Genome-wide association study and development of molecular markers for yield and quality traits in peanut (Arachis hypogaea L.).

BACKGROUND: This study aims to decipher the genetic basis governing yield components and quality attributes of peanuts, a critical aspect for advancing molecular breeding techniques. Integrating genotype re-sequencing and phenotypic evaluations of seven yield components and two grain quality traits across four distinct environments allowed for the execution of a genome-wide association study (GWAS). RESULTS: The nine phenotypic traits were all continuous and followed a normal distribution. The broad heritability ranged from 88.09 to 98.08%, and the genotype-environment interaction effects were all significant. There was a highly significant negative correlation between protein content (PC) and oil content (OC). The 10× genome re-sequencing of 199 peanut accessions yielded a total of 631,988 high-quality single nucleotide polymorphisms (SNPs), with 374 significant SNP loci identified in association with the nine traits of interest. Notably, 66 of these pertinent SNPs were detected in multiple environments, and 48 of them were linked to multiple traits of interest. Five loci situated on chromosome 16 (Chr16) exhibited pleiotropic effects on yield traits, accounting for 17.64-32.61% of the observed phenotypic variation. Two loci on Chr08 were found to be strongly associated with protein and oil contents, accounting for 12.86% and 14.06% of their respective phenotypic variations, respectively. Linkage disequilibrium (LD) block analysis of these seven loci unraveled five nonsynonymous variants, leading to the identification of one yield-related candidate gene and two quality-related candidate genes. The correlation between phenotypic variation and SNP loci in these candidate genes was validated by Kompetitive allele-specific PCR (KASP) marker analysis. CONCLUSIONS: Overall, molecular markers were developed for genetic loci associated with yield and quality traits through a GWAS investigation of 199 peanut accessions across four distinct environments. These molecular tools can aid in the development of desirable peanut germplasm with an equilibrium of yield and quality through marker-assisted breeding.

Research on Time Series-Based Pipeline Ground Penetrating Radar Calibration Angle Prediction Algorithm.

The pipeline ground-penetrating radar stands as an indispensable detection device for ensuring underground space security. A wheeled pipeline robot is deployed to traverse the interior of urban underground drainage pipelines along their central axis. It is subject to influences such as resistance, speed, and human factors, leading to deviations in its posture. A guiding wheel is employed to rectify its roll angle and ensure the precise spatial positioning of defects both inside and outside the pipeline, as detected by the radar antenna. By analyzing its deflection factors and correction trajectories, the intelligent correction control of the pipeline ground-penetrating radar falls into the realm of nonlinear multi-constraint optimization. Consequently, a time-series-based correction angle prediction algorithm is proposed. The application of the long short-term memory (LSTM) deep learning model facilitates the prediction of correction angles and torque for the guiding wheel. This study compares the performance of LSTM with an autoregressive integrated moving average model under identical dataset conditions. The subsequent findings reveal a reduction of 4.11° and 8.25 N·m in mean absolute error, and a decrease of 10.66% and 7.27% in mean squared error for the predicted correction angles and torques, respectively. These outcomes are achieved utilizing the three-channel drainage pipeline ground-penetrating radar device with top antenna operating at 1.2 GHz and left/right antennas at 750 MHz. The LSTM prediction model intelligently corrects its posture. Experimental results demonstrate an average correction speed of 5 s and an average angular error of ±1°. It is verified that the model can correct its attitude in real-time with small errors, thereby enhancing the accuracy of ground-penetrating radar antennas in locating pipeline defects.

Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat (PPR) gene family in cultivated peanut (Arachis hypogaea L.).

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

Identification of quantitative trait loci and development of diagnostic markers for growth habit traits in peanut (Arachis hypogaea L.).

KEY MESSAGE: QTLs for growth habit are identified on Arahy.15 and Arahy.06 in peanut, and diagnostic markers are developed and validated for further use in marker-assisted breeding. Peanut is a unique legume crop because its pods develop and mature underground. The pegs derive from flowers following pollination, then reach the ground and develop into pods in the soil. Pod number per plant is influenced by peanut growth habit (GH) that has been categorized into four types, including erect, bunch, spreading and prostrate. Restricting pod development at the plant base, as would be the case for peanut plants with upright lateral branches, would decrease pod yield. On the other hand, GH characterized by spreading lateral branches on the ground would facilitate pod formation on the nodes, thereby increasing yield potential. We describe herein an investigation into the GH traits of 521 peanut recombinant inbred lines grown in three distinct environments. Quantitative trait loci (QTLs) for GH were identified on linkage group (LG) 15 between 203.1 and 204.2 cM and on LG 16 from 139.1 to 139.3 cM. Analysis of resequencing data in the identified QTL regions revealed that single nucleotide polymorphism (SNP) or insertion and/or deletion (INDEL) at Arahy15.156854742, Arahy15.156931574, Arahy15.156976352 and Arahy06.111973258 may affect the functions of their respective candidate genes, Arahy.QV02Z8, Arahy.509QUQ, Arahy.ATH5WE and Arahy.SC7TJM. These SNPs and INDELs in relation to peanut GH were further developed for KASP genotyping and tested on a panel of 77 peanut accessions with distinct GH features. This study validates four diagnostic markers that may be used to distinguish erect/bunch peanuts from spreading/prostrate peanuts, thereby facilitating marker-assisted selection for GH traits in peanut breeding.

AhNPR3 regulates the expression of WRKY and PR genes, and mediates the immune response of the peanut (Arachis hypogaea L.).

