Silvio Salvi

Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array

High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker–trait associations in mapping experiments. We developed a genotyping array including about 90 000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence–absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize

Flowering time is a fundamental trait of maize adaptation to different agricultural environments. Although a large body of information is available on the map position of quantitative trait loci for flowering time, little is known about the molecular basis of quantitative trait loci. Through positional cloning and association mapping, we resolved the major flowering-time quantitative trait locus, Vegetative to generative transition 1 (Vgt1), to an ≈2-kb noncoding region positioned 70 kb upstream of an Ap2-like transcription factor that we have shown to be involved in flowering-time control. Vgt1 functions as a cis-acting regulatory element as indicated by the correlation of the Vgt1 alleles with the transcript expression levels of the downstream gene. Additionally, within Vgt1, we identified evolutionarily conserved noncoding sequences across the maize–sorghum–rice lineages. Our results support the notion that changes in distant cis-acting regulatory regions are a key component of plant genetic adaptation throughout breeding and evolution.

RFLP mapping of quantitative trait loci controlling abscisic acid concentration in leaves of drought-stressed maize (Zea mays L.)

Abstract Abscisic acid (ABA) concentration in leaves of drought-stressed plants is a quantitatively inherited trait. In order to identify quantitative trait loci (QTLs) controlling leaf ABA concentration (L-ABA) in maize, leaf samples were collected from 80 F3:4 families of the cross Os420 (high L-ABA)×IABO78 (low L-ABA) tested under drought conditions in field trials conducted over 2 years. In each year, leaf samples were collected at stem elongation and near anthesis. The genetic map obtained with 106 restriction fragment length polymorphism (RFLP) loci covered 1370 cM, which represented approximately 85% of the UMC maize map. Sixteen different QTLs with a LOD>2.0 were revealed in at least one sampling. Across samplings, only four QTLs significantly influenced L-ABA, accounting for 66% of the phenotypic variation and 76% of the genetic variation among families. At these QTLs, the alleles which increased L-ABA were contributed by Os420. The two most important QTLs were mapped on chromosome 2 near csu133 and csu109a. The effects associated with the QTL near csu133 were more pronounced near anthesis. The support intervals of the four primary QTLs for L-ABA did not overlap the presumed map position of mutants impaired in ABA biosynthesis.

Searching for quantitative trait loci controlling root traits in maize: a critical appraisal

The identification of quantitative trait loci (QTLs) for root traits can provide useful indications on their genetic basis and the associated effects on grain yield under different water regimes. Furthermore, the availability of molecular markers linked to QTLs controlling variation for root traits and grain yield will allow for the implementation of marker-assisted selection to improve productivity. In maize (Zea mays L.), four mapping populations have been investigated to locate QTLs for root traits under controlled conditions and/or in the field. A comparative analysis of the QTL results was carried out based on the availability of molecular markers common to the investigated populations and the UMC maize reference map. Several chromosome regions affected root traits in two or even three populations. A number of these regions also affected grain yield under well-watered and/or drought-stressed conditions. The most important QTL effects were detected on chromosome bins 1.03, 1.06, 1.08, 2.03, 2.04, 7.02, 8.06 and 10.04. Exploiting the syntenic information available for maize and rice, a number of QTLs for root traits described in rice were found to map in regions syntenic to a number of the listed maize chromosome bins (e.g., bin 2.04). The development of near isogenic lines (NILs) for the most important QTLs will allow to investigate whether the concomitant effects of the QTLs on root traits and grain yield are due to linkage and/or pleiotropy and, ultimately, may offer the opportunity to clone the genes underlying such QTLs. Although QTL analysis remains a resource-demanding undertaking, its integration with genomics and post-genomics approaches will play an increasingly important role for the identification of genes affecting root characteristics and grain yield in maize and for harnessing the favourable allelic variation at such loci.

Development and validation of a 20K single nucleotide polymorphism (SNP) whole genome genotyping array for apple (Malus × domestica Borkh).

