Genome‐wide identification of quantitative trait nucleotides for plant architecture‐related traits in peanut

Ethiopia is the center of origin and has a varied genetic foundation for Arabica coffee, but there is still a lack of yield-competitive enhanced varieties, which is why the average productivity in the country is significantly lower than the global average. The average national productivity is quite low as a result. To find high-yielding coffee for commercial usage, it may be helpful to further assess the performance of the top-performing selections for growth and yield characteristics at the full bearing stage. Therefore, it is crucial to create pure line coffee types that are stable, disease resistant, and high yielding in order to close this gap and increase coffee productivity. Thus, the purpose of this study was to assess the genotypes of pure lines coffee for yield and yield components. In order to illustrate the growth and yield characteristics of eleven Arabica pure line coffee genotypes and three standard checks, the experiment was carried out at Awada, Leku, and Wonago. A randomized complete block design (RCBD) with three replications was used to carry out the experiment between 2015 and 2020. Data were gathered on plant height, number of primary branches, number of secondary branches, length of the longest primary branch, number of main stem nodes, stem girth, internode length on the main stem, canopy diameter, number of nodes on longest primary, and yield per hectare. The findings showed that there were differences between the growth features. Total plant height (1.88–3.34 m), stem diameter (2.93–4.42 cm), canopy diameter (153.58–195.17 cm), number of main stem nodes (30.47–42.00), primary branch number (59.93–82.93), secondary branch number (12.97–37.80), average length of primary branches (92.50–116.10 cm), and number of nodes on longest primary (18.43–29.07) at Awada. Stem diameter (2.78–4.20 cm), canopy diameter (171.19–216.33 cm), number of main stem nodes (29.27–34.93), inter node length on the main stem (4.99–6.77 cm), number of primary branches (55.67–119.67), number of secondary branches (37.80–76.53), average length of primary branches (90.73–125.07 cm), and number of nodes on longest primary (22.27–61.67) of the plant are all measured at Leku. The number of main stem nodes (27.02-31.13), inter node length on the main stem (6.82 - 14.83 cm), number of primary branches (54.00 - 60.93), number of secondary branches (10.73 - 23.73), average length of primary branches (90.20 - 102.40 cm), stem diameter (2.97 - 3.64 cm), canopy diameter (149.67 - 202.17 cm), and number of nodes on longest primary (20.00 - 25.40) are all measured at Wonago. According to the study's findings, pure line selection 9634 (1684 kg/ha) had the highest overall yield per hectare, followed by 9615 (1671 kg/ha) and 85298 (902 kg/ha), which had the lowest. Awada, Leku and Wonago, there will be a better probability of getting improved pure line Arabica coffee varieties inside south Ethiopian producing climate. To suggest an appropriate and stable pure line variety for coffee growers in the South, the experiment should be conducted again at a different representative trial site.

Ameliorative roles of biochar-based fertilizer on morpho-physiological traits, nutrient uptake and yield in peanut (Arachis hypogaea L.) under water stress

The concentration degree of floret flowering date (CDFFD) is an important selection index for earliness breeding of peanut, while the genetic basis of CDFFD-related traits in peanut is poorly understood. The aim of this study was to develop a high-density genetic linkage map of cultivated peanut by combining SLAF-seq (specific locus amplified fragment sequencing) with SSR (simple sequence repeat) techniques and to identify QTL (quantitative trait loci) controlling CDFFD. A RIL population derived from a cross between Silihong and Jinonghei 3 was sequenced by the HiSeq 2500 platform. A total of 1262.02 M high-quality paired-end reads were obtained, and 1,328,966 SNPs (single nucleotide polymorphisms) were developed from parents and progenies, of which 754,499 SNPs were successfully encoded and genotyping. The resulting map consisted of 3326 genetic markers (2996 SNPs and 330 SSRs) distributed in 20 linkage groups (LGs), spanning 1822.83 cM with an average distance of 0.55 cM between adjacent markers. These assigned SNP markers had average sequencing depths of 16.94-, 15.66-, and 20.94-fold in the male parent, female parent, and their progenies, respectively. Based on phenotyping in 4 environments, 15 significant QTL for the CDFFD were identified. And one major QTL for days to accumulation of 25 flowers (DA25F), qDA25F6.2, located on chromosome 6 was obtained, with a phenotypic variance explanation (PVE) of 12.49%. This map exhibited higher resolution and accuracy, which will facilitate more QTL discovery for interested agronomic and quality traits in cultivated peanut.

