BioMercator: integrating genetic maps and QTL towards discovery of candidate genes

Evaluation of genes from eIF4E and eIF4G multigenic families as potential candidates for partial resistance QTLs to Rice yellow mottle virus in rice

QTLs for partial resistance to Rice yellow mottle virus (RYMV) in rice were mapped in two populations of doubled-haploid lines (DHLs) and recombinant inbred lines (RILs) derived from the same cross but evaluated for different resistance criteria (virus content and symptom severity). An integrative map was used to compare the two genetic maps and a global analysis of both populations was performed. Most of the QTLs previously identified in DHL population were confirmed with increased significance and precision. As many recent studies evidenced the role of eukaryotic translation initiation factors (eIF) of 4E and 4G families in plant susceptibility to RNA viruses, we checked if these genes co-locate with QTLs of resistance to RYMV. Their systematic in silico identification was carried out on the rice genome and their physical locations were compared to QTL positions on the integrative map. In order to confirm or not the co-locations observed, the analysis was completed by evaluation of near-isogenic lines, QTL fine mapping and sequencing of candidate genes. Three members from eIF4G family could be retained as reliable candidates whereas eIF4E genes, commonly found to govern resistances in other plant/virus interactions, were discarded. Together with the recent identification of an eIF(iso)4G as a major resistance gene, data suggests an important role of genes from eIF4G family in rice resistance to RYMV but does not exclude the contribution of factors different from the translation initiation complex.

Candidate gene identification approach: progress and challenges

Although it has been widely applied in identification of genes responsible for biomedically, economically, or even evolutionarily important complex and quantitative traits, traditional candidate gene approach is largely limited by its reliance on the priori knowledge about the physiological, biochemical or functional aspects of possible candidates. Such limitation results in a fatal information bottleneck, which has apparently become an obstacle for further applications of traditional candidate gene approach on many occasions. While the identification of candidate genes involved in genetic traits of specific interest remains a challenge, significant progress in this subject has been achieved in the last few years. Several strategies have been developed, or being developed, to break the barrier of information bottleneck. Recently, being a new developing method of candidate gene approach, digital candidate gene approach (DigiCGA) has emerged and been primarily applied to identify potential candidate genes in some studies. This review summarizes the progress, application software, online tools, and challenges related to this approach.

Using genotype x nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints

Lower market prices and environmental concerns now orientate wheat (Triticum aestivum L.) breeding programs towards low input agricultural practices, and more particularly low nitrogen (N) input management. Such programs require knowledge of the genetic determination of plant reaction to N deficiency. Our aim was to characterize the genetic basis of N use efficiency and genotype x N interactions. The detection of QTL for grain yield, grain protein yield and their components was performed on a mapping population of 222 doubled haploid lines (DH), obtained from the cross between an N stress tolerant variety and an N stress sensitive variety. Experiments on the population were carried out in seven different environments, and in each case under high (N(+)) and low (N(-)) N supplies. In total, 233 QTL were detected for traits measured in each combination of environment and N supply, for "global" interaction variables (N(+)-N(-) and N(-)/N(+)), for sensitivity to N stress and for performance under N-limited conditions which were assessed using factorial regression parameters. The 233 QTL were detected on the whole genome and clustered into 82 genome regions. The dwarfing gene (Rht-B1), the photoperiod sensitivity gene (Ppd-D1) and the awns inhibitor gene (B1) coincided with regions that contained the highest numbers of QTL. Non-interactive QTL were detected on linkage groups 3D, 4B, 5A1 and 7B2. Interactive QTL were revealed by interaction or factorial regression variables (2D2, 3D, 5A1, 5D, 6A, 6B, 7B2) or by both variables (1B, 2A1, 2A2, 2D1, 4B, 5A2, 5B). The usefulness of QTL meta-analysis and factorial regression to study QTL x N interactions and the impact of Rht-B1, Ppd-D1 and B1, are discussed.