High-density SNP arrays for genome-wide assessment of allelic variation have made high resolution genetic characterization of crop germplasm feasible. A medium density array for apple, the IRSC 8K SNP array, has been successfully developed and used for screens of bi-parental populations. However, the number of robust and well-distributed markers contained on this array was not sufficient to perform genome-wide association analyses in wider germplasm sets, or Pedigree-Based Analysis at high precision, because of rapid decay of linkage disequilibrium. We describe the development of an Illumina Infinium array targeting 20K SNPs. The SNPs were predicted from re-sequencing data derived from the genomes of 13 Malus × domestica apple cultivars and one accession belonging to a crab apple species (M. micromalus). A pipeline for SNP selection was devised that avoided the pitfalls associated with the inclusion of paralogous sequence variants, supported the construction of robust multi-allelic SNP haploblocks and selected up to 11 entries within narrow genomic regions of ±5 kb, termed focal points (FPs). Broad genome coverage was attained by placing FPs at 1 cM intervals on a consensus genetic map, complementing them with FPs to enrich the ends of each of the chromosomes, and by bridging physical intervals greater than 400 Kbps. The selection also included ∼3.7K validated SNPs from the IRSC 8K array. The array has already been used in other studies where ∼15.8K SNP markers were mapped with an average of ∼6.8K SNPs per full-sib family. The newly developed array with its high density of polymorphic validated SNPs is expected to be of great utility for Pedigree-Based Analysis and Genomic Selection. It will also be a valuable tool to help dissect the genetic mechanisms controlling important fruit quality traits, and to aid the identification of marker-trait associations suitable for the application of Marker Assisted Selection in apple breeding programs.

Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes

A previous study on maize F(2:3) families derived from Lo964xLo1016 highlighted one QTL in bin 1.06 (hereafter named root-yield-1.06) affecting root and agronomic traits of plants grown in well-watered (WW) and water-stressed (WS) conditions. Starting from different F(4) families, two pairs of near isogenic lines (NILs) were developed at root-yield-1.06. The objective of this study was to evaluate root-yield-1.06 effects across different water regimes, genetic backgrounds, and inbreeding levels. The NILs per se and their crosses with Lo1016 and Lo964 were tested in 2008 and 2009 near to Bologna, with the well-watered (WW) and water-stressed (WS) treatments providing, on average, 70 mm and 35 mm of water, respectively. For NILs per se, the interactions QTL x water regime and QTL x family were negligible in most cases; the QTL additive effects across families were significant for several traits, especially root clump weight. For NILs crosses, analogously to NILs per se, the interactions were generally negligible and the additive effects across water regimes and families were significant for most traits, especially grain yield. A meta-analysis carried out considering the QTLs described in this and previous studies inferred one single locus as responsible for the effects on roots and agronomic traits. Our results show that root-yield-1.06 has a major constitutive effect on root traits, plant vigour and productivity across water regimes, genetic backgrounds, and inbreeding levels. These features suggest that root-yield-1.06 is a valuable candidate for cloning the sequence underlying its effects and for marker-assisted selection to improve yield stability in maize.

Wheat syntenome unveils new evidences of contrasted evolutionary plasticity between paleo‐ and neoduplicated subgenomes

Bread wheat derives from a grass ancestor structured in seven protochromosomes followed by a paleotetraploidization to reach a 12 chromosomes intermediate and a neohexaploidization (involving subgenomes A, B and D) event that finally shaped the 21 modern chromosomes. Insights into wheat syntenome in sequencing conserved orthologous set (COS) genes unravelled differences in genomic structure (such as gene conservation and diversity) and genetical landscape (such as recombination pattern) between ancestral as well as recent duplicated blocks. Contrasted evolutionary plasticity is observed where the B subgenome appears more sensitive (i.e. plastic) in contrast to A as dominant (i.e. stable) in response to the neotetraploidization and D subgenome as supra-dominant (i.e. pivotal) in response to the neohexaploidization event. Finally, the wheat syntenome, delivered through a public web interface PlantSyntenyViewer at http://urgi.versailles.inra.fr/synteny-wheat, can be considered as a guide for accelerated dissection of major agronomical traits in wheat.

Sequence-based SNP genotyping in durum wheat.

