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To investigate the quenching sensitivity of the 2195 Al–Li alloy rolled sheet and guide the design of the quenching process, the time–temperature-property (TTP) curves of this material were researched through interrupted quenching experiments. The differential scanning calorimetry (DSC) and transmission electron microscope (TEM) were used to characterize the evolution of precipitates during isothermal treatment. The results of this essay demonstrated that the nose temperature of 2195 Al–Li alloy is around 370 °C and the temperature range of quenching sensitivity is 340 °C to 400 °C. The microstructure observation revealed that the T1 particles precipitate and grow rapidly at the temperature from 340 to 400 °C, which is due to the high nucleation rate of phase and fast solute diffusion kinetics, especially at the nose temperature. The needle-shaped θ′/θ″ and T1 particles grow up quickly as the isothermal preservation time prolonged, leading to the decrease of the supersaturated solid solution of the matrix. This will reduce the number of the age-induced precipitate and weaken the subsequent age hardening effect. Therefore, the rate of cooling should be increased in the quenching sensitivity range (340–400 °C) to inhibit the precipitation of the second phase and obtain excellent mechanical properties. While in other temperature ranges, the cooling rate should be decreased appropriately to reduce residual stress. The appropriate average cooling rate is recommended to be around 13 °C s−1 at the temperature from 340 to 400 °C.

Domesticates are an excellent model for understanding biological consequences of rapid climate change. Maize (Zea mays ssp. mays) was domesticated from a tropical grass yet is widespread across temperate regions today. We investigate the biological basis of temperate adaptation in diverse structured nested association mapping (NAM) populations from China, Europe (Dent and Flint) and the United States as well as in the Ames inbred diversity panel, using days to flowering as a proxy. Using cross-population prediction, where high prediction accuracy derives from overall genomic relatedness, shared genetic architecture, and sufficient diversity in the training population, we identify patterns in predictive ability across the five populations. To identify the source of temperate adapted alleles in these populations, we predict top associated genome-wide association study (GWAS) identified loci in a Random Forest Classifier using independent temperate–tropical North American populations based on lines selected from Hapmap3 as predictors. We find that North American populations are well predicted (AUC equals 0.89 and 0.85 for Ames and USNAM, respectively), European populations somewhat well predicted (AUC equals 0.59 and 0.67 for the Dent and Flint panels, respectively) and that the Chinese population is not predicted well at all (AUC is 0.47), suggesting an independent adaptation process for early flowering in China. Multiple adaptations for the complex trait days to flowering in maize provide hope for similar natural systems under climate change.

Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over 10 years, through testing yield in 40–66 lines each year at 6–14 locations with 38–41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, was used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11032-022-01287-8.

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

Apple industries suffer from major apple diseases because of widely planted susceptible cultivars. Developed disease-resistant cultivars that often carry only a single source of resistance are not expected to be durable over time. Cultivars with multiple sources of resistance are often commercially unacceptable due to unsatisfactory fruit quality alleles inherited from unimproved and improved parents. To improve fruit quality, approximately five modified backcrossing generations have been used with phenotypic selection for offspring with the least proportion of unimproved genome and elite fruit quality. Modified backcrossing is time-consuming owing to the long juvenility of apple. Unimproved parents are always assumed to carry undesirable alleles in addition to the targeted resistance allele. To efficiently identify favorable offspring each generation, DNA-based markers would be useful. Locus-specific DNA tests are unavailable to detect many sources of resistance alleles. Known numbers of DNA segments from unimproved parents could help subsequent parent selection among offspring because of the direct connection to the probability of eliminating such segments each generation. Accurately estimating the proportion and number of unimproved segments requires precise information on genomic positions of recombinations that can be detected with effective genetic marker sets. High-resolution and genome-wide apple SNP arrays can be used to characterize unimproved DNA segments present in disease-resistant offspring to efficiently achieve durable resistance and elite fruit quality. To hasten apple flowering, a rapid generation cycling approach with transgenic genetic stocks is available. Using these tools is expected to effectively exploit additional unimproved germplasm toward apple genetic improvement.