Meta-analysis of polyploid cotton QTL shows unequal contributions of subgenomes to a complex network of genes and gene clusters implicated in lint fiber development

QTL mapping experiments yield heterogeneous results due to the use of different genotypes, environments, and sampling variation. Compilation of QTL mapping results yields a more complete picture of the genetic control of a trait and reveals patterns in organization of trait variation. A total of 432 QTL mapped in one diploid and 10 tetraploid interspecific cotton populations were aligned using a reference map and depicted in a CMap resource. Early demonstrations that genes from the non-fiber-producing diploid ancestor contribute to tetraploid lint fiber genetics gain further support from multiple populations and environments and advanced-generation studies detecting QTL of small phenotypic effect. Both tetraploid subgenomes contribute QTL at largely non-homeologous locations, suggesting divergent selection acting on many corresponding genes before and/or after polyploid formation. QTL correspondence across studies was only modest, suggesting that additional QTL for the target traits remain to be discovered. Crosses between closely-related genotypes differing by single-gene mutants yield profoundly different QTL landscapes, suggesting that fiber variation involves a complex network of interacting genes. Members of the lint fiber development network appear clustered, with cluster members showing heterogeneous phenotypic effects. Meta-analysis linked to synteny-based and expression-based information provides clues about specific genes and families involved in QTL networks.

Developmental genes have pleiotropic effects on plant morphology and source capacity, eventually impacting on seed protein content and productivity in pea

Increasing pea (Pisum sativum) seed nutritional value and particularly seed protein content, while maintaining yield, is an important challenge for further development of this crop. Seed protein content and yield are complex and unstable traits, integrating all the processes occurring during the plant life cycle. During filling, seeds are the main sink to which assimilates are preferentially allocated at the expense of vegetative organs. Nitrogen seed demand is satisfied partly by nitrogen acquired by the roots, but also by nitrogen remobilized from vegetative organs. In this study, we evaluated the respective roles of nitrogen source capacity and sink strength in the genetic variability of seed protein content and yield. We showed in eight genotypes of diverse origins that both the maximal rate of nitrogen accumulation in the seeds and nitrogen source capacity varied among genotypes. Then, to identify the genetic factors responsible for seed protein content and yield variation, we searched for quantitative trait loci (QTL) for seed traits and for indicators of sink strength and source nitrogen capacity. We detected 261 QTL across five environments for all traits measured. Most QTL for seed and plant traits mapped in clusters, raising the possibility of common underlying processes and candidate genes. In most environments, the genes Le and Afila, which control internode length and the switch between leaflets and tendrils, respectively, determined plant nitrogen status. Depending on the environment, these genes were linked to QTL of seed protein content and yield, suggesting that source-sink adjustments depend on growing conditions.

Identification of quantitative trait loci influencing aerial morphogenesis in the model legume Medicago truncatula

In many legume crops, especially in forage legumes, aerial morphogenesis defined as growth and development of plant organs, is an essential trait as it determines plant and seed biomass as well as forage quality (protein concentration, dry matter digestibility). Medicago truncatula is a model species for legume crops. A set of 29 accessions of M. truncatula was evaluated for aerial morphogenetic traits. A recombinant inbred lines (RILs) mapping population was used for analysing quantitative variation in aerial morphogenetic traits and QTL detection. Genes described to be involved in aerial morphogenetic traits in other species were mapped to analyse co-location between QTLs and genes. A large variation was found for flowering date, morphology and dynamics of branch elongation among the 29 accessions and within the RILs population. Flowering date was negatively correlated to main stem and branch length. QTLs were detected for all traits, and each QTL explained from 5.2 to 59.2% of the phenotypic variation. A QTL explaining a large part of genetic variation for flowering date and branch growth was found on chromosome 7. The other chromosomes were also involved in the variation detected in several traits. Mapping of candidate genes indicates a co-location between a homologue of Constans gene or a flowering locus T (FT) gene and the QTL of flowering date on chromosome 7. Other candidate genes for several QTLs are described.

MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments

BACKGROUND

Integration of multiple results from Quantitative Trait Loci (QTL) studies is a key point to understand the genetic determinism of complex traits. Up to now many efforts have been made by public database developers to facilitate the storage, compilation and visualization of multiple QTL mapping experiment results. However, studying the congruency between these results still remains a complex task. Presently, the few computational and statistical frameworks to do so are mainly based on empirical methods (e.g. consensus genetic maps are generally built by iterative projection).

