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In the investigation of stratigraphic reservoirs, a significant discrepancy frequently exists between the delineation of the formation pinch-out line as traced using the characteristics of seismic wave reflections and the actual location of the formation pinch-out line. This has been the main problem restricting further hydrocarbon exploration and development. In this study, Hala'alate Mountain on the northwestern margin of the Junggar Basin is taken as an example for carrying out the study of stratigraphic reservoirs by integrating logging, drilling, and 3D seismic data. On the one hand, in studies based on the identification of formation pinch-out points using seismic data, the identification error of reservoir pinch-out lines is reduced by the improved included angle extrapolation method by utilizing the half energy attribute. On the other hand, the Poisson's ratio curve is reconstructed using acoustic curves and oil-gas sensitive logging, then the reservoir oil-bearing facies zone is predicted using Poisson's ratio post-stack genetic inversion to comprehensively analyze the controlling factors of stratigraphic reservoirs. The study area mainly features structural lithologic reservoirs, structural stratigraphic reservoirs and stratigraphic overlaps that pinch out reservoirs. The boundary of a stratigraphic reservoir is affected by the dip angle of the unconformity surface, the formation dip angle, and other factors. The improved included angle extrapolation method improves the identification accuracy of stratigraphic overlap pinch-out reservoirs. The reservoir distribution then is calculated according to Poisson's ratio inversion, improving the prediction accuracy for the reservoir. This method improves the predictive effect for stratigraphic reservoirs and provides a new idea for the exploration and development of similar reservoirs. [ABSTRACT FROM AUTHOR]

Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, complex genetic traits can be improved by genomic selection (GS), which has become a practical and effective breeding tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought stress environments in northeast China. Adequate genetic variations were observed for all the target traits, but they were divergent across environments. Similarly, the heritability of the target trait also varied across years and environments, the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) were higher than those in 2020 (0.65, 0.53, 0.33) and cross-2-years (0.32, 0.26, 0.33). In total, three marker datasets, 11,865 SilicoDArT markers obtained from the DArT-seq platform, 7837 SNPs obtained from the DArT-seq platform, and 91,003 SNPs obtained from the GBS platform, were used for GS analysis after quality control. The results of phylogenetic trees showed that broad genetic diversity existed in the NCCP population. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the two-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For the trait with lower heritability, the prediction accuracy can be improved using the dataset filtered by linkage disequilibrium. For the same trait, the prediction accuracies estimated with two DArT marker datasets were consistently higher than that estimated with the GBS SNP dataset under the same genotyping cost. The prediction accuracy was improved by controlling population structure and marker quality, even though the marker density was reduced. The prediction accuracies were improved by more than 30% using the significant-associated SNPs. Due to the complexity of drought tolerance under the natural stress environments, multiple years of data need to be accumulated to improve prediction accuracy by reducing genotype-by-environment interaction. Modeling genotype-by-environment interaction into genomic prediction needs to be further developed for improving drought tolerance in maize. The results obtained from the present study provides valuable pathway for improving drought tolerance in maize using GS. [ABSTRACT FROM AUTHOR]

Key message: We lay the foundation for further research on maize resistance to Fusarium verticillioides cob rot by identifying a candidate resistance gene. Fusarium verticillioides ear rot is the most common type of maize ear rot in the Huanghuaihai Plain of China. Ear rot resistance includes cob and kernel resistance. Most of the current literature concentrates on kernel resistance, and genetic studies on cob resistance are scarce. We aimed on identifying the QTLs responsible for F. verticillioides cob rot (FCR) resistance. Twenty-eight genes associated with 48 single nucleotide polymorphisms (SNPs) were identified (P < 10−4) to correlate with FCR resistance using a whole-genome association study. The major quantitative trait locus, qRcfv2, for FCR resistance was identified on chromosome 2 through linkage mapping and was validated in near-isogenic line populations. Two candidate genes associated with two SNPs were detected in the qRcfv2 region with a lower threshold (P < 10−3). Through real-time fluorescence quantitative PCR, one candidate gene was found to have no expression in the cob but the other was expressed in response to F. verticillioides. These results lay a foundation for research on the resistance mechanisms of cob and provide resources for marker-assisted selection. [ABSTRACT FROM AUTHOR]

Phenotypic evaluation of the drought-tolerant traits and identification of the genetic markers associated with these traits in diverse germplasms are essential for developing drought-tolerant germplasm through molecular breeding. A collection of 210 off-PVP (no longer subject to Plant Variety Protection) maize inbred lines introduced from the USA (ALs) were genotyped using a low-coverage sequencing method and phenotyped in eight environments (location × year × treatment) in China. The CV of phenotypic data for six target traits varied from 5.34 to 20.69% under well-watered (WW) conditions and from 5.46 to 35.98% under drought-stressed (WS) conditions. ALs exhibited higher grain yield per plot (GY) under the WS conditions and premature characteristic compared with the local checks, which are important breeding targets in drought tolerance. Two subgroups, SS and NSS, were identified in this collection based on population structure analysis, PCA, and an NJ tree. A total of 413 trait-associated SNPs under the WW conditions and 696 SNPs under the WS conditions were detected in a GWAS (genome-wide association study) analysis, with the phenotypic variation explained by each SNP to the target traits varied from 10.02 to 25.40%. In the genomic prediction (GP) analysis, the prediction models incorporating trait-marker associations showed higher prediction accuracies than the prediction models using an equivalent number of randomly selected SNPs for all the six traits evaluated under both the WW and WS conditions. The results observed in this study provide valuable information for understanding the genetic variation of drought stress tolerance in maize, and show great potential to improve drought stress tolerance in maize via genomic selection. [ABSTRACT FROM AUTHOR]

Fusarium verticillioides can be transmitted via seeds and cause systemic infection in maize (Zea mays L.); its mycotoxin has harmful effects on animal and human health. We combined QTL mapping in recombinant inbred line (RIL) populations with a genome-wide association study (GWAS) of 217 diverse maize lines using 224,152 single nucleotide polymorphisms (SNPs) under controlled conditions to determine the genetic architecture of F. verticillioides seed rot (FSR) resistance. Our study identified 8 quantitative trait loci (QTLs) and 43 genes associated with 57 SNPs that were correlated with FSR resistance through linkage mapping and GWAS, respectively. Among these, there were three candidate genes, namely GRMZM2G0081223, AC213654.3&#95;FG004, and GRMZM2G099255, which were detected in both linkage mapping and GWAS. Furthermore, the near-isogenic lines (NILs) containing GRMZM2G0081223, which also had a susceptible parent background, were found to have a significantly improved level of resistance. In addition, the expression profile of the three candidate genes revealed that they all respond to the infection following inoculation with F. verticillioides. These genetic analyses indicate that FSR resistance is controlled by loci with minor effect, and the polymerization breeding of lines with beneficial alleles and candidate genes could improve FSR resistance in maize.