Marker development for marker-assisted selection in plant breeding is increasingly based on next-generation sequencing (NGS). However, marker development in crops with highly repetitive, complex genomes is still challenging. Here we applied sequence-based genotyping (SBG), which couples AFLP®-based complexity reduction to NGS, for de novo single nucleotide polymorphisms (SNP) marker discovery in and genotyping of a biparental durum wheat population. We identified 9983 putative SNPs in 6372 contigs between the two parents and used these SNPs for genotyping 91 recombinant inbred lines (RILs). Excluding redundant information from multiple SNPs per contig, 2606 (41%) markers were used for integration in a pre-existing framework map, resulting in the integration of 2365 markers over 2607 cM. Of the 2606 markers available for mapping, 91% were integrated in the pre-existing map, containing 708 SSRs, DArT markers, and SNPs from CRoPS technology, with a map-size increase of 492 cM (23%). These results demonstrate the high quality of the discovered SNP markers. With this methodology, it was possible to saturate the map at a final marker density of 0.8 cM/marker. Looking at the binned marker distribution (Figure 2), 63 of the 268 10-cM bins contained only SBG markers, showing that these markers are filling in gaps in the framework map. As to the markers that could not be used for mapping, the main reason was the low sequencing coverage used for genotyping. We conclude that SBG is a valuable tool for efficient, high-throughput and high-quality marker discovery and genotyping for complex genomes such as that of durum wheat.

Genetic dissection of maize phenology using an intraspecific introgression library

BackgroundCollections of nearly isogenic lines where each line carries a delimited portion of a donor source genome into a common recipient genetic background are known as introgression libraries and have already shown to be instrumental for the dissection of quantitative traits. By means of marker-assisted backcrossing, we have produced an introgression library using the extremely early-flowering maize (Zea mays L.) variety Gaspé Flint and the elite line B73 as donor and recipient genotypes, respectively, and utilized this collection to investigate the genetic basis of flowering time and related traits of adaptive and agronomic importance in maize.ResultsThe collection includes 75 lines with an average Gaspé Flint introgression length of 43.1 cM. The collection was evaluated for flowering time, internode length, number of ears, number of nodes (phytomeres), number of nodes above the ear, number and proportion of nodes below the ear and plant height. Five QTLs for flowering time were mapped, all corresponding to major QTLs for number of nodes. Three additional QTLs for number of nodes were mapped. Besides flowering time, the QTLs for number of nodes drove phenotypic variation for plant height and number of nodes below and above the top ear, but not for internode length. A number of apparently Mendelian-inherited phenotypes were also observed.ConclusionsWhile the inheritance of flowering time was dominated by the well-known QTL Vgt1, a number of other important flowering time QTLs were identified and, thanks to the type of plant material here utilized, immediately isogenized and made available for fine mapping. At each flowering time QTL, early flowering correlated with fewer vegetative phytomeres, indicating the latter as a key developmental strategy to adapt the maize crop from the original tropical environment to the northern border of the temperate zone (southern Canada), where Gaspé Flint was originally cultivated. Because of the trait differences between the two parental genotypes, this collection will serve as a permanent source of nearly isogenic materials for multiple studies of QTL analysis and cloning.

Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

Optimization of root system architecture (RSA) traits is an important objective for modern wheat breeding. Linkage and association mapping for RSA in two recombinant inbred line populations and one association mapping panel of 183 elite durum wheat (Triticum turgidum L. var. durum Desf.) accessions evaluated as seedlings grown on filter paper/polycarbonate screening plates revealed 20 clusters of quantitative trait loci (QTLs) for root length and number, as well as 30 QTLs for root growth angle (RGA). Divergent RGA phenotypes observed by seminal root screening were validated by root phenotyping of field-grown adult plants. QTLs were mapped on a high-density tetraploid consensus map based on transcript-associated Illumina 90K single nucleotide polymorphisms (SNPs) developed for bread and durum wheat, thus allowing for an accurate cross-referencing of RSA QTLs between durum and bread wheat. Among the main QTL clusters for root length and number highlighted in this study, 15 overlapped with QTLs for multiple RSA traits reported in bread wheat, while out of 30 QTLs for RGA, only six showed co-location with previously reported QTLs in wheat. Based on their relative additive effects/significance, allelic distribution in the association mapping panel, and co-location with QTLs for grain weight and grain yield, the RSA QTLs have been prioritized in terms of breeding value. Three major QTL clusters for root length and number (RSA_QTL_cluster_5#, RSA_QTL_cluster_6#, and RSA_QTL_cluster_12#) and nine RGA QTL clusters (QRGA.ubo-2A.1, QRGA.ubo-2A.3, QRGA.ubo-2B.2/2B.3, QRGA.ubo-4B.4, QRGA.ubo-6A.1, QRGA.ubo-6A.2, QRGA.ubo-7A.1, QRGA.ubo-7A.2, and QRGA.ubo-7B) appear particularly valuable for further characterization towards a possible implementation of breeding applications in marker-assisted selection and/or cloning of the causal genes underlying the QTLs.