RESULTS

In this article, we present a new computational and statistical package, called MetaQTL, for carrying out whole-genome meta-analysis of QTL mapping experiments. Contrary to existing methods, MetaQTL offers a complete statistical process to establish a consensus model for both the marker and the QTL positions on the whole genome. First, MetaQTL implements a new statistical approach to merge multiple distinct genetic maps into a single consensus map which is optimal in terms of weighted least squares and can be used to investigate recombination rate heterogeneity between studies. Secondly, assuming that QTL can be projected on the consensus map, MetaQTL offers a new clustering approach based on a Gaussian mixture model to decide how many QTL underly the distribution of the observed QTL.

CONCLUSION

We demonstrate using simulations that the usual model choice criteria from mixture model literature perform relatively well in this context. As expected, simulations also show that this new clustering algorithm leads to a reduction in the length of the confidence interval of QTL location provided that across studies there are enough observed QTL for each underlying true QTL location. The usefulness of our approach is illustrated on published QTL detection results of flowering time in maize. Finally, MetaQTL is freely available at http://bioinformatics.org/mqtl.

Most significant genome regions involved in the control of earliness traits in bread wheat, as revealed by QTL meta-analysis

Earliness is one of the most important adaptation traits in plant breeding. Our purpose was to identify the genome regions of bread wheat involved in the control of earliness and its three components: photoperiod sensitivity (PS), vernalization requirement (VR) and intrinsic earliness (IE). A QTL meta-analysis was carried out to examine the replicability of QTL across 13 independent studies and to propose meta-QTL (MQTL). Initial QTL were projected on a recent consensus map (2004). Quality criteria were proposed to assess the reliability of this projection. These criteria were based on the distances between markers in the QTL regions. Chromosomes of groups 2 and 5 had a greater incidence on earliness control as they carry the known, major genes Ppd and Vrn. Other chromosome regions played an intermediate role in earliness control: 4A [heading date (HD) Meta-QTL], 4B (HD MQTL), 2B (VR MQTL) and 5B (IE MQTL). Markers at this four MQTL should prove helpful in marker-assisted selection, to better control earliness.

Genomic regions involved in response to grain yield selection at high and low nitrogen fertilization in maize

In order to validate the role of genomic regions involved in nitrogen use efficiency and detected in a population of recombinant inbred lines (RIL), we have applied from the same population a recurrent selection for adaptation to low N-input (N0) and to high N-input (N1). Variation of allele frequency at neutral marker during the two cycles of recurrent selection may provide information about markers linked to QTLs. Significant temporal variation of allele frequency was investigated using the test of Waples, which tests the hypothesis of genetic drift versus selection. Most genomic regions (12/19) responding to selection were detected for selection at high N-input and only two were common to selection at high and low N-inputs. This was consistent with the greater grain yield response to selection observed for the population selected under high N-input compared with the population selected under low N-input, when they were evaluated at high N-fertilization. In contrast, when they were evaluated at low N-input both types of selection gave similar yield. As was expected, in the first cycle we observed selection of markers linked to grain yield QTLs. In the course of the second cycle three situations were observed: the confirmation of most regions already selected in C1 including all C1 regions overlapping with grain yield QTLs; the non-confirmation of some C1 regions (2/9); and the identification of new genomic zones (10/17). The detected marker-QTL associations revealed the consistency of the involvement of some traits, such as root architecture and glutamine synthetase activity, which would be of major importance for grain yield setting whatever the nitrogen fertilization.

Using mating designs to uncover QTL and the genetic architecture of complex traits

