ultivated peanut or groundnut (A. hypogaea L.) is among the most important oil and food legumes, grown on 25 million ha between latitudes 40° N and 40° S with annual production of ~46 million tons (http://www.fao.org/faostat/en/#home). It presumably was domesticated in South America ~6,000 years ago and then was widely distributed in post-Columbian times 1. Combining richness in seed oil (~46-58%) and protein (~22-32%), peanut is important in fighting malnutrition and ensuring food security.

A high-density SNP-based genetic linkage map was constructed and integrated with a previous map in the Tapidor x Ningyou7 (TNDH) Brassica napus population, giving a new map with a total of 2041 molecular markers and an average marker density which increased from 0.39 to 0.97 (0.82 SNP bin) per cM. Root and shoot traits were screened under low and ‘normal’ phosphate (Pi) supply using a ‘pouch and wick’ system, and had been screened previously in an agar based system. The P-efficient parent Ningyou7 had a shorter primary root length (PRL), greater lateral root density (LRD) and a greater shoot biomass than the P-inefficient parent Tapidor under both treatments and growth systems. Quantitative trait loci (QTL) analysis identified a total of 131 QTL, and QTL meta-analysis found four integrated QTL across the growth systems. Integration reduced the confidence interval by ~41%. QTL for root and shoot biomass were co-located on chromosome A3 and for lateral root emergence were co-located on chromosomes A4/C4 and C8/C9. There was a major QTL for LRD on chromosome C9 explaining ~18% of the phenotypic variation. QTL underlying an increased LRD may be a useful breeding target for P uptake efficiency in Brassica.

Key message A comprehensive linkage atlas for seed yield in rapeseed. AbstractMost agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-017-2911-7) contains supplementary material, which is available to authorized users.

Sugarcane (Saccharum spp.) supplies globally ∼80% of table sugar and 60% of bioethanol. Sugarcane orange rust and Sugarcane yellow leaf virus (SCYLV) are major sugarcane diseases, causing up to 50 and 40% yield losses, respectively. Sugarcane cultivars resistant to these diseases are needed to sustain sugarcane production in several regions. Dissecting DNA sequence variants controlling disease resistance provides a valuable tool for fulfilling a breeding strategy to develop resistant cultivars. In this study, we evaluated disease reactions to orange rust and SCYLV of a sugarcane diversity panel in repeated trials. We conducted a genome-wide association study between high-density markers and disease resistance reactions. We identified 91 putative DNA markers and 82 candidate genes significantly associated with resistance to one of the two diseases. These provide an important genetic resource for finding genes and molecular markers for disease resistance. Our results emphasized the importance of utilizing a wide germplasm collection for breeding resistant sugarcane cultivars.

Sugarcane (Saccharum spp.) is a highly energy-efficient crop primarily for sugar and bio-ethanol production. Sugarcane genetics and cultivar improvement have been extremely challenging largely due to its complex genomes with high polyploidy levels. In this study, we deeply sequenced the coding regions of 307 sugarcane germplasm accessions. Nearly five million sequence variations were catalogued. The average of 98× sequence depth enabled different allele dosages of sequence variation to be differentiated in this polyploid collection. With selected high-quality genome-wide SNPs, we performed population genomic studies and environmental association analysis. Results illustrated that the ancient sugarcane hybrids, S. barberi and S. sinense, and modern sugarcane hybrids are significantly different in terms of genomic compositions, hybridization processes and their potential ancestry contributors. Linkage disequilibrium (LD) analysis showed a large extent of LD in sugarcane, with 962.4 Kbp, 2739.2 Kbp and 3573.6 Kbp for S. spontaneum, S. officinarum and modern S. hybrids respectively. Candidate selective sweep regions and genes were identified during domestication and historical selection processes of sugarcane in addition to genes associated with environmental variables at the original locations of the collection. This research provided an extensive amount of genomic resources for sugarcane community and the in-depth population genomic analyses shed light on the breeding and evolution history of sugarcane, a highly polyploid species.

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An integrated dense genetic linkage map was constructed in a B. carinata population and used for comparative genome analysis and QTL identification for flowering time. An integrated dense linkage map of Brassica carinata (BBCC) was constructed in a doubled haploid population based on DArT-Seq(TM) markers. A total of 4,031 markers corresponding to 1,366 unique loci were mapped including 639 bins, covering a genetic distance of 2,048 cM. We identified 136 blocks and islands conserved in Brassicaceae, which showed a feature of hexaploidisation representing the suggested ancestral crucifer karyotype. The B and C genome of B. carinata shared 85 % of commonly conserved blocks with the B genome of B. nigra/B. juncea and 80 % of commonly conserved blocks with the C genome of B. napus, and shown frequent structural rearrangements such as insertions and inversions. Up to 24 quantitative trait loci (QTL) for flowering and budding time were identified in the DH population. Of these QTL, one consistent QTL (qFT.B4-2) for flowering time was identified in all of the environments in the J block of the B4 linkage group, where a group of genes for flowering time were aligned in A. thaliana. Another major QTL for flowering time under a winter-cropped environment was detected in the E block of C6, where the BnFT-C6 gene was previously localised in B. napus. This high-density map would be useful not only to reveal the genetic variation in the species with QTL analysis and genome sequencing, but also for other applications such as marker-assisted selection and genomic selection, for the African mustard improvement.

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