Genetic mutations such as single nucleotide polymorphisms (SNP) are known as one of the most common forms which related to various genetic disorders and cancers. Among of the methods developed for efficient detection of such SNP, polymerase chain reaction (PCR) methods are widely used worldwide for its cost and viable advantages. However, the technique to discriminate small amounts of SNP mixed in abundant normal DNA is incomplete due to intrinsic technical problems of PCR such as amplification occurring even in 3’mismatched cases because of high enzyme activity of DNA polymerases. To overcome the issue, specifically designed PCR platform, STexS (SNP typing with excellent specificity) using double stranded oligonucleotides was implemented as a means to emphasize the amplification of SNP templates by decreasing unwanted amplification of 3’mismatched DNA copies. In this study, the results indicate several EGFR mutations were easily detected specifically utilizing the STexS platform. Further trials show the novel method works effectively to discriminate mutations in not only general allele specific (AS)-PCRs, but also amplification refractory mutation system (ARMS)-PCR. The STexS platform will give aid in PCRs targeting potential SNPs or genetically mutated biomarkers in human clinical samples.

Objective To compare the advantages and disadvantages of the amplification refractory mutation system -polymerase chain reaction (ARMS-PCR) with restriction fragment length polymorphism polymerase chain reaction(RFLP-PCR) technology, then explore its application in detection and analysis of single nucleotide polymorphism (SNP). Methods By using ARMS-PCR and RFLP-PCR the polymorphisms of the MMP-2 in cervical cancer and the normal-control group were analyzed. The research sites were-1306 C/T.The final result was verified by DNA sequencing. Results The results of amplified used ARMS-PCR showed that the genotypes of the SNP site in MMP-2 gene promoter were CC, CT, TT, and the DNA fragments were corresponding with the results of DNA sequencing. Conclusion ARMS-PCR technology has the advantages of lower cost time-saving simple operation and reliable results, while has a high value for research of SNP. Key words: Uterine cervical neoplasms; Polymerase chain reaction; Polymorphism; singlenucleotide; MMP-2

The genetic architecture determinants of yield traits in peanut (Arachis hypogaea L.) are poorly understood. In the present study, an effort was made to map quantitative trait loci (QTLs) for yield traits using recombinant inbred lines (RIL). A genetic linkage map was constructed containing 609 loci, covering a total of 1557.48 cM with an average distance of 2.56 cM between adjacent markers. The present map exhibited good collinearity with the physical map of diploid species of Arachis. Ninety-two repeatable QTLs were identified for 11 traits including height of main stem, total branching number, and nine pod- and seed-related traits. Of the 92 QTLs, 15 QTLs were expressed across three environments and 65 QTLs were newly identified. Twelve QTLs for the height of main stem and the pod- and seed-related traits explaining more than 10 % of phenotypic variation showed a great potential for marker-assisted selection in improving these traits. The trait-by-trait meta-analysis revealed 33 consensus QTLs. The consensus QTLs and other QTLs were further integrated into 29 pleiotropic unique QTLs with the confidence interval of 1.86 cM on average. The significant co-localization of QTLs was consistent with the significant phenotypic correlations among these traits. The complexity of the genetic architecture of yield traits was demonstrated. The present QTLs for pod- and seed-related traits could be the most fundamental genetic factors contributing to the yield traits in peanut. The results provide a good foundation for fine mapping, cloning and designing molecular breeding of favorable genes in peanut.