Systemic acquired resistance is an essential immune response that triggers a broad-spectrum disease resistance throughout the plant. In the present study, we identified a peanut lesion mimic mutant m14 derived from an ethyl methane sulfonate-mutagenized mutant pool of peanut cultivar "Yuanza9102." Brown lesions were observed in the leaves of an m14 mutant from seedling stage to maturity. Using MutMap together with bulked segregation RNA analysis approaches, a G-to-A point mutation was identified in the exon region of candidate gene Arahy.R60CUW, which is the homolog of AtNPR3 (Nonexpresser of PR genes) in Arabidopsis. This point mutation caused a transition from Gly to Arg within the C-terminal transactivation domain of AhNPR3A. The mutation of AhNPR3A showed no effect in the induction of PR genes when treated with salicylic acid. Instead, the mutation resulted in upregulation of WRKY genes and several PR genes, including pathogenesis-related thaumatin- and chitinase-encoding genes, which is consistent with the resistant phenotype of m14 to leaf spot disease. Further study on the AhNPR3A gene will provide valuable insights into understanding the molecular mechanism of systemic acquired resistance in peanut. Moreover, our results indicated that a combination of MutMap and bulked segregation RNA analysis is an effective method for identifying genes from peanut mutants.

Impact of multiple selective breeding programs on genetic diversity in soybean germplasm.

KEY MESSAGE: Independent soybean breeding programs shape genetic diversity from unimproved germplasm to modern cultivars in similar ways, but distinct breeding populations retain unique genetic variation, preserving additional diversity. From the domestication of wild soybean (Glycine soja Sieb. & Zucc.), over 3,000 years ago, to the modern soybean (Glycine max L. Merr) cultivars that provide much of the world's oil and protein, soybean populations have undergone fundamental changes. We evaluated the molecular impact of breeding and selection using 391 soybean accessions including US cultivars and their progenitors from the USDA Soybean Germplasm Collection (CGP), plus two new populations specifically developed to increase genetic diversity and high yield in two alternative gene pools: one derived from exotic G. max germplasm (AGP) and one derived from G. soja (SGP). Reduction in nucleotide genetic diversity (π) was observed with selection within gene pools, but artificial selection in the AGP maintained more diversity than in the CGP. The highest FST levels were seen between ancestral and elite lines in all gene pools, but specific nucleotide-level patterns varied between gene pools. Population structure analyses support that independent selection resulted in high-yielding elite lines with similar allelic compositions in the AGP and CGP. SGP, however, produced elite progeny that were well differentiated from, but lower yielding than, CGP elites. Both the AGP and SGP retained a significant number of private alleles that are absent in CGP. We conclude that the genomic diversity shaped by multiple selective breeding programs can result in gene pools of highly productive elite lines with similar allelic compositions in a genome-wide perspective. Breeding programs with different ancestral lines, however, can retain private alleles representing unique genetic diversity.

Marker-assisted backcrossing to improve seed oleic acid content in four elite and popular peanut (Arachis hypogaea L.) cultivars with high oil content.

High oleic acid composition is an important determinant of seed quality in peanut (Arachis hypogaea) in regard to its nutritional benefits for human health and prolonged shelf-life for peanut products. To improve the oleic acid content of popular peanut cultivars in China, four peanut cultivars of different market types were hybridized with high-oleic-acid donors and backcrossed for four generations as recurrent parents using fad2 marker-assisted backcross selection. Seed quality traits in advanced generations derived by selfing were assessed using near-infrared reflectance spectroscopy for detection of oleic acid and Kompetitive allele-specific PCR (KASP) screening of fad2 mutant markers. Twenty-four high-oleic-acid lines of BC4F4 and BC4F5 populations, with morphological features and agronomic traits similar to those of the recurrent parents, were obtained within 5 years. The genetic backgrounds of BC4F5 lines were estimated using the KASP assay, which revealed the genetic background recovery rate was 79.49%-92.31%. The superior lines raised are undergoing a multi-location test for cultivar registration and release. To our knowledge, this is the first application of single nucleotide polymorphism markers based on the high-throughput and cost-effective KASP assay for detection of fad2 mutations and genetic background evaluation in a peanut breeding program.

Genetic Diversity, Population Structure, and Botanical Variety of 320 Global Peanut Accessions Revealed Through Tunable Genotyping-by-Sequencing.

Cultivated peanut (Arachis hypogaea L.) were classified into six botanical varieties according to the morphological characteristics. However, their genetic evolutionary relationships at the genome-wide level were still unclear. A total of 320 peanut accessions, including four of the six botanical varieties, and 37,128 high-quality single nucleotide polymorphisms (SNPs) detected by tunable genotyping-by-sequencing (tGBS) were used to reveal the evolutionary relationships among different botanical varieties and verify the phenotypic classification. A phylogenetic tree indicated that the tested accessions were grouped into three clusters. Almost all of the peanut accessions in cluster C1 belong to var. fastigiata, and clusters C2 and C3 mainly consisted of accessions from var. vulgaris and subsp. hypogaea, respectively. The results of a principal component analysis were consistent with relationships revealed in the phylogenetic tree. Population structure analysis showed that var. fastigiata and var. vulgaris were not separated when K = 2 (subgroup number), whereas they were clearly divided when K = 3. However, var. hypogaea and var. hirsuta could not be distinguished from each other all the way. The nucleotide diversity (π) value implied that var. vulgaris exhibited the highest genetic diversity (0.048), followed by var. fastigiata (0.035) and subsp. hypogaea (0.012), which is consistent with the result of phylogenetic tree. Moreover, the fixation index (FST) value confirmed that var. fastigiata and var. vulgaris were closely related to each other (FST = 0.284), while both of them were clearly distinct from var. hypogaea (FST > 0.4). The present study confirmed the traditional botanical classifications of cultivated peanut at the genome-wide level. Furthermore, the reliable SNPs identified in this study may be a valuable resource for peanut breeders.