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Highlights d Gravity triggers asymmetric expression of SAUR family genes in soybean hypocotyls d SAUR19 subfamily genes are expressed asymmetrically in Arabidopsis hypocotyls in tropisms d SAUR19 subfamily genes are critical for hypocotyl gravitrophic and phototropic responses d ARF7 and ARF19 mediate the expression of SAUR19 subfamily genes in tropic responses

The successful application of heterosis in hybrid rice has dramatically improved rice productivity, but the genetic mechanism for heterosis in the hybrid rice remains unclear. In this study, we generated two populations of rice F1hybrids with present-day commercial hybrid parents, genotyped the parents with 50k SNP chip and genome resequencing, and recorded the phenotype of ∼2,000 hybrids at three field trials. By integrating these data with the collected genotypes of ∼4,200 rice landraces and improved varieties that were reported previously, we found that the male and female parents have different levels of genome introgressions from other rice subpopulations, includingindica,aus, andjaponica, therefore shaping heterotic loci in the hybrids. Among the introgressed exogenous genome, we found that heterotic loci, includingGhd8/DTH8,Gn1a, andIPA1existed in wild rice, but were significantly divergently selected among the rice subpopulations, suggesting these loci were subject to environmental adaptation. During modern rice hybrid breeding, heterotic loci were further selected by removing loci with negative effect and fixing loci with positive effect and pyramid breeding. Our results provide insight into the genetic basis underlying the heterosis of elite hybrid rice varieties, which could facilitate a better understanding of heterosis and rice hybrid breeding.

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SummaryAnalyses of genome variations with high-throughput assays have improved our understanding of genetic basis of crop domestication and identified the selected genome regions, but little is known about that of modern breeding, which has limited the usefulness of massive elite cultivars in further breeding. Here we deploy pedigree-based analysis of an elite rice, Huanghuazhan, to exploit key genome regions during its breeding. The cultivars in the pedigree were resequenced with 7.69 depth on average, and 2.1 million high-quality single nucleotide polymorphisms (SNPs) were obtained. Tracing the derivation of genome blocks with pedigree and information on SNPs revealed the chromosomal recombination during breeding, which showed that 26.22% of Huanghuazhan genome are strictly conserved key regions. These major effect regions were further supported by a QTL mapping of 260 recombinant inbred lines derived from the cross of Huanghuazhan and a very dissimilar cultivar, Shuanggui 36, and by the genome profile of eight cultivars and 36 elite lines derived from Huanghuazhan. Hitting these regions with the cloned genes revealed they include numbers of key genes, which were then applied to demonstrate how Huanghuazhan were bred after 30 years of effort and to dissect the deficiency of artificial selection. We concluded the regions are helpful to the further breeding based on this pedigree and performing breeding by design. Our study provides genetic dissection of modern rice breeding and sheds new light on how to perform genomewide breeding by design.

Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

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Improving breeding has been widely utilized in crop breeding and contributed to yield and quality improvement, yet few researches have been done to analyze genetic architecture underlying breeding improvement comprehensively. Here, we collected genotype and phenotype data of 99 cultivars from the complete pedigree including Huanghuazhan, an elite, high-quality, conventional indica rice that has been grown over 4.5 million hectares in southern China and from which more than 20 excellent cultivars have been derived. We identified 1,313 selective sweeps (SSWs) revealing four stage-specific selection patterns corresponding to improvement preference during 65 years, and 1113 conserved Huanghuazhan traceable blocks (cHTBs) introduced from different donors and conserved in >3 breeding generations were the core genomic regions for superior performance of Huanghuazhan. Based on 151 quantitative trait loci (QTLs) identified for 13 improved traits in the pedigree, we reproduced their improvement process in silico, highlighting improving breeding works well for traits controlled by major/major + minor effect QTLs, but was inefficient for traits controlled by QTLs with complex interactions or explaining low levels of phenotypic variation. These results indicate long-term breeding improvement is efficient to construct superior genetic architecture for elite performance, yet molecular breeding with designed genotype of QTLs can facilitate complex traits improvement.