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Agropyron cristatum (2n=4x=28, PPPP) is a wild relative of common wheat which contains a large number of desirable genes that can be exploited for wheat improvement. Wheat–A. cristatum 2P alien translocation lines exhibit many desirable traits, such as small flag leaves, a high spikelet number and density, and a compact plant type. An agronomic trait evaluation and a genetic analysis were carried out on translocation lines and backcross populations of these lines carrying different translocation fragments. The results showed that a translocation fragment from 2PT-3 (2PL) reduced the length of the flag leaves, while translocation fragments from 2PT-3 (2PL) and 2PT-5 (2PL (0.60–1.00)) reduced the width of the flag leaves. A translocation fragment from 2PT-13 (2PS (0.18–0.36)) increased the length and area of the flag leaves. Translocation fragments from 2PT-3 (2PL) and 2PT-8 (2PL (0.86–1.00)) increased the density of spikelets. Translocation fragments from 2PT-7 (2PL (0.00–0.09)), 2PT-8 (2PL (0.86–1.00)), 2PT-10 (2PS), and 2PT-13 (2PS (0.18–0.36)) reduced plant height. This study provides a scientific basis for the effective utilization of wheat–A. cristatum translocation lines.

Cobalt-rich crusted deposits are found all over the world’s oceans, and their distribution is closely related to the submarine topography. The determination of crusting area is the basic work for the exploration and mining of these deposits. Many factors affect the accumulation of crusts, and topography is a crucial factor. Mineralization forecast requires comprehensive consideration of geological background and experts’ views and opinions, the prior knowledge of prospectors is the biggest factor affecting the results. In the course of ocean research, especially with the rapid development of space information technology, a huge amount of ocean data that cover about 70% of the total surface area have been accumulated rapidly; how to extract valuable information from large, fast, complex, and multisource data has become a hot topic in current ocean research. Machine learning- and deep learning-related research methods can read feature signs from mineral data to obtain existing mineral knowledge to further serve mine prediction work. Based on the study of terrain features of cobalt-rich crust in high-producing areas, the numerical matrix of altitude of 1 km2 ocean surface was obtained, with the geographical coordinates of cobalt-rich crust sites as the center. Using the analysis method of convolutional neural network, the numerical matrix is trained to learn regional features such as slope and flatness and to distinguish the cobalt-rich crust–crust site topography from other submarine topography. According to the training model, the high-producing cobalt-rich crusting area was predicted and better forecasting value is obtained. Meanwhile, the accuracy of the selection of crusting target area was improved by combining the influence of other factors.

Powdery mildew, caused by Blumeria graminis f. sp. tritici, is one of the most severe wheat diseases. Mining powdery mildew resistance genes in wheat cultivars and their appliance in breeding program is a promising way to control this disease. Genetic analysis revealed that a single dominant resistance gene named PmTm4 originated from Chinese wheat line Tangmai 4 confers resistance to prevailing isolates of B. graminis f. sp. tritici isolate E09. Detailed comparative genomics analyses helped to develop closely linked markers to PmTm4 and a fine genetic map was constructed using large F2 population, in which PmTm4 was located into a 0.66-cM genetic interval. The orthologous subgenome region of PmTm4 in Aegilops tauschii was identified, and two resistance gene analogs (RGA) were characterized from the corresponding sequence scaffolds of Ae. tauschii draft assembly. The closely linked markers and identified Ae. tauschii orthologs in the mapping interval provide an entry point for chromosome landing and map-based cloning of PmTm4.

Pre-harvest sprouting (PHS) occurs frequently in most of the wheat cultivation area worldwide, which severely reduces yield and end-use quality, resulting in substantial economic loss. In this study, quantitative trait loci (QTL) for PHS resistance were mapped using an available high-density single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) genetic linkage map developed from a 269 recombinant inbred lines (RILs) population of Yanda 1817×Beinong 6. Using phenotypic data on two locations (Beijing and Shijiazhuang, China) in two years (2012 and 2013 harvesting seasons), five QTLs, designated as QPhs.cau-3A.1, QPhs.cau-3A.2, QPhs.cau-5B, QPhs.cau-4A, and QPhs.cau-6A, for PHS (GP) were detected by inclusive composite interval mapping (ICIM) (LOD≥2.5). Two major QTLs, QPhs.cau-3A.2 and QPhs.cau-5B, were mapped on 3AL and 5BS chromosome arms, explaining 6.29–21.65% and 4.36–5.94% of the phenotypic variance, respectively. Precise mapping and comparative genomic analysis revealed that the TaVp-1A flanking region on 3AL is responsible for QPhs.cau-3A.2. SNP markers flanking QPhs.cau-3A.2 genomic region were developed and could be used for introgression of PHS tolerance into high yielding wheat varieties through marker-assisted selection (MAS).

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F 8 recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010-2014 by inclusive composite interval mapping (ICIM) (LOD ≥ 2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82-23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.