BackgroundIn sexually reproducing organisms, meiotic crossovers ensure the proper segregation of chromosomes and contribute to genetic diversity by shuffling allelic combinations. Such genetic reassortment is exploited in breeding to combine favorable alleles, and in genetic research to identify genetic factors underlying traits of interest via linkage or association-based approaches. Crossover numbers and distributions along chromosomes vary between species, but little is known about their intraspecies variation.ResultsHere, we report on the variation of recombination rates between 22 European maize inbred lines that belong to the Dent and Flint gene pools. We genotype 23 doubled-haploid populations derived from crosses between these lines with a 50 k-SNP array and construct high-density genetic maps, showing good correspondence with the maize B73 genome sequence assembly. By aligning each genetic map to the B73 sequence, we obtain the recombination rates along chromosomes specific to each population. We identify significant differences in recombination rates at the genome-wide, chromosome, and intrachromosomal levels between populations, as well as significant variation for genome-wide recombination rates among maize lines. Crossover interference analysis using a two-pathway modeling framework reveals a negative association between recombination rate and interference strength.ConclusionsTo our knowledge, the present work provides the most comprehensive study on intraspecific variation of recombination rates and crossover interference strength in eukaryotes. Differences found in recombination rates will allow for selection of high or low recombining lines in crossing programs. Our methodology should pave the way for precise identification of genes controlling recombination rates in maize and other organisms.

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3-4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.

In tomato (Solanum lycopersicum) fruit, the number of locules (cavities containing seeds that are derived from carpels) varies from two to up to 10 or more. Locule number affects fruit shape and size and is controlled by several quantitative trait loci (QTLs). The large majority of the phenotypic variation is explained by two of these QTLs, fasciated (fas) and locule number (lc), that interact epistatically with one another. FAS has been cloned, and mutations in the gene are described as key factors leading to the increase in fruit size in modern varieties. Here, we report the map-based cloning of lc. The lc QTL includes a 1,600-bp region that is located 1,080 bp from the 3′ end of WUSCHEL, which encodes a homeodomain protein that regulates stem cell fate in plants. The molecular evolution of lc showed a reduction of diversity in cultivated accessions with the exception of two single-nucleotide polymorphisms. These two single-nucleotide polymorphisms were shown to be responsible for the increase in locule number. An evolutionary model of locule number is proposed herein, suggesting that the fas mutation appeared after the mutation in the lc locus to confer the extreme high-locule-number phenotype.

Assessing the genetic variability of plant performance under heat and drought scenarios can contribute to reduce the negative effects of climate change. We propose here an approach that consisted of (1) clustering time courses of environmental variables simulated by a crop model in current (35 years 3 55 sites) and future conditions into six scenarios of temperature and water deficit as experienced by maize (Zea mays L.) plants; (2) performing 29 field experiments in contrasting conditions across Europe with 244 maize hybrids; (3) assigning individual experiments to scenarios based on environmental conditions as measured in each field experiment; frequencies of temperature scenarios in our experiments corresponded to future heat scenarios (+5°C); (4) analyzing the genetic variation of plant performance for each environmental scenario. Forty-eight quantitative trait loci (QTLs) of yield were identified by association genetics using a multi-environment multi-locus model. Eight and twelve QTLs were associated to tolerances to heat and drought stresses because they were specific to hot and dry scenarios, respectively, with low or even negative allelic effects in favorable scenarios. Twenty-four QTLs improved yield in favorable conditions but showed nonsignificant effects under stress; they were therefore associated with higher sensitivity. Our approach showed a pattern of QTL effects expressed as functions of environmental variables and scenarios, allowing us to suggest hypotheses for mechanisms and candidate genes underlying each QTL. It can be used for assessing the performance of genotypes and the contribution of genomic regions under current and future stress situations and to accelerate breeding for drought-prone environments.With climate changes, crops will be subjected to more frequent episodes of drought and high temperature that may threaten food security (IPCC, 2014). Reducing the impacts of these effects is an urgent priority that (not exclusively) involves the genetic progress of plant performance under heat and drought stresses (Tester and Langridge, 2010;Lobell et al., 2011). Because hundreds of new genotypes of most cereals are commercialized every year, a generic approach is needed to avoid an endless series of experiments assessing the performances of the newly released genotypes. A systematic exploration of the natural genetic diversity used in breeding can provide information usable for large groups of genotypes. This entails the identification, among the thousands of accessions existing in gene banks, of allelic variants exhibiting specific adaptation traits by addressing three questions: (1) Is there a genetic variability for yield and related traits in dry and hot environments? (2) Can this genetic variability be dissected into the effect of genomic regions (quantitative trait loci, QTLs), and (3) have these genomic