Peanut (Arachis hypogaea L.) is one of the most important oil crops worldwide, and its yet increasing market demand may be met by genetic improvement of yield related traits, which may be facilitated by a good understanding of the underlying genetic base of these traits. Here, we have carried out a genome-wide association study (GWAS) with the aim to identify genomic regions and the candidate genes within these regions that may be involved in determining the phenotypic variation at seven yield-related traits in peanut. For the GWAS analyses, 195 peanut accessions were phenotyped and/or genotyped; the latter was done using a genotyping-by-sequencing approach, which produced a total of 13,435 high-quality single nucleotide polymorphisms (SNPs). Analyses of these SNPs show that the analyzed peanut accessions can be approximately grouped into two big groups that, to some extent, agree with the botanical classification of peanut at the subspecies level. By taking this genetic structure as well as the relationships between the analyzed accessions into consideration, our GWAS analyses have identified 93 non-overlapping peak SNPs that are significantly associated with four of the studied traits. Gene annotation of the genome regions surrounding these peak SNPs have found a total of 311 unique candidate genes. Among the 93 yield-related-trait-associated SNP peaks, 12 are found to be co-localized with the quantitative trait loci (QTLs) that were identified by earlier related QTL mapping studies, and these 12 SNP peaks are only related to three traits and are almost all located on chromosomes Arahy.05 and Arahy.16. Gene annotation of these 12 co-localized SNP peaks have found 36 candidates genes, and a close examination of these candidate genes found one very interesting gene (arahy.RI9HIF), the rice homolog of which produces a protein that has been shown to improve rice yield when over-expressed. Further tests of the arahy.RI9HIF gene, as well as other candidate genes especially those within the more confident co-localized genomic regions, may hold the potential for significantly improving peanut yield.

Agronomic and physiological traits in peanut (Arachis hypogaea) are important to breeders for selecting high‐yielding and resilient genotypes. However, direct measurement of these traits is labor‐intensive and time‐consuming. This study assessed the feasibility of using unmanned aerial vehicles (UAV)‐based hyperspectral imaging and machine learning (ML) techniques to predict three agronomic traits (biomass, pod count, and yield) and two physiological traits (photosynthesis and stomatal conductance) in peanut under drought stress. Two different approaches were evaluated. The first approach employed eighty narrowband vegetation indices as input features for an ensemble model that included K‐nearest neighbors, support vector regression, random forest, and multi‐layer perceptron (MLP). The second approach utilized mean and standard deviation of canopy spectral reflectance per band. The resultant 400 features were used to train a deep learning (DL) model consisting of one‐dimensional convolutional layers followed by an MLP regressor. Predictions of the agronomic traits obtained using feature learning and DL (R2 = 0.45–0.73; symmetric mean absolute percentage error [sMAPE] = 24%–51%) outperformed those obtained using feature engineering and conventional ML models (R2 = 0.44–0.61, sMAPE = 27%–59%). In contrast, the ensemble model had a slightly better performance in predicting physiological traits (R2 = 0.35–0.57; sMAPE = 37%–70%) compared to the results obtained from the DL model (R2 = 0.36–0.52; sMAPE = 47%–64%). The results showed that the combination of UAV‐based hyperspectral imaging and ML techniques have the potential to assist breeders in rapid screening of genotypes for improved yield and drought tolerance in peanut.

Endometriosis is a benign gynecologic disorder that has malignant-like characteristics such as invasion, and it can cause malignancy and cancer in advanced stages. Genetic, epigenetic and environmental factors play significant roles in the establishment and maintenance of endometriosis. Studies have demonstrated that proto-oncogenes may be an important regulator of cell proliferation in endometriotic tissue. B-Raf is a proto-oncogene, which participates in the MAP kinase/ERK signaling pathway and plays an important role in different types of cancers and disorders. The aim of the present study was to evaluate the association of BRAF gene single nucleotide polymorphism (SNP), rs113488022, with the risk of endometriosis in Iranian women, in order to identify a potential biomarker for the early non-invasive detection of endometriosis and endometriosis-related cancers. In a population based case-control study conducted in Tehran, in-person interviews were completed for 65 women aged 15– 49 years and an equal number of controls frequency-matched to cases by age. All cases were newly diagnosed with endometriosis and all controls were with no laparoscopic evidence of disease. The genomic DNA was extracted from peripheral blood leucocytes and subsequently the rs113488022 SNP of the BRAF gene was genotyped using amplification refractory mutation system- polymerase chain reaction (ARMS-PCR). Statistical analysis was performed using SPSS. In comparison of case and control groups, no significant differences were found in genotype and allele frequencies. Moreover, the results showed that there was no significant association between the presence of risk allele (A) and increased risk of endometriosis (Odds ratio=0.781, 95% CI: 0.480 -1.272, P=0.321). In conclusion, there was no evidence of an association between the rs113488022 SNP of the BRAF gene, and overall risk of endometriosis in Iranian population. Larger studies with different ethnics are needed to validate our findings.

