Analysis Of Phenotypic Data Research Articles

Glycomyces amatae sp. nov., isolated from a yellow-ringed grass moth (Amata sperbius).

A novel mycelium-forming actinomycete strain, designated A-F 0318T, was isolated from a yellow-ringed grass moth (Amata sperbius) collected from Phitsanulok Province, Thailand. Long chains of non-motile cylindrical spores with a smooth surface developed on aerial mycelia. The polyphasic taxonomic study suggested that strain A-F 0318T belonged to the genus Glycomyces. The 16S rRNA gene sequence analysis indicated that strain A-F 0318T was closely related to Glycomyces harbinensis LL-DO5139T with 97.94 % sequence similarity. The average nucleotide identity (ANI) based on blast, ANI based on the MUMmer algorithm and average amino acid identity values of strain A-F 0318T with G. harbinensis LL-DO5139T were 86.9, 89.1 and 84.24 %, respectively. The digital DNA-DNA hybridization value between A-F 0318T and its closest relative, G. harbinensis LL-DO5139T was 33.8 %. The digital G+C content of the genomic DNA was 71.7 mol%. The cell-wall peptidoglycan contained meso-diaminopimelic acid. The whole-cell sugars contained ribose, xylose, glucose and galactose. The predominant menaquinone was MK-10(H4). The predominant fatty acids were iso-C16 : 0, anteiso-C15 : 0, anteiso-C17 : 0 and iso-C16 : 1 G. The polar lipid profile consisted of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, phosphatidylinositol mannoside, two unknown phosphoglycolipids and one unknown phospholipid. Based on comparative analysis of genotypic, phenotypic and chemotaxonomic data, the novel actinomycete strain A-F 0318T (=TBRC 13612T=NBRC 115417T) represents the type strain of a novel species, for which the name Glycomyces amatae sp. nov. is proposed.

A novel locus conferring resistance to Puccinia hordei maps to the genomic region corresponding to Rph14 on barley chromosome 2HS.

Barley leaf rust (BLR), caused by Puccinia hordei, is best controlled through genetic resistance. An efficient resistance breeding program prioritizes the need to identify, characterize, and map new sources of resistance as well as understanding the effectiveness, structure, and function of resistance genes. In this study, three mapping populations were developed by crossing Israelian barley lines “AGG-396,” “AGG-397,” and “AGG-403” (carrying unknown leaf rust resistance) with a susceptible variety “Gus” to characterize and map resistance. Genetic analysis of phenotypic data from rust testing F3s with a P. hordei pathotype 5457 P+ revealed monogenic inheritance in all three populations. Targeted genotyping-by-sequencing of the three populations detected marker trait associations in the same genomic region on the short arm of chromosome 2H between 39 and 57 Mb (AGG-396/Gus), 44 and 64 Mb (AGG-397/Gus), and 31 and 58 Mb (AGG-403/Gus), suggesting that the resistance in all three lines is likely conferred by the same locus (tentatively designated RphAGG396). Two Kompetitive allele-specific PCR (KASP) markers, HvGBSv2-902 and HvGBSv2-932, defined a genetic distance of 3.8 cM proximal and 7.1 cM distal to RphAGG396, respectively. To increase the marker density at the RphAGG396 locus, 75 CAPS markers were designed between two flanking markers. Integration of marker data resulted in the identification of two critical recombinants and mapping RphAGG396 between markers- Mloc-28 (40.75 Mb) and Mloc-41 (41.92 Mb) narrowing the physical window to 1.17 Mb based on the Morex v2.0 reference genome assembly. To enhance map resolution, 600 F2s were genotyped with markers- Mloc-28 and Mloc-41 and nine recombinants were identified, placing the gene at a genetic distance of 0.5 and 0.2 cM between the two markers, respectively. Two annotated NLR (nucleotide-binding domain leucine-rich repeat) genes (r2.2HG0093020 and r2.2HG0093030) were identified as the best candidates for RphAGG396. A closely linked marker was developed for RphAGG396 that can be used for marker-assisted selection.