Analysis of quantitative trait loci (QTL) affecting complex traits is often pursued in single-cross experiments. For most purposes, including breeding, some assessment is desired of the generalizability of the QTL findings and of the overall genetic architecture of the trait. Single-cross experiments provide a poor basis for these purposes, as comparison across experiments is hampered by segregation of different allelic combinations among different parents and by context-dependent effects of QTL. To overcome this problem, we combined the benefits of QTL analysis (to identify genomic regions affecting trait variation) and classic diallel analysis (to obtain insight into the general inheritance of the trait) by analyzing multiple mapping families that are connected via shared parents. We first provide a theoretical derivation of main (general combining ability (GCA)) and interaction (specific combining ability (SCA)) effects on F(2) family means relative to variance components in a randomly mating reference population. Then, using computer simulations to generate F(2) families derived from 10 inbred parents in different partial-diallel designs, we show that QTL can be detected and that the residual among-family variance can be analyzed. Standard diallel analysis methods are applied in order to reveal the presence and mode of action (in terms of GCA and SCA) of undetected polygenes. Given a fixed experiment size (total number of individuals), we demonstrate that QTL detection and estimation of the genetic architecture of polygenic effects are competing goals, which should be explicitly accounted for in the experimental design. Our approach provides a general strategy for exploring the genetic architecture, as well as the QTL underlying variation in quantitative traits.

Comparison of quantitative trait loci for adaptive traits between oak and chestnut based on an expressed sequence tag consensus map

A comparative genetic and QTL mapping was performed between Quercus robur L. and Castanea sativa Mill., two major forest tree species belonging to the Fagaceae family. Oak EST-derived markers (STSs) were used to align the 12 linkage groups of the two species. Fifty-one and 45 STSs were mapped in oak and chestnut, respectively. These STSs, added to SSR markers previously mapped in both species, provided a total number of 55 orthologous molecular markers for comparative mapping within the Fagaceae family. Homeologous genomic regions identified between oak and chestnut allowed us to compare QTL positions for three important adaptive traits. Colocation of the QTL controlling the timing of bud burst was significant between the two species. However, conservation of QTL for height growth was not supported by statistical tests. No QTL for carbon isotope discrimination was conserved between the two species. Putative candidate genes for bud burst can be identified on the basis of colocations between EST-derived markers and QTL.

Mapping of a spontaneous mutation for early flowering time in maize highlights contrasting allelic series at two-linked QTL on chromosome 8

Only a few mutations affecting flowering time have been detected in maize. We analyzed a spontaneous early mutation, vgt-f7p, which appeared during production of the inbred line F7. This mutation shortens the time from planting to flowering by about 100 growing degree days (GDD), and reduces the number of nodes. It therefore seems to affect the timing of meristem differentiation from a vegetative to a reproductive state. It was mapped to a 6 cM confidence interval on chromosome 8, using a QTL mapping approach. QTL analysis of a mapping population generated by crossing the mutant F7 line (F7p) and the Gaspé flint population showed that vgt-f7p is probably allelic to vgt1, a QTL described in previous studies, and affects earliness more strongly than the Gaspé allele at vgt1. Global analysis of the QTL in the region suggested that there may be two consensus QTL, vgt1 and vgt2. These two QTL have contrasting allelic effects: rare alleles conferring extremely early flowering at vgt1 vs. greater diversity and milder effects at locus vgt2. Finally, detailed syntenic analysis showed that the vgt1 region displays a highly conserved duplicated region on chromosome 6, which also plays an important role in maize flowering time variation. The cloning of vgt1 should, therefore, also facilitate the analysis of the molecular basis of variation due to this second region.

A composite linkage map from two crosses for the species complex Picea mariana x Picea rubens and analysis of synteny with other Pinaceae

Four individual linkage maps were constructed from two crosses for the species complex Picea mariana (Mill.) B.S.P. x Picea rubens Sarg in order to integrate their information into a composite map and to compare with other Pinaceae. For all individual linkage maps, 12 major linkage groups were recovered with 306 markers per map on average. Before building the composite linkage map, the common male parent between the two crosses made it possible to construct a reference linkage map to validate the relative position of homologous markers. The final composite map had a length of 2,319 cM (Haldane) and contained a total of 1,124 positioned markers, including 1,014 AFLPs, 3 RAPDs, 53 SSRs, and 54 ESTPs, assembled into 12 major linkage groups. Marker density of the composite map was statistically homogenous and was much higher (one marker every 2.1 cM) than that of the individual linkage maps (one marker every 5.7 to 7.1 cM). Synteny was well conserved between individual, reference, and composite linkage maps and 94% of homologous markers were colinear between the reference and composite maps. The combined information from the two crosses increased by about 24% the number of anchor markers compared to the information from any single cross. With a total number of 107 anchor markers (SSRs and ESTPs), the composite linkage map is a useful starting point for large-scale genome comparisons at the intergeneric level in the Pinaceae. Comparisons of this map with those in Pinus and Pseudotsuga allowed the identification of one breakdown in synteny where one linkage group homologous to both Picea and Pinus corresponded to two linkage groups in Pseudotsuga. Implications for the evolution of the Pinaceae genome are discussed.

