Genome-wide recombination variation in biparental segregating and reciprocal backcross populations provides information for introgression breeding in Brassica napus

Variation in patterns of recombination in plant genomes provides information about species evolution,genetic diversity and crop improvement. We investigated meiotic crossovers generated in biparental segregating and reciprocal backcross populations of the allopolyploid genome of rapeseed(Brassica napus)(AACC, 2n = 38). A structured set of 1445 intercrossed lines was derived from two homozygous de novo genome-assembled parents that represented the major genetic clusters of semi-winter Chinese and winter European rapeseeds, and was used to increase QTL resolution and achieve genomic reciprocal introgression. A high-density genetic map constructed with 6161 genetic bins and anchored centromere regions was used to establish the pattern of recombination variation in each chromosome. Around 93%of the genome contained crossovers at a mean rate of 3.8 c M Mb^(-1), with the remaining 7% attributed to centromeres or low marker density. Recombination hotspots predominated in the A genome, including two-thirds of those associated with breeding introgression from B. rapa. Genetic background might affect recombination variation. Introgression of genetic diversity from European winter to Chinese semi-winter rapeseed showed an increase in crossover rate under the semi-winter environment. Evidence for an elevated recombination rate having historically contributed to selective trait improvement includes accumulation of favorable alleles for seed oil content on hotspots of chromosome A10. Conversely, strong artificial selection may affect recombination rate variation, as appears to be the case with a coldspot resulting from strong selection for glucosinolate alleles on A09. But the cold region would be promptly reactivated by crossing design indicated by the pedigree analysis. Knowledge of recombination hotspots and coldspots associated with QTL for 22 traits can guide selection strategies for introgression breeding between the two gene pools. These results and rich genomic resources broaden our understanding of recombination behavior in allopolyploids and may advance rapeseed genetic improvement.

Genome-wide prediction for hybrids between parents with distinguished difference on exotic introgressions in Brassica napus

Extensive exotic introgression could significantly enlarge the genetic distance of hybrid parental populations to promote strong heterosis.The goal of this study was to investigate whether genome-wide prediction can support pre-breeding in populations with exotic introgressions.We evaluated seed yield,seed yield related traits and seed quality traits of 363 hybrids of Brassica napus (AACC) derived from two parental populations divergent on massive exotic introgression of related species in three environments.The hybrids presented strong heterosis on seed yield,which was much higher than other investigated traits.Five genomic best linear unbiased prediction models considering the exotic introgression and different marker effects (additive,dominance,and epistatic effects) were constructed to test the prediction ability for different traits of the hybrids.The analysis showed that the trait complexity,exotic introgression,genetic relationship between the training set and testing set,training set size,and environments affected the prediction ability.The models with best prediction ability for different traits varied.However,relatively high prediction ability (e.g.,0.728 for seed yield) was also observed when the simplest models were used,excluding the effects of the special exotic introgression and epistasis effect by5-fold cross validation,which would simplify the prediction for the trait with complex architecture for hybrids with exotic introgression.The results provide novel insights and strategies for genome-wide prediction of hybrids between genetically distinct parent groups with exotic introgressions.

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

In recent years,much attention has been devoted to the development and applications of smart grid technologies,with special emphasis on flexible resources such as distributed generations(DGs),energy storages,active loads,and electric vehicles(EVs).Demand response(DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations.Despite their potential contributions to power system secure and economic operation,uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned.In addition,the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments.In this context,this paper aims to propose a DR based congestion management strategy for smart distribution systems.The general framework and procedures for distribution congestion management is first presented.A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established.Subsequently,the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices.The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.