The field phenotyping platform's next darling: Dicotyledons.

The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.

Fine mapping of candidate quantitative trait loci for plant and ear height in a maize nested-association mapping population.

Plant height (PH) and ear height (EH) are two important traits in maize (Zea mays L.), as they are closely related to lodging resistance and planting density. Our objectives were to (1) investigate single-nucleotide polymorphisms (SNPs) that are associated with PH and EH for detecting quantitative trait loci (QTL) and new gene that determines PH and EH, (2) explore the value of the QTL in maize breeding, and (3) investigate whether the “triangle heterotic group” theory is applicable for lowering PH and EH to increase yield. Seven inbred female parents were crossed with a common founder male parent Ye 107 to create a nested association mapping (NAM) population. The analysis of phenotypic data on PH and EH revealed wide variation among the parents of the NAM population. Genome-wide association study (GWAS) and high-resolution linkage mapping were conducted using the NAM population, which generated 264,694 SNPs by genotyping-by-sequencing. A total of 105 SNPs and 22 QTL were identified by GWAS and found to be significantly associated with PH and EH. A high-confidence QTL for PH, Qtl-chr1-EP, was identified on chromosome 1 via GWAS and confirmed by linkage analysis in two recombinant inbred line (RIL) populations. Results revealed that the SNP variation in the promoter region of the candidate gene Zm00001d031938, located at Qtl-chr1-EP, which encoded UDP-N-acetylglucosamine-peptide N-acetyl-glucosaminyl-transferase, might decrease PH and EH. Furthermore, the triangle heterotic pattern adopted in maize breeding programs by our team is practicable in selecting high-yield crosses based on the low ratio of EH/PH (EP).

A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data.

The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.

Molecular and morphological identification of frog species collected at Rara Lake in Rara National Park, Nepal.

This study seeks to clarify the taxonomic identity of three adult frogs and two tadpoles from Rara Lake situated in Rara National Park, Mugu district, Western Nepal, using both phenotypic and mitochondrial sequence data (16S rRNA gene). Based on the molecular data, we determined our specimens belonged to two species; Paa cf. ercepeae (Dubois, 1974) and Paa rarica (Dubois, Matsui, and Ohler, 2001), and this assignment is also supported by morphology. Principal component analysis of phenotypic data clustered species into large-bodied (SVL > 55 mm) and small-bodied (SVL < 55 mm) groups. The molecular data suggest that Paa rarica belonged to a group that contains several undescribed lineages and that Paa liebigii (Gnther, 1860) hosts extensive cryptic diversity. Our study also documents that Paa species in the Himalayan region are more widely distributed than previously believed.

Genotyping-by-Sequencing Based Investigation of Population Structure and Genome Wide Association Studies for Seven Agronomically Important Traits in a Set of 346 Oryza rufipogon Accessions

Being one of the most important staple dietary constituents globally, genetic enhancement of cultivated rice for yield, agronomically important traits is of substantial importance. Even though the climatic factors and crop management practices impact complex traits like yield immensely, the contribution of variation by underlying genetic factors surpasses them all. Previous studies have highlighted the importance of utilizing exotic germplasm, landraces in enhancing the diversity of gene pool, leading to better selections and thus superior cultivars. Thus, to fully exploit the potential of progenitor of Asian cultivated rice for productivity related traits, genome wide association study (GWAS) for seven agronomically important traits was conducted on a panel of 346 O. rufipogon accessions using a set of 15,083 high-quality single nucleotide polymorphic markers.The phenotypic data analysis indicated large continuous variation for all the traits under study, with a significant negative correlation observed between grain parameters and agronomic parameters like plant height, culm thickness. The presence of 74.28% admixtures in the panel as revealed by investigating population structure indicated the panel to be very poorly genetically differentiated, with rapid LD decay. The genome-wide association analyses revealed a total of 47 strong MTAs with 19 SNPs located in/close to previously reported QTL/genic regions providing a positive analytic proof for our studies. The allelic differences of significant MTAs were found to be statistically significant at 34 genomic regions. A total of 51 O. rufipogon accessions harboured combination of superior alleles and thus serve as potential candidates for accelerating rice breeding programs. The present study identified 27 novel SNPs to be significantly associated with different traits. Allelic differences between cultivated and wild rice at significant MTAs determined superior alleles to be absent at 12 positions implying substantial scope of improvement by their targeted introgression into cultivars. Introgression of novel significant genomic regions into breeder’s pool would broaden the genetic base of cultivated rice, thus making the crop more resilient.