Background Dual-purpose cattle are more adaptive to environmental challenges than single-purpose dairy or beef cattle. Balance among milk, reproductive, and mastitis resistance traits in breeding programs is therefore more critical for dual-purpose cattle to increase net income and maintain well-being. With dual-purpose Xinjiang Brown cattle adapted to the Xinjiang Region in northwestern China, we conducted genome-wide association studies (GWAS) to dissect the genetic architecture related to milk, reproductive, and mastitis resistance traits. Phenotypic data were collected for 2410 individuals measured during 1995–2017. By adding another 445 ancestors, a total of 2855 related individuals were used to derive estimated breeding values for all individuals, including the 2410 individuals with phenotypes. Among phenotyped individuals, we genotyped 403 cows with the Illumina 150 K Bovine BeadChip. Results GWAS were conducted with the FarmCPU (Fixed and random model circulating probability unification) method. We identified 12 markers significantly associated with six of the 10 traits under the threshold of 5% after a Bonferroni multiple test correction. Seven of these SNPs were in QTL regions previously identified to be associated with related traits. One identified SNP, BovineHD1600006691, was significantly associated with both age at first service and age at first calving. This SNP directly overlapped a QTL previously reported to be associated with calving ease. Within 160 Kb upstream and downstream of each significant SNP identified, we speculated candidate genes based on functionality. Four of the SNPs were located within four candidate genes, including CDH2, which is linked to milk fat percentage, and GABRG2, which is associated with milk protein yield. Conclusions These findings are beneficial not only for breeding through marker-assisted selection, but also for genome editing underlying the related traits to enhance the overall performance of dual-purpose cattle.

Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT .

Key message We report a repertoire of diverse aneuploids harbored by a newly synthesized segmental allotetraploid rice population with fully sequenced sub-genomes and demonstrate their retention features and phenotypic consequences. Abstract Aneuploidy, defined as unequal numbers of different chromosomes, is a large-effect genetic variant and may produce diverse cellular and organismal phenotypes. Polyploids are more permissive to chromosomal content imbalance than their diploid and haploid counterparts, and therefore, may enable more in-depth investigation of the phenotypic consequences of aneuploidy. Based on whole-genome resequencing, we identify that ca. 40% of the 312 selfed individual plants sampled from an early generation rice segmental allotetraploid population are constitutive aneuploids harboring 55 distinct aneuploid karyotypes. We document that gain of a chromosome is more prevalent than loss of a chromosome, and the 12 rice chromosomes have distinct tendencies to be in an aneuploid state. These properties of aneuploidy are constrained by multiple factors including the number of genes residing on the chromosome and predicted functional connectivity with other chromosomes. Two broad categories of aneuploidy-associated phenotypes are recognized: those shared by different aneuploids, and those associated with aneuploidy of a specific chromosome. A repertoire of diverse aneuploids in the context of a segmental allotetraploid rice genome with fully sequenced sub-genomes provides a tractable resource to explore the roles of aneuploidy in nascent polyploid genome evolution and helps to decipher the mechanisms conferring karyotypic stabilization on the path to polyploid speciation and towards artificial construction of novel polyploid crops. Electronic supplementary material The online version of this article (10.1007/s00122-018-3077-7) contains supplementary material, which is available to authorized users.

Maize (Zea mays, L.) cultivation has expanded greatly from tropical to temperate zones; however, its sensitivity to chilling often results in decreased germination rates, weak seedlings with reduced survival rates, and eventually lower yields. We conducted germination tests on the maize-282-diverse-panel (282 inbred lines) under normal (25 °C) and chilling (8 °C) conditions. Three raw measurements of germination were recorded under each condition: 1) germination rate, 2) days to 50% germination, and 3) germination index. Three relative traits were derived as indicators of cold-tolerance. By using the 2,271,584 single nucleotide polymorphisms (SNPs) on the panel from previous studies, and genome-wide association studies by using FarmCPU R package to identify 17 genetic loci associated with cold tolerance. Seven associated SNPs hit directly on candidate genes; four SNPs were in high linkage disequilibrium with candidate genes within 366 kb. In total, 18 candidate genes were identified, including 10 candidate genes supported by previous QTL studies and five genes supported by previous gene cloning studies in maize, rice, and Arabidopsis. Three new candidate genes revealed by two associated SNPs were supported by both QTL analyses and gene cloning studies. These candidate genes and associated SNPs provide valuable resources for future studies to develop cold-tolerant maize varieties.

Background Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. Results To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. Conclusions This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1289-9) contains supplementary material, which is available to authorized users.

Selection of recombinant inbred lines (RILs) from elite hybrids is a key method in maize breeding especially in developing countries. The RILs are normally derived by repeated self-pollination and selection. In this study, we first investigated the accuracy of different models in predicting the performance of F(1) hybrids between RILs derived from two elite maize inbred lines Zong3 and 87-1, and then compared these models through simulation using a wider range of genetic models. Results indicated that appropriate prediction models depended on genetic architecture, e.g., combined model using breeding value and genome-wide prediction (BV+GWP) has the highest prediction accuracy for high V(D)/V(A) ratio (0.5) traits. Theoretical studies demonstrated that different components of genetic variance were captured by different prediction models, which in turn explained the accuracy of these models in predicting the F(1) hybrid performance. Based on genome-wide prediction model (GWP), 114 untested F(1) hybrids possibly having higher grain yield than the original F(1) hybrid Yuyu22 (the single cross between Zong3 and 87-1) have been identified and recommended for further field test.