The efficiency of marker-assisted prediction of phenotypes has been studied intensively for different types of plant breeding populations. However, one remaining question is how to incorporate and counterbalance information from biparental and multiparental populations into model training for genome-wide prediction. To address this question, we evaluated testcross performance of 1652 doubled-haploid maize (Zea mays L.) lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments. The lines are arranged in two diverse half-sib panels representing two major European heterotic germplasm pools. The data set contains 10 related biparental dent families and 11 related biparental flint families generated from crosses of maize lines important for European maize breeding. With this new data set we analyzed genome-based best linear unbiased prediction in different validation schemes and compositions of estimation and test sets. Further, we theoretically and empirically investigated marker linkage phases across multiparental populations. In general, predictive abilities similar to or higher than those within biparental families could be achieved by combining several half-sib families in the estimation set. For the majority of families, 375 half-sib lines in the estimation set were sufficient to reach the same predictive performance of biomass yield as an estimation set of 50 full-sib lines. In contrast, prediction across heterotic pools was not possible for most cases. Our findings are important for experimental design in genome-based prediction as they provide guidelines for the genetic structure and required sample size of data sets used for model training. IN the context of quantitative trait locus (QTL) mapping, multiparental populations have been suggested to be advantageous over biparental families due to their greater allelic diversity and the possibility of evaluating allelic effects in multiple genetic backgrounds (Muranty 1996;Xu 1998;Verhoeven et al. 2006). Especially if the multiparental population consists of several families connected by common parents, they can provide greater power of QTL detection and better resolution of QTL localization compared to individual families (Rebai and Goffinet 1993;Jannink and Jansen 2001;Blanc et al. 2006;Yu et al. 2008;Bardol et al. 2013;Mackay et al. 2014). In the context of genome-based prediction (Meuwissen et al. 2001), accuracies achieved within large biparental families are assumed to be the maximum that can be obtained with a given sample size (Crossa et al. 2014), because of medium allele frequencies, absence of genetic substructure, and equal linkage phases between markers and functional polymorphisms. However, prediction accuracies of newly generated progenies from different crosses will be poor. This is especially true if the respective germplasm exhibits broad allelic diversity and is unrelated to the biparental family from which single nucleotide polymorphism (...

Multiparental designs combined with dense genotyping of parents have been proposed as a way to increase the diversity and resolution of quantitative trait loci (QTL) mapping studies, using methods combining linkage disequilibrium information with linkage analysis (LDLA). Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize (Zea mays L.). Ten biparental dent families (N = 841) and 11 biparental flint families (N = 811) were genotyped with 56,110 single nucleotide polymorphism markers and evaluated as test crosses with the central line of the reciprocal design for biomass yield, plant height, and precocity. Alleles at candidate QTL were defined as (i) parental alleles, (ii) haplotypic identity by descent, and (iii) single-marker groupings. Between five and 16 QTL were detected depending on the model, trait, and genetic group considered. In the flint design, a major QTL (R 2 = 27%) with pleiotropic effects was detected on chromosome 10, whereas other QTL displayed milder effects (R 2 , 10%). On average, the LDLA models detected more QTL but generally explained lower percentages of variance, consistent with the fact that most QTL display complex allelic series. Only 15% of the QTL were common to the two designs. A joint analysis of the two designs detected between 15 and 21 QTL for the five traits. Of these, between 27 for silking date and 41% for tasseling date were significant in both groups. Favorable allelic effects detected in both groups open perspectives for improving biomass production. MOST traits of agronomic interest present a continuous variation resulting from the sum of the effects of various quantitative trait loci (QTL). Mapping these QTL is a first step toward elucidating their molecular nature and offers important application perspectives for marker-assisted breeding. QTL mapping started in plants with segregating families derived from the cross of two inbred lines (Lander and Botstein 1989). However, such biparental designs address only a small portion of the diversity available (a maximum of two alleles can segregate at a given QTL) and the accuracy of QTL positions is usually poor. To overcome these limitations, Rebai and Goffinet (1993) and Charcosset et al. (1994) proposed models for joint QTL detection in several biparental families connected to each other by the use of common parental lines. When the number of parents is less than the number of families, connections can be taken into account to reduce the number of allelic effects to be estimated in the detection model. This increases power and accuracy of detection when QTL behave additively (see Blanc et al. 2006). However, such a model makes the assumption that each parental line carries a different allele, which limits its benefit when the number of parental lines is high relative to the number of families, a situation commonly encountered in breeding programs. Recent advances in sequencing and genotyping technologies make it possi...

Background: The natural phenotypic variability present in the germplasm of cultivated plants can be linked to molecular polymorphisms using association genetics. However it is necessary to consider the genetic structure of the germplasm used to avoid false association. The knowledge of genetic structure of plant populations can help in inferring plant evolutionary history. In this context, we genotyped 360 wild, feral and cultivated accessions with 20 simple sequence repeat markers and investigated the extent and structure of the genetic variation. The study focused on the red fruited tomato clade involved in the domestication of tomato and confirmed the admixture status of cherry tomatoes (Solanum lycopersicum var. cerasiforme). We used a nested sample strategy to set-up core collection maximizing the genetic diversity with a minimum of individuals.

Association mapping has been proposed as an efficient approach to assist in the identification of the molecular basis of agronomical traits in plants. For this purpose, we analyzed the phenotypic and genetic diversity of a large collection of tomato accessions including 44 heirloom and vintage cultivars (Solanum lycopersicum), 127 S. lycopersicum var. cerasiforme (cherry tomato) and 17 Solanum pimpinellifolium accessions. The accessions were genotyped using a SNPlex™ assay of 192 SNPs, among which 121 were informative for subsequent analysis. Linkage disequilibrium (LD) of pairwise loci and population structure were analyzed, and the association analysis between SNP genotypes and ten fruit quality traits was performed using a mixed linear model. High level of LD was found in the collection at the whole genome level. It was lower when considering only the 127 S. lycopersicum var. cerasiforme accessions. Genetic structure analysis showed that the population was structured into two main groups, corresponding to cultivated and wild types and many intermediates. The number of associations detected per trait varied, according to the way the structure was taken into account, with 0-41 associations detected per trait in the whole collection and a maximum of four associations in the S. lycopersicum var. cerasiforme accessions. A total of 40 associations (30 %) were co-localized with previously identified quantitative trait loci. This study thus showed the potential and limits of using association mapping in tomato populations.