Genome-wide association study and selection for field resistance to cassava root rot disease and productive traits

Cassava root rot disease is caused by a complex of soil-borne pathogens and has high economic impacts because it directly affects the tuberous roots, which are the main commercial product. This study aimed to evaluate cassava genotypes for resistance to root rot disease in a field with a previous history of high disease incidence. It also aimed to identify possible genomic regions associated with field resistance based on genome-wide association studies. A total of 148 genotypes from Embrapa Mandioca and Fruticultura were evaluated over two years, including improved materials and curated germplasms. Analysis of phenotypic data was conducted, as well as a genomic association analysis, based on the general linear model, mixed linear model, and fixed and random model circulating probability unification. The observed high disease index (ω) was directly correlated with genotype survival, affecting plant height, shoot yield, and fresh root yield. The genotypes were grouped into five clusters, which were classified according to level of root rot resistance (i.e., extremely susceptible, susceptible, moderately susceptible, moderately resistant, and resistant). The 10 genotypes with the best performance in the field were selected as potential progenitors for the development of segregating progenies. Estimates of genomic kinship between these genotypes ranged from -0.183 to 0.671. The genotypes BGM-1171 and BGM-1190 showed the lowest degree of kinship with the other selected sources of resistance. The genotypes BGM-0209, BGM-0398, and BGM-0659 showed negative kinship values with most elite varieties, while BGM-0659 presented negative kinship with all landraces. A genome-wide association analysis detected five significant single nucleotide polymorphisms related to defense mechanisms against biotic and abiotic stresses, with putative association with fresh root yield in soil infested with root rot pathogens. These findings can be utilized to develop molecular selection for root rot resistance in cassava.

Fine mapping of QFlw-5B, a major QTL for flag leaf width in common wheat (Triticum aestivum L.).

AmajorstableQTLforflagleafwidthwasnarroweddownto2.5Mbregioncontainingtwopredicatedputativecandidategenes,anditseffectsonyield-relatedtraitswascharacterized. Flag leaf width (FLW) is important to production in wheat. In a previous study, a major quantitative trait locus for FLW (QFlw-5B) was detected on chromosome 5B, within an interval of 6.5cM flanked by the markers of XwPt-9103 and Xbarc142, using a mapping population of recombinant inbred lines derived from a cross between Kenong9204 (KN9204) and Jing411 (J411) (denoted as KJ-RILs). The aim of this study was to fine map QFlw-5B and characterize its genetic effects on yield-related traits. Multiple near-isogenic lines (NILs) were developed using one residual heterozygous line for QFlw-5B. Five recombinants for QFlw-5B were identified, and its location was narrowed to a 2.5Mb region based on combined phenotypic and genotypic data analysis. This region contained 27 predicted genes, two of which were considered as the most likely candidate genes for QFlw-5B. The FLW of NIL-KN9204 was significantly higher than that of NIL-J411 across all the tested environments. Meanwhile, significant increases in plant height, grain width and 1000-grain weight were observed in NIL-KN9204 compared with that in NIL-J411. These results indicate that QFlw-5B has great potential for marker-assisted selection in wheat breeding programs designed to improve both plant architecture and yield. This study also provides a basis for the map-based cloning of QFlw-5B.