BackgroundThis 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).ResultsThe 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.ConclusionsOverall, 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.

BackgroundPeanut (Arachis hypogaea L.) is a grain legume crop that originated from South America and is now grown around the world. Peanut growth habit affects the variety’s adaptability, planting patterns, mechanized harvesting, disease resistance, and yield. The objective of this study was to map the quantitative trait locus (QTL) associated with peanut growth habit-related traits by combining the genome-wide association analysis (GWAS) and bulked segregant analysis sequencing (BSA-seq) methods.ResultsGWAS was performed with 17,223 single nucleotide polymorphisms (SNPs) in 103 accessions of the U.S. mini core collection genotyped using an Affymetrix version 2.0 SNP array. With a total of 12,342 high-quality polymorphic SNPs, the 90 suggestive and significant SNPs associated with lateral branch angle (LBA), main stem height (MSH), lateral branch height (LBL), extent radius (ER), and the index of plant type (IOPT) were identified. These SNPs were distributed among 15 chromosomes. A total of 597 associated candidate genes may have important roles in biological processes, hormone signaling, growth, and development. BSA-seq coupled with specific length amplified fragment sequencing (SLAF-seq) method was used to find the association with LBA, an important trait of the peanut growth habit. A 4.08 Mb genomic region on B05 was associated with LBA. Based on the linkage disequilibrium (LD) decay distance, we narrowed down and confirmed the region within the 160 kb region (144,193,467–144,513,467) on B05. Four candidate genes in this region were involved in plant growth. The expression levels of Araip.E64SW detected by qRT-PCR showed significant difference between ‘Jihua 5’ and ‘M130’.ConclusionsIn this study, the SNP (AX-147,251,085 and AX-144,353,467) associated with LBA by GWAS was overlapped with the results in BSA-seq through combined analysis of GWAS and BSA-seq. Based on LD decay distance, the genome range related to LBA on B05 was shortened to 144,193,467–144,513,467. Three candidate genes related to F-box family proteins (Araip.E64SW, Araip.YG1LK, and Araip.JJ6RA) and one candidate gene related to PPP family proteins (Araip.YU281) may be involved in plant growth and development in this genome region. The expression analysis revealed that Araip.E64SW was involved in peanut growth habits. These candidate genes will provide molecular targets in marker-assisted selection for peanut growth habits.

Ethiopia is the center of origin and has a varied genetic foundation for Arabica coffee, but there is still a lack of yield-competitive enhanced varieties, which is why the average productivity in the country is significantly lower than the global average. The average national productivity is quite low as a result. To find high-yielding coffee for commercial usage, it may be helpful to further assess the performance of the top-performing selections for growth and yield characteristics at the full bearing stage. Therefore, it is crucial to create pure line coffee types that are stable, disease resistant, and high yielding in order to close this gap and increase coffee productivity. Thus, the purpose of this study was to assess the genotypes of pure lines coffee for yield and yield components. In order to illustrate the growth and yield characteristics of eleven Arabica pure line coffee genotypes and three standard checks, the experiment was carried out at Awada, Leku, and Wonago. A randomized complete block design (RCBD) with three replications was used to carry out the experiment between 2015 and 2020. Data were gathered on plant height, number of primary branches, number of secondary branches, length of the longest primary branch, number of main stem nodes, stem girth, internode length on the main stem, canopy diameter, number of nodes on longest primary, and yield per hectare. The findings showed that there were differences between the growth features. Total plant height (1.88–3.34 m), stem diameter (2.93–4.42 cm), canopy diameter (153.58–195.17 cm), number of main stem nodes (30.47–42.00), primary branch number (59.93–82.93), secondary branch number (12.97–37.80), average length of primary branches (92.50–116.10 cm), and number of nodes on longest primary (18.43–29.07) at Awada. Stem diameter (2.78–4.20 cm), canopy diameter (171.19–216.33 cm), number of main stem nodes (29.27–34.93), inter node length on the main stem (4.99–6.77 cm), number of primary ...