At harvest traits such as seed weight are the sum of development and responses to stresses over the growing season and particularly during the reproductive phase of growth. The aim here was to measure quantitative trait loci (QTL) underlying the seed weight from early development to drying post harvest. One hundred forty-three F(5) derived recombinant inbred lines (RILs) developed from the cross of soybean cultivars 'Charleston' and 'Dongnong 594' were used for the analysis of QTL underlying mean 100-seed weight at six different developmental stages. QTL x Environment interactions (QE) were analyzed by a mixed genetic mode based on 3 years' data. At an experiment-wise threshold of a=0.05 and by single-point analysis 94 QTL unaffected by QE underlay the mean seed weight at different developmental stages. Sixty-eight QTL affected by QE that also underlay mean seed weight were identified. From the 162 QTL 42 could be located on 12 linkage groups by composite interval mapping (LOD>2.0). The numbers, locations and types of the QTL and the genetic effects were different at each developmental stage. On linkage group C2 the distantly linked QTL swC2-1, swC2-2 and swC2-3 each affected mean seed weight throughout the different developmental stages. The DNA markers linked to the QTL possessed potential for use in marker-assisted selection for soybean seed size. The identification of QTL with genetic main effects and QE interaction effects suggested that such interactions might significantly alter seed weight during seed development.

Background Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most fatal pests of soybean (Glycine max (L.) Merr.) worldwide and causes huge loss of soybean yield each year. Multiple sources of resistance are urgently needed for effective management of SCN via the development of resistant cultivars. The aim of the present study was to investigate the genetic architecture of resistance to SCN HG Type 0 (race 3) and HG Type 1.2.3.5.7 (race 4) in landraces and released elite soybean cultivars mostly from China. Results A total of 440 diverse soybean landraces and elite cultivars were screened for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7. Exactly 131 new sources of SCN resistance were identified. Lines were genotyped by SNP markers detected by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach. A total of 36,976 SNPs were identified with minor allele frequencies (MAF) > 4 % that were present in 97 % of all the genotypes. Genome-wide association mapping showed that a total of 19 association signals were significantly related to the resistance for the two HG Types. Of the 19 association signals, eight signals overlapped with reported QTL including Rhg1 and Rhg4 genes. Another eight were located in the linked regions encompassing known QTL. Three QTL were found that were not previously reported. The average value of female index (FI) of soybean accessions with resistant alleles was significantly lower than those with susceptible alleles for each peak SNP. Disease resistance proteins with leucine rich regions, cytochrome P450s, protein kinases, zinc finger domain proteins, RING domain proteins, MYB and WRKY transcription activation families were identified. Such proteins may participate in the resistant reaction to SCN and were frequently found in the tightly linked genomic regions of the peak SNPs. Conclusions GWAS extended understanding of the genetic architecture of SCN resistance in multiple genetic backgrounds. Nineteen association signals were obtained for the resistance to the two Hg Types of SCN. The multiple beneficial alleles from resistant germplasm sources will be useful for the breeding of cultivars with improved resistance to SCN. Analysis of genes near association signals may facilitate the recognition of the causal gene(s) underlying SCN resistances. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1800-1) contains supplementary material, which is available to authorized users.

Background The inheritance of most human diseases and agriculturally important traits is controlled by many genes with small effects. Identifying these genes, while simultaneously controlling false positives, is challenging. Among available statistical methods, the mixed linear model (MLM) has been the most flexible and powerful for controlling population structure and individual unequal relatedness (kinship), the two common causes of spurious associations. The introduction of the compressed MLM (CMLM) method provided additional opportunities for optimization by adding two new model parameters: grouping algorithms and number of groups. Results This study introduces another model parameter to develop an enriched CMLM (ECMLM). The parameter involves algorithms to define kinship between groups (that is, kinship algorithms). The ECMLM calculates kinship using several different algorithms and then chooses the best combination between kinship algorithms and grouping algorithms. Conclusion Simulations show that the ECMLM increases statistical power. In some cases, the magnitude of power gained by using ECMLM instead of CMLM is larger than the improvement found by using CMLM instead of MLM. Electronic supplementary material The online version of this article (doi:10.1186/s12915-014-0073-5) contains supplementary material, which is available to authorized users.