WGNAM: whole-genome nested association mapping

Key messageA powerful QTL analysis method for nested association mapping populations is presented. Based on a one-stage multi-locus model, it provides accurate predictions of founder specific QTL effects.Nested association mapping (NAM) populations have been created to enable the identification of quantitative trait loci (QTL) in different genetic backgrounds. A whole-genome nested association mapping (WGNAM) method is presented to perform QTL analysis in NAM populations. The WGNAM method is an adaptation of the multi-parent whole genome average interval mapping approach where the crossing design is incorporated through the probability of inheriting founder alleles for every marker across the genome. Based on a linear mixed model, this method provides a one-stage analysis of raw phenotypic data, molecular markers, and crossing design. It simultaneously scans the whole-genome through an iterative process leading to a model with all the identified QTL while keeping the false positive rate low. The WGNAM approach was assessed through a simulation study, confirming to be a powerful and accurate method for QTL analysis for a NAM population. This novel method can also accommodate a multi-reference NAM (MR-NAM) population where donor parents are crossed with multiple reference parents to increase genetic diversity. Therefore, a demonstration is presented using a MR-NAM population for wheat (Triticum aestivum L.) to perform a QTL analysis for plant height. The strength and size of the putative QTL were summarized enhancing the understanding of the QTL effects depending on the parental origin. Compared to other methods, the proposed methodology based on a one-stage analysis provides greater power to detect QTL and increased accuracy in the estimation of their effects. The WGNAM method establishes the basis for accurate QTL mapping studies for NAM and MR-NAM populations.

Profiling PI3K-AKT-MTOR variants in focal brain malformations reveals new insights for diagnostic care.

Focal malformations of cortical development including focal cortical dysplasia, hemimegalencephaly and megalencephaly, are a spectrum of neurodevelopmental disorders associated with brain overgrowth, cellular and architectural dysplasia, intractable epilepsy, autism and intellectual disability. Importantly, focal cortical dysplasia is the most common cause of focal intractable paediatric epilepsy. Gain and loss of function variants in the PI3K-AKT-MTOR pathway have been identified in this spectrum, with variable levels of mosaicism and tissue distribution. In this study, we performed deep molecular profiling of common PI3K-AKT-MTOR pathway variants in surgically resected tissues using droplet digital polymerase chain reaction (ddPCR), combined with analysis of key phenotype data. A total of 159 samples, including 124 brain tissue samples, were collected from 58 children with focal malformations of cortical development. We designed an ultra-sensitive and highly targeted molecular diagnostic panel using ddPCR for six mutational hotspots in three PI3K-AKT-MTOR pathway genes, namely PIK3CA (p.E542K, p.E545K, p.H1047R), AKT3 (p.E17K) and MTOR (p.S2215F, p.S2215Y). We quantified the level of mosaicism across all samples and correlated genotypes with key clinical, neuroimaging and histopathological data. Pathogenic variants were identified in 17 individuals, with an overall molecular solve rate of 29.31%. Variant allele fractions ranged from 0.14 to 22.67% across all mutation-positive samples. Our data show that pathogenic MTOR variants are mostly associated with focal cortical dysplasia, whereas pathogenic PIK3CA variants are more frequent in hemimegalencephaly. Further, the presence of one of these hotspot mutations correlated with earlier onset of epilepsy. However, levels of mosaicism did not correlate with the severity of the cortical malformation by neuroimaging or histopathology. Importantly, we could not identify these mutational hotspots in other types of surgically resected epileptic lesions (e.g. polymicrogyria or mesial temporal sclerosis) suggesting that PI3K-AKT-MTOR mutations are specifically causal in the focal cortical dysplasia-hemimegalencephaly spectrum. Finally, our data suggest that ultra-sensitive molecular profiling of the most common PI3K-AKT-MTOR mutations by targeted sequencing droplet digital polymerase chain reaction is an effective molecular approach for these disorders with a good diagnostic yield when paired with neuroimaging and histopathology.

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

High throughput phenotyping (HTP) platforms and devices are increasingly used for the characterization of growth and developmental processes for large sets of plant genotypes. Such HTP data require challenging statistical analyses in which longitudinal genetic signals need to be