Background: Rheumatoid arthritis (RA) is autoimmune illness due to different reasons, one of these reasons is high level or Single Nucleotide Polymorphism (SNP) in cytokines as Macrophage Migration Inhibitory Factor (MIF). There is no study detect or relate MIF level and/or MIF-173 G/C SNP with RA in Iraq population. Aim: This study aimed to investigate the present and/or the contribution of MIF level and/or MIF-173 G/C SNP to RA development in Iraq population. Methods: This study covered 44 cases of rheumatoid arthritis patients and 44 apparently healthy persons as a control. Blood was collected and transferred into 2 tubes: gel tube for obtain serum to detect Rheumatoid Factor (RF) and C-Reactive Protein (CRP) seropositivity by agglutination and measure MIF level by ELISA; and EDTA tube used for extraction and amplification of DNA for study of MIF -173 G/C SNP by Amplification Refractory Mutation System- Polymerase Chain Reaction (ARMS-PCR) and Sanger DNA sequencing method. Both samples were stored in -20°C for later use.Results: Significantly (P=0.000) both RF and CRP were more positive (47.73% and 40.9%) in RA than healthy control (4.55% and 6.82 %), respectively. The serum MIF (ng/ml) level in RA (3.699±0.602), was significantly (P=0.040) higher than in controls (2.311±0.269). ARMS-PCR, showed that MIF has insignificant (P=0.456) difference in the distribution of three genotypes at -173 locus of promoter with frequencies in RA: GG (34.09%) and GC (65.91%); in controls: GG (40.91%), GC (56.82%) and CC (2.27%). Moreover, MIF-173 GC genotype in RA was associated significantly (P=0.040, P=0.044, P=0.042) with high MIF level, positive RF and positive CRP (4.488±0.824 ng/ml, 58.62% and 51.72%) than in control (2.534±0.398 ng/ml, 26.7% and 20%), respectively. Conclusion: RA specially with MIF-173 GC genotype associated significantly with elevated MIF level, positive RF and positive CRP.

Genotyping of single-nucleotide polymorphisms (SNPs) has been applied in various genetic contexts. Tetra-primer amplification refractory mutation system (ARMS) polymerase chain reaction (PCR) is reported as a prominent assay for SNP genotyping. However, there were published data that may question the reliability of this method on some occasions, in addition to a laborious and time-consuming procedure of the optimization step. In the current study, a new SNP genotyping method named modified tetra-primer ARMS (MTPA) PCR was developed based on tetra-primer ARMS PCR. The modified method has two improvements in its instruction, including equalization of outer primer and inner primer strength by additional mismatch in outer primers, and consideration of equal annealing temperature of specific fragments more than melting temperature of primers. Advantageously, a new computer software was provided for designing primers based on novel concepts. The usual tetra-primer ARMS PCR has a laborious process for optimization. In nonoptimal PCR programs, identification of the accurate genotype was found to be very difficult. However, in MTPA PCR, equalization of the amplicons and primer strength leads to increasing specificity and convenience of genotyping, which was validated by sequencing. In the MTPA PCR technique, a new mismatch at -2 positions of outer primers and equal annealing temperature improve the genotyping procedure. Together, the introduced method could be suggested as a powerful tool for genotyping single-nucleotide mutations and polymorphisms.

Key messageQTLs 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.