

We chose an Elaeis interspecific pseudo-backcross of first generation (E. oleifera × E. guineensis) × E. guineensis to identify quantitative trait loci (QTLs) for fatty acid composition of palm oil. A dense microsatellite linkage map of 362 loci spanned 1.485 cM, representing the 16 pairs of homologous chromosomes in the Elaeis genus from which we traced segregating alleles from both E. oleifera and E. guineensis grandparents. The relative linear orders of mapped loci suggested the probable absence of chromosome rearrangements between the E. oleifera and E. guineensis genomes. A total of 19 QTL associated to the palm oil fatty acid composition were evidenced. The QTL positions and the species origin as well as the estimated effects of the QTL marker alleles were in coherence with the knowledge of the oil biosynthesis pathway in plants and with the individual phenotypic correlations between the traits. The mapping of chosen Elaeis key genes related to oleic acid C18:1, using intra-gene SNPs, supported several QTLs underlying notably FATA and SAD enzymes. The high number of hyper-variable SSR loci of known relative linear orders and the QTL information make these resources valuable for such mapping study in other Elaeis breeding materials.

Detecção de locos de características quantitativas nos cromossomos 1, 2, 3, 12, 14, 15 e X de suínos: características de desempenho

Mapeou-se quantitative trait loci (QTL) associados a características de desempenho nos cromossomos 1, 2, 3, 12, 14, 15 e X de suínos pertencentes a uma população F2, formada a partir do cruzamento entre dois machos da raça naturalizada brasileira Piau e 18 fêmeas comerciais (Landrace x Large White x Pietrain). O mapa genético de ligação da população foi construído após a genotipagem dos animais para 35 marcadores microssatélites. As estimativas do conteúdo de informação polimórfica indicaram que os marcadores microssatélites foram adequados para as análises de QTL. Os dados foram analisados pelo mapeamento por intervalo usando-se o programa GridQTL. Encontraram-se seis QTL, sendo que o QTL genômico para idade ao abate atingiu a significância de 5% de probabilidade. As informações dos QTL detectados neste estudo são úteis para identificar genes que podem ser usados em conjunto com os métodos convencionais de seleção, aumentar a acurácia deles e prover uma compreensão dos fenótipos produtivos de suínos.

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

BackgroundHybrid 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.ResultsFour 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’.ConclusionBeyond 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.