Analysis Of Phenotypic Data Research Articles

Association analysis between agronomic traits and AFLP markers in a wide germplasm of proso millet (Panicum miliaceum L.) under normal and salinity stress conditions

BackgroundProso millet is a highly nutritious cereal considered an essential component of processed foods. It is also recognized with high water-use efficiency as well as short growing seasons. This research was primarily aimed at investigating the genetic diversity among genotypes based on evaluating those important traits proposed in previous researches under both normal and salinity- stress conditions. Use of Amplified fragment length polymorphism (AFLP) molecular markers as well as evaluating the association between markers and the investigated traits under both conditions was also another purpose of this research.ResultsAccording to the phenotypic correlation coefficients, the seed yield had the highest correlation with the forage and biological yields under both conditions. By disintegrating those traits investigated under normal and salinity-stress conditions into principal component analysis, it was found that the first four principal components justified more than 59.94 and 62.48% of the whole variance, respectively. The dendrogram obtained by cluster analysis displayed three groups of genotypes under both normal and salinity- stress conditions. Then, association analyses were conducted on 143 proso millet genotypes and 15 agronomic traits as well as 514 polymorphic AFLP markers (out of 866 created bands) generated by 11 primer combinations (out of the initial 20 primer combinations) EcoRI/MseI. The results obtained by mixed linear model (MLM) indicated that under normal conditions, the M14/E10–45 and M14/E10–60 markers had strong associations with seed yield. A similar trend was also observed for M14/E10–45 and M14/E11–44 markers in relation to forage yield. On the other hand, M14/E10–14, M14/E10–64 markers (for seed yield) and M14/E10–64 marker (for forage yield), had significant and stable association in all environments under salinity-stress conditions. Moreover, a number of markers showed considerable associations and stability under both normal and salinity stress conditions.ConclusionsAccording to the analysis of phenotypic data, the wide germplasm of Iranian proso millet has significant variation in terms of measured traits. It can be concluded that markers showing strong associations with traits under salinity-stress conditions are suitable candidates to be used in future marker-assisted selection (MAS) studies to improve salinity-resistance genotypes of Panicum miliaceum in arid and semiarid areas.

Greenhouse Spatial Effects Detected in the Barley (Hordeum vulgare L.) Epigenome Underlie Stochasticity of DNA Methylation.

Environmental cues are known to alter the methylation profile of genomic DNA, and thereby change the expression of some genes. A proportion of such modifications may become adaptive by adjusting expression of stress response genes but others have been shown to be highly stochastic, even under controlled conditions. The influence of environmental flux on plants adds an additional layer of complexity that has potential to confound attempts to interpret interactions between environment, methylome, and plant form. We therefore adopt a positional and longitudinal approach to study progressive changes to barley DNA methylation patterns in response to salt exposure during development under greenhouse conditions. Methylation-sensitive amplified polymorphism (MSAP) and phenotypic analyses of nine diverse barley varieties were grown in a randomized plot design, under two salt treatments (0 and 75 mM NaCl). Combining environmental, phenotypic and epigenetic data analyses, we show that at least part of the epigenetic variability, previously described as stochastic, is linked to environmental micro-variations during plant growth. Additionally, we show that differences in methylation increase with time of exposure to micro-variations in environment. We propose that subsequent epigenetic studies take into account microclimate-induced epigenetic variability.

Fruit size and firmness QTL alleles of breeding interest identified in a sweet cherry ‘Ambrunés’ × ‘Sweetheart’ population

The Spanish local cultivar ‘Ambrunés’ stands out due to its high organoleptic quality and fruit firmness. These characteristics make it an important parent for breeding cherries with excellent fresh and post-harvest quality. In this work, an F1 sweet cherry population (n = 140) from ‘Ambrunés’ × ‘Sweetheart’ was phenotyped for 2 years for fruit diameter, weight and firmness and genotyped with the RosBREED cherry Illumina Infinium® 6K SNP array v1. These data were used to construct a linkage map and to carry out quantitative trait locus (QTL) mapping of these fruit quality traits. Genotyping of the parental cultivars revealed that ‘Ambrunés’ is highly heterozygous, and its genetic map is the longest reported in the species using the same SNP array. Phenotypic data analyses confirmed a high heritability of fruit size and firmness and a distorted segregation towards softer and smaller fruits. However, individuals with larger and firmer fruits than the parental cultivars were observed, revealing the presence of alleles of breeding interest. In contrast to other genetic backgrounds in which a negative correlation was observed between firmness and size, in this work, no correlation or low positive correlation was detected between both traits. Firmness, diameter and weight QTLs detected validated QTLs previously found for the same traits in the species, and major QTLs for the three traits were located on a narrow region of LG1 of ‘Ambrunés’. Haplotype analyses of these QTLs revealed haplotypes of breeding interest in coupling phase in ‘Ambrunés’, which can be used for the selection of progeny with larger and firmer fruits.