Linkage mapping of 1454 new maize candidate gene Loci

Bioinformatic analyses of maize EST sequences have highlighted large numbers of candidate genes putatively involved in agriculturally important traits. To contribute to ongoing efforts toward mapping of these genes, we used two populations of intermated recombinant inbred lines (IRILs), which allow a higher map resolution than nonintermated RILs. The first panel (IBM), derived from B73 x Mo17, is publicly available from the Maize Genetics Cooperation Stock Center. The second panel (LHRF) was developed from F2 x F252 to map loci monomorphic on IBM. We built framework maps of 237 loci from the IBM panel and 271 loci from the LHRF panel. Both maps were used to place 1454 loci (1056 on map IBM_Gnp2004 and 398 on map LHRF_Gnp2004) that corresponded to 954 cDNA probes previously unmapped. RFLP was mostly used, but PCR-based methods were also performed for some cDNAs to map SNPs. Unlike in usual IRIL-based maps published so far, corrected meiotic centimorgan distances were calculated, taking into account the number of intermating generations undergone by the IRILs. The corrected sizes of our framework maps were 1825 cM for IBM_Gnp2004 and 1862 cM for LHRF_Gnp2004. All loci mapped on LHRF_Gnp2004 were also projected on a consensus map IBMconsensus_Gnp2004. cDNA loci formed clusters near the centromeres except for chromosomes 1 and 8.

CARHTA GENE: multipopulation integrated genetic and radiation hybrid mapping

CAR(H)(T)A GENE: is an integrated genetic and radiation hybrid (RH) mapping tool which can deal with multiple populations, including mixtures of genetic and RH data. CAR(H)(T)A GENE: performs multipoint maximum likelihood estimations with accelerated expectation-maximization algorithms for some pedigrees and has sophisticated algorithms for marker ordering. Dedicated heuristics for framework mapping are also included. CAR(H)(T)A GENE: can be used as a C++ library, through a shell command and a graphical interface. The XML output for companion tools is integrated.

AVAILABILITY

The program is available free of charge from www.inra.fr/bia/T/CarthaGene for Linux, Windows and Solaris machines (with Open Source).

CONTACT

tschiex@toulouse.inra.fr.

Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome

Genetic architecture of flowering time in maize was addressed by synthesizing a total of 313 quantitative trait loci (QTL) available for this trait. These were analyzed first with an overview statistic that highlighted regions of key importance and then with a meta-analysis method that yielded a synthetic genetic model with 62 consensus QTL. Six of these displayed a major effect. Meta-analysis led in this case to a twofold increase in the precision in QTL position estimation, when compared to the most precise initial QTL position within the corresponding region. The 62 consensus QTL were compared first to the positions of the few flowering-time candidate genes that have been mapped in maize. We then projected rice candidate genes onto the maize genome using a synteny conservation approach based on comparative mapping between the maize genetic map and japonica rice physical map. This yielded 19 associations between maize QTL and genes involved in flowering time in rice and in Arabidopsis. Results suggest that the combination of meta-analysis within a species of interest and synteny-based projections from a related model plant can be an efficient strategy for identifying new candidate genes for trait variation.

MCQTL: multi-allelic QTL mapping in multi-cross design

The aim of the MCQTL software package is to perform QTL mapping in multi-cross designs. It allows the analysis of the usual populations derived from inbred lines and can link the families by assuming that the QTL locations are the same in all of them. Moreover, a diallel modelling of the QTL genotypic effects is allowed in multiple related families. The implemented model is a linear regression model. A composite interval mapping and an iterative QTL mapping are implemented to deal with multiple QTL models. Marker cofactor selections by forward or backward stepwise methods are implemented as well as computation of threshold test value by permutation.

AVAILABILITY

The program is available on request after signing a licence agreement; free of charge for academic and non-profit organizations at http://www.genoplante.org (Bioinformatics products).