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Smulders, M. J. M.

Publications and source records attributed to Smulders, M. J. M..

4 recordsLinked to original sources

Two high-quality rose genomes underpin a novel Rosa pangenome to advance rose genomics, phylogenetics, and breeding

Rosa, belonging to the family Rosaceae, encompasses more than 150 species which are widely distributed in the northern hemisphere. Renowned for their beauty, roses are cultivated throughout the world for ornamental purposes and the production of essential oils and perfumes. Despite their cultural and commercial significance, the genomic resources of wild Rosa species have not been studied comprehensively, hampering the understanding of their genetic diversity, evolutionary history, and breeding potential. Here we report on high-quality de novo genomes for Rosa sericea and Rosa rugosa. By integrating these two de novo genomes with existing public genomic resources, we have built a Rosaceae panproteome and a Rosa pangenome (spanning wild, traditional garden, and modern rose lineages) using a De Bruijn graph (DBG)-based approach. A maximum likelihood (ML) phylogeny of 18 Rosa haplotypes based on 4,367 single-copy core homology groups (genes) provided robust evolutionary inference, confirming the basal position of R. sericea, and enabled a gene-based macrosynteny analysis across the pangenome. Our analyses revealed significant genomic diversity among species, extensive variation in core gene content, and lineage-specific transposable element (TE) expansion patterns that contribute to the variation in Rosa genome size and to species-specific adaptations. The pangenome also revealed biased diversification of homology groups potentially linked to phenotypic plasticity in Rosa. Specifically, our analysis of the rose scent-related gene family, NUDX1, uncovered its evolutionary trajectory in Rosa, in which TEs insertions provided putative novel regulatory elements that facilitated adaptive evolution in metabolic pathways. This pangenomic study deepens our understanding of the genetic diversity and evolution of traits within the Rosa genus. In addition, the findings lay the foundation for future efforts to understand the genetic mechanisms driving trait evolution, which can support rose breeding.

genomics↗

De novo whole-genome assembly of Chrysanthemum makinoi, a key wild ancestor to hexaploid Chrysanthemum

Chrysanthemum is among the top ten cut, potted and perennial garden flowers in the world. Despite this, to date, only the genomes of two wild diploid chrysanthemums have been sequenced and assembled. Here we present the most complete and contiguous chrysanthemum de novo assembly published so far, as well as a corresponding ab initio annotation. The wild diploid Chrysanthemum makinoi is thought to be one of the ancestors of the cultivated hexaploid varieties which are currently grown all around the world. Using a combination of Oxford Nanopore long reads, Pacific Biosciences long reads, Illumina short reads, Dovetail sequences and a genetic map, we assembled 3.1 Gb of its sequence into 9 pseudochromosomes, with an N50 of 330 Mb and BUSCO complete score of 92.1%. Our ab initio annotation pipeline predicted 95 074 genes and marked 80.0% of the genome as repetitive. This genome assembly of C. makinoi provides an important step forward in understanding the chrysanthemum genome, evolution and history.

genomics↗

Detecting quantitative trait loci and exploring chromosomal pairing in autopolyploids using polyqtlR

MotivationThe investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialised tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. ResultsHere we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent (IBD) probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualisation tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availability and implementationpolyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Contactpeter.bourke@wur.nl or chris.maliepaard@wur.nl Supplementary informationSupplementary data are available

genetics↗

polymapR: linkage analysis and genetic map construction from F1 populations of outcrossing polyploids

MotivationPolyploid species carry more than two copies of each chromosome, a condition found in many of the worlds most important crops. Genetic mapping in polyploids is more complex than in diploid species, resulting in a lack of available software tools. These are needed if we are to realise all the opportunities offered by modern genotyping platforms for genetic research and breeding in polyploid crops.\n\nResultspolymapR is an R package for genetic linkage analysis and integrated genetic map construction from bi-parental populations of outcrossing autopolyploids. It can currently analyse triploid, tetraploid and hexaploid marker datasets and is applicable to various crops including potato, leek, alfalfa, blueberry, chrysanthemum, sweet potato or kiwifruit. It can detect, estimate and correct for preferential chromosome pairing, and has been tested on high-density marker datasets from potato, rose and chrysanthemum, generating high-density integrated linkage maps in all of these crops.\n\nAvailability and ImplementationpolymapR is freely available under the general public license from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/packages=polymapR.\n\nContactChris Maliepaard chris.maliepaard@wur.nl or Roeland E. Voorrips roeland.voorrips@wur.nl

genetics↗