Comparative analysis of SNP data and hybrid taxa information by using a classificatory linear mixed model to study the genetic variation and heritability of initial height growth in selected poplar hybrids

Advances in genomics increase the possibility of using SNP markers in selecting new parental genotypes for poplar breeding or new poplar varieties for commercial deployment. Here, we use a classificatory linear mixed model and a quantitative genetic approach for the combined analysis of SNP and phenotypic data from poplar hybrids at the beginning of their testing program. The main objective was to compare genetic parameters estimated based on two classification criteria: original (hybrid) taxa and SNP clustering. Height growth measurements obtained in three consecutive years from a poplar trial planted in 2002 in the center of Chile were included. In 2016, DNA was extracted from leaves of the same hybrids and genotyped by sequencing. An increasing number of clusters based on the similarity of SNP information was obtained. Broad sense heritability values observed at all levels of genomic clustering were larger than the only estimate obtained by using the original taxa classification. Thus, the method can help to predict a higher genetic gain in the early selection of poplars, based on initial height growth. The method did not affect the accuracy of the heritability estimation. The systematic increment in the intra-clonal covariance with the clustering level also suggests a highly positive genotype-by-time interaction effect at high levels of SNP clustering, which can also be positive for selection purposes. We concluded that the use of SNP clustering allowed the expression of larger genetic differences among hybrids in initial height growth, regardless of the original hybrid taxa.

Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density, and other risk factors as predictors. In model training, the synthetic minority oversampling technique was used to account for low fracture rate, and tenfold cross-validation was employed for hyperparameters optimization. In the testing, the area under curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also significantly better than that of logistic regression. This study suggested that improving fracture prediction in older men can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach. This result should not be extrapolated to women or young individuals.

Opportunities and challenges in the collection and analysis of digital phenotyping data.

The broad adoption and use of smartphones has led to fundamentally new opportunities for capturing social, behavioral, and cognitive phenotypes in free-living settings, outside of research laboratories and clinics. Predicated on the use of existing personal devices rather than the introduction of additional instrumentation, smartphone-based digital phenotyping presents us with several opportunities and challenges in data collection and data analysis. These two aspects are strongly coupled, because decisions about what data to collect and how to collect it constrain what statistical analyses can be carried out, now and years later, and therefore ultimately determine what scientific, clinical, and public health questions may be asked and answered. Digital phenotyping combines the excitement of fast-paced technologies, smartphones, cloud computing and machine learning, with deep mathematical and statistical questions, and it does this in the service of a better understanding our own behavior in ways that are objective, scalable, and reproducible. We will discuss some fundamental aspects of collection and analysis of digital phenotyping data, which takes us on a brief tour of several important scientific and technological concepts, from the open-source paradigm to computational complexity, with some unexpected insights provided by fields as varied as zoology and quantum mechanics.

Novel classification of axial spondyloarthritis to predict radiographic progression using machine learning

Prediction and determination of drug efficacy for radiographic progression is limited by the heterogeneity inherent in axial spondyloarthritis (axSpA). We investigated whether unbiased clustering analysis of phenotypic data can lead to coherent subgroups of axSpA patients with a distinct risk of radiographic progression. A group of 412 patients with axSpA was clustered in an unbiased way using a agglomerative hierarchical clustering method, based on their phenotype mapping. We used a generalised linear model, naïve Bayes, Decision Trees, K-Nearest-Neighbors, and Support Vector Machines to construct a consensus classification method. Radiographic progression over 2 years was assessed using the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS). axSpA patients were classified into three distinct subgroups with distinct clinical characteristics. Sex, smoking, HLA-B27, baseline mSASSS, uveitis, and peripheral arthritis were the key features that were found to stratifying the phenogroups. The three phenogroups showed distinct differences in radiographic progression rate (p<0.05) and the proportion of progressors (p<0.001). Phenogroup 2, consisting of male smokers, had the worst radiographic progression, while phenogroup 3, exclusively suffering from uveitis, showed the least radiographic progression. The axSpA phenogroup classification, including its ability to stratify risk, was successfully replicated in an independent validation group. Phenotype mapping results in a clinically relevant classification of axSpA that is applicable for risk stratification. Novel coupling between phenotypic features and radiographic progression can provide a glimpse into the mechanisms underlying divergent and shared features of axSpA.

The Hessian fly recessive resistance gene h4 mapped to chromosome 1A of the wheat cultivar 'Java' using genotyping-by-sequencing.

The recessive Hessian fly resistance gene h4 and flanking SNP markers were located to a 642kb region in chromosome 1A of the wheat cultivar 'Java.' Hessian fly (HF), Mayetiola destructor, is one of the most destructive insect pests in wheat worldwide. The wheat cultivar 'Java' was reported to carry a recessive gene (h4) for HF resistance; however, its chromosome location has not been determined. To map the HF resistance gene in Java, two populations of recombinant inbred lines (RILs) were developed from 'Bobwhite' × Java and 'Overley' × Java, respectively, and were phenotyped for responses to infestation of HF Great Plains biotype. Analysis of phenotypic data from the F1 and the RIL populations confirmed that one recessive gene conditioned HF resistance in Java. Two linkage maps were constructed using single-nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing (GBS). The h4 gene was mapped to the distal end of the short arm of chromosome 1A, which explained 60.4 to 70.5% of the phenotypic variation for HF resistance in the two populations. The GBS-SNPs in the h4 candidate interval were converted into Kompetitive Allele-Specific Polymerase Chain Reaction (KASP) markers to eliminate the missing data points in GBS-SNPs. Using the revised maps with KASP markers, h4 was further located to a 642kb interval (6,635,984-7,277,935bp). The two flanking KASP markers, KASP3299 and KASP1871, as well as four other closely linked KASP markers, may be useful for pyramiding h4 with other HF resistance genes in breeding.

A taxonomic revision of the Striated Grasswren Amytornis striatus complex (Aves: Maluridae) after analysis of phylogenetic and phenotypic data

ABSTRACT Taxonomy of the Striated Grasswren Amytornis striatus complex has long been contentious and remains incompletely resolved. Its many populations are distributed widely but disjunctly and show only subtle distinction in morphology and plumage. Genetic data are meagre and have not been correlated with phenotype. We conducted a phenotypic analysis across its range and obtained mitochondrial DNA sequences from most populations. We recognise three species: south-eastern Striated Grasswren Amytornis striatus sensu stricto, Opalton Grasswren Amytornis rowleyi in central Queensland and Rufous Grasswren Amytornis whitei in the remainder of the range, with subspecies in the Pilbara and in the central and western deserts. In A. striatus as so circumscribed, we separate allopatric Murray Mallee and central New South Wales populations subspecifically. Isolated populations of Rufous Grasswren, from the North West Cape Peninsula, Western Australia and Eyre Peninsula, South Australia are also distinct. Awareness of such unanticipated diversity within the group has profound implications for conservation.

Large chromosomal variants drive adaptation in sunflowers.

Analysis of genomic and phenotypic data in Helianthus species demonstrates that large chromosomal regions contribute to multiple traits associated with distinct ecotypes.

&lt;p&gt;&lt;strong&gt;Two new foliicolous species of &lt;em&gt;Strigula &lt;/em&gt;(Strigulaceae, Strigulales) in Korea offer insight in phorophyte-dependent variation of thallus morphology&lt;/strong&gt;&lt;/p&gt;

Foliicolous lichens grow on the surface of living leaves in tropical and subtropical forests. Compared to a large number of species of foliicolous lichens reported from other regions in tropical and subtropical Asia, only six species of the otherwise abundant genus Strigula have been registered from South Korea so far. Three of these, morphologically identified as S. concreta, S. macrocarpa, and S. smaragdula, had previously been shown to share near-identical ITS sequences, casting doubt about the usefulness of this marker for species delimitation in the genus Strigula. To shed light on this conundrum, we surveyed the diversity of the genus Strigula in the Gotjawal forest area on Jeju Island south of mainland Korea, where the climate and vegetation are suitable for foliicolous lichens. As the result of a combined analysis of phenotype and molecular data of the ITS fungal barcoding marker, we found that material morphologically similar to known species formed two strongly supported clades, representing two species new to science, S. depressa Woo, Lücking &amp; Hur sp. nov. and S. multiformis Woo, Lücking &amp; Hur sp. nov., which are described herein. Strigula multiformis included the four previously sequenced specimens identified as S. concreta, S. macrocarpa, and S. smaragdula. A detailed analysis of morphological and anatomical characters revealed that all specimens of S. multiformis were anatomically uniform but varied in thallus morphology, mostly resembling S. smaragdula but with some forms similar to S. concreta and S. macrocarpa, explaining the previous misidentifications. This variation was found to be driven by leaf characters of the phorophyte species, as these apparently influence the morphology of the subcuticular thalli.

Abstract 278: Phenomapping of Worsening Renal Function During Hospitalization for Acute Heart Failure

Background: Worsening renal function (WRF) during the hospitalization has been recognized as a predictor for worse outcomes in patients with acute heart failure (AHF). However, in recent years, elevation of serum creatinine during the acute phase of the treatment is accepted as a sign of efficient decongestion. Herein, we aimed to evaluate the phenotypic difference in this heterogeneous phenomenon by using clustering analysis. Methods: A total of 4000 patient data from the West-Tokyo Heart Failure Registry, a multicenter, prospective registry for consecutive AHF hospitalization were analyzed. Within 632 patients identified to have WRF (17%; defined as elevation of eGFR over 20 percent during the hospitalization), we applied two-step clustering analysis of phenotypic data (37 variables) to define and characterize phenotypically distinct population. After identification of phenotypically distinct subgroups, survival analysis with Cox proportional hazard was conducted to elucidate the impact of the classification on composite outcomes of heart failure re-hospitalization and all cause death. Results: The analysis identified four distinct populations (group 1-4) that distinctly differed in terms of clinical characteristics: Group1 composed of patients with reduced ejection fraction (EF), while group 2 to 4 were composed of patients with mid-range and preserved EF. Group 1 patients also had lowest eGFR and blood pressure at the time of admission. Group 1 and 2 patients were younger, but had higher plasma BNP compared to 3 and 4. Among these subgroups, group 1 had the worst, and group 2 had the most favorable prognosis. The difference in prognosis between these two groups was significant after adjustments with known prognostic factors (hazard ratio, 0.58; 95 percent confidence interval, 0.35-0.97). Conclusion: WRF represents heterogeneous condition; our clustering analysis revealed four phenotypically distinct population with significant difference in their prognosis. Further investigation is needed to assess its therapeutic implication.

High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays.

The effects of radiation dosages on plant species are quantitatively presented as the lethal dose or the dose required for growth reduction in mutation breeding. However, lethal dose and growth reduction fail to provide dynamic growth behavior information such as growth rate after irradiation. Irradiated seeds of Arabidopsis were grown in an environmentally controlled high-throughput phenotyping (HTP) platform to capture growth images that were analyzed with machine learning algorithms. Analysis of digital phenotyping data revealed unique growth patterns following treatments below LD50 value at 641 Gy. Plants treated with 100-Gy gamma irradiation showed almost identical growth pattern compared with wild type; the hormesis effect was observed >21 days after sowing. In 200 Gy-treated plants, a uniform growth pattern but smaller rosette areas than the wild type were seen (p < 0.05). The shift between vegetative and reproductive stages was not retarded by irradiation at 200 and 300 Gy although growth inhibition was detected under the same irradiation dose. Results were validated using 200 and 300 Gy doses with HTP in a separate study. To our knowledge, this is the first study to apply a HTP platform to measure and analyze the dosage effect of radiation in plants. The method enabled an in-depth analysis of growth patterns, which could not be detected previously due to a lack of time-series data. This information will improve our knowledge about the effects of radiation in model plant species and crops.

Smoothing and extraction of traits in the growth analysis of noninvasive phenotypic data

BackgroundNon-destructive high-throughput plant phenotyping is becoming increasingly used and various methods for growth analysis have been proposed. Traditional longitudinal or repeated measures analyses that model growth using statistical models are common. However, often the variation in the data is inappropriately modelled, in part because the required models are complicated and difficult to fit. We provide a novel, computationally efficient technique that is based on smoothing and extraction of traits (SET), which we compare with the alternative traditional longitudinal analysis methods.ResultsThe SET-based and longitudinal analyses were applied to a tomato experiment to investigate the effects on plant growth of zinc (Zn) addition and growing plants in soil inoculated with arbuscular mycorrhizal fungi (AMF). Conclusions from the SET-based and longitudinal analyses are similar, although the former analysis results in more significant differences. They showed that added Zn had little effect on plants grown in inoculated soils, but that growth depended on the amount of added Zn for plants grown in uninoculated soils. The longitudinal analysis of the unsmoothed data fitted a mixed model that involved both fixed and random regression modelling with splines, as well as allowing for unequal variances and autocorrelation between time points.ConclusionsA SET-based analysis can be used in any situation in which a traditional longitudinal analysis might be applied, especially when there are many observed time points. Two reasons for deploying the SET-based method are (i) biologically relevant growth parameters are required that parsimoniously describe growth, usually focussing on a small number of intervals, and/or (ii) a computationally efficient method is required for which a valid analysis is easier to achieve, while still capturing the essential features of the exhibited growth dynamics. Also discussed are the statistical models that need to be considered for traditional longitudinal analyses and it is demonstrated that the oft-omitted unequal variances and autocorrelation may be required for a valid longitudinal analysis. With respect to the separate issue of the subjective choice of mathematical growth functions or splines to characterize growth, it is recommended that, for both SET-based and longitudinal analyses, an evidence-based procedure is adopted.

Apolipoprotein E gene polymorphisms: a relationship with the risk of coronary artery disease and the effectiveness of lipid-lowering therapy with rosuvastatin

Aim . To analyze the associations of apolipoprotein E (APOE) gene polymorphisms (rs7412 and rs4420638) with the risk of coronary artery disease (CAD) and the effectiveness of lipid-lowering therapy with rosuvastatin. Material and methods . The study involved the analysis of deoxyribonucleic acid samples and phenotypic data of 1700 unrelated individuals of Slavic origin, natives of Central Russia. A pharmacogenetic testing included 205 patients with CAD. Patients were prescribed rosuvastatin with determination of lipid levels and intima-media thickness (IMT) after 6 and 12 months of follow-up. Results. Carriers of the minor T allele for rs7412 polymorphism of the APOE gene were characterized by a lower CAD risk and a higher baseline level of low-density lipoprotein cholesterol (LDL-C). Associations of this polymorphism depended on body mass index. An association with an increased CAD risk was typical for male carriers of the variant G allele for rs4420638 polymorphism of the APOE gene. When treating CAD patients with rosuvas-tatin, a more pronounced lipid-lowering effect on total cholesterol and LDL-C was characteristic of individuals homozygous for the minor T allele for rs7412 of the APOE gene after 1 month of therapy; decrease of the lipidlowering effect with respect to total cholesterol took place in carriers of the heterozygous A/G genotype of rs4420638 polymorphism of the APOE gene after 12 months of therapy. An association was found between the rs7412 polymorphism of the APOE gene and the changes of maximum IMT in CAD patients treated with rosuvastatin. It consisted of the absence of IMT regression in T allele carriers after 6 months of lipid-lowering therapy. Conclusion. Polymorphisms of the APOE gene are associated with parameters of cholesterol metabolism, the risk of CAD and the effectiveness of lipid-lowering therapy with rosuvastatin.

Seed protein content and its relationships with agronomic traits in pigeonpea is controlled by both main and epistatic effects QTLs

The genetic architecture of seed protein content (SPC) and its relationships to agronomic traits in pigeonpea is poorly understood. Accordingly, five F2 populations segregating for SPC and four agronomic traits (seed weight (SW), seed yield (SY), growth habit (GH) and days to first flowering (DFF)) were phenotyped and genotyped using genotyping-by-sequencing approach. Five high-density population-specific genetic maps were constructed with an average inter-marker distance of 1.6 to 3.5 cM, and subsequently, integrated into a consensus map with average marker spacing of 1.6 cM. Based on analysis of phenotyping data and genotyping data, 192 main effect QTLs (M-QTLs) with phenotypic variation explained (PVE) of 0.7 to 91.3% were detected for the five traits across the five populations. Major effect (PVE ≥ 10%) M-QTLs included 14 M-QTLs for SPC, 16 M-QTLs for SW, 17 M-QTLs for SY, 19 M-QTLs for GH and 24 M-QTLs for DFF. Also, 573 epistatic QTLs (E-QTLs) were detected with PVE ranging from 6.3 to 99.4% across traits and populations. Colocalization of M-QTLs and E-QTLs explained the genetic basis of the significant (P < 0.05) correlations of SPC with SW, SY, DFF and GH. The nature of genetic architecture of SPC and its relationship with agronomic traits suggest that genomics-assisted breeding targeting genome-wide variations would be effective for the simultaneous improvement of SPC and other important traits.

Robust estimation of heritability and predictive accuracy in plant breeding: evaluation using simulation and empirical data

BackgroundGenomic prediction (GP) is used in animal and plant breeding to help identify the best genotypes for selection. One of the most important measures of the effectiveness and reliability of GP in plant breeding is predictive accuracy. An accurate estimate of this measure is thus central to GP. Moreover, regression models are the models of choice for analyzing field trial data in plant breeding. However, models that use the classical likelihood typically perform poorly, often resulting in biased parameter estimates, when their underlying assumptions are violated. This typically happens when data are contaminated with outliers. These biases often translate into inaccurate estimates of heritability and predictive accuracy, compromising the performance of GP. Since phenotypic data are susceptible to contamination, improving the methods for estimating heritability and predictive accuracy can enhance the performance of GP. Robust statistical methods provide an intuitively appealing and a theoretically well justified framework for overcoming some of the drawbacks of classical regression, most notably the departure from the normality assumption. We compare the performance of robust and classical approaches to two recently published methods for estimating heritability and predictive accuracy of GP using simulation of several plausible scenarios of random and block data contamination with outliers and commercial maize and rye breeding datasets.ResultsThe robust approach generally performed as good as or better than the classical approach in phenotypic data analysis and in estimating the predictive accuracy of heritability and genomic prediction under both the random and block contamination scenarios. Notably, it consistently outperformed the classical approach under the random contamination scenario. Analyses of the empirical maize and rye datasets further reinforce the stability and reliability of the robust approach in the presence of outliers or missing data.ConclusionsThe proposed robust approach enhances the predictive accuracy of heritability and genomic prediction by minimizing the deleterious effects of outliers for a broad range of simulation scenarios and empirical breeding datasets. Accordingly, plant breeders should seriously consider regularly using the robust alongside the classical approach and increasing the number of replicates to three or more, to further enhance the accuracy of the robust approach.

Isolation and Characterisation of Biosurfactant-producing Pseudomonas specie from Soil

Bacteria, especially members of the genera Bacillus and pseudomonads express surface-active compounds that are useful in biotechnology. Studies have shown that biosurfactant-expressing strains are rapidly isolated from both soil and water environments that are either contaminated or uncontaminated. The aim of this research is to isolate a large collection of surfactants expressing pseudomonads and to screen and characterised them for biosurfactant production. In this study, bacterial strains were isolated from Dundee Botanic Garden (United Kingdom) soil using pseudomonas selection agar supplemented with centrimide, fusidin and cephaloridine media (PSA+CFC) that select only pseudomonads. The isolates where screened for liquid surface tension reducing ability (LSTRA) using the drop-collapse assay before characterising the key strains using different metabolic and growth-based assays including their antibiogram. At least 30 key strains were identified from a collection of 58 isolated strains and further studied for diversity. A total of 27 assays were conducted to ascertain the phenotype of the 30 keys strains. All the 30 strains (100%) tested positive for catalase and glucose utilisation, while 28 (93%) tested positive for oxidase and KB* broth culture acidity. Also 22 (73%), 26 (87%) and 18 (60%) were found to be positive for swarming, swimming and twitching motilities respectively, while 22 (73%) were positive for lipase, 26 (87%) for protease and 27 (90%) for gelatinase. Furthermore, 12 (40%), 2 (7%), and 9 (30%) were resistance to mercury, kanamycin and to nalidixic acid respectively. Hierarchical cluster analysis of phenotypic characterisation data confirmed that these strains were a diverse group of pseudomonads.

Proof of concept to unmask the breeding value of genetic resources of barley (Hordeum vulgare) with a hybrid strategy

AbstractBroadening the genetic base of elite breeding programmes is crucial for further breeding success. The absence of major adaption genes, however, often masks the grain yield breeding value of genetic resources. We assessed the ability of a hybrid strategy to provide unbiased performance estimates of 21 barley genetic resources. By crossing them to elite tester lines, 25 three‐way hybrids were produced and evaluated together with a part of their parents and eight elite hybrids for important agronomic traits in replicated field trials in four environments. The phenotypic data analyses revealed that the hybrid strategy facilitated to identify promising resources by substantially improving lodging resistance. Combining genotypic data for 5,562 SNPs with the phenotypic data highlighted the potential to boost the diversity of the elite breeding pool via targeted introgression of genetic resources into the male and female heterotic pools. We propose an application of the hybrid strategy for genetic resources of entire genebank collections and to use genome‐wide predictions to support a targeted choice of accessions with high value for barley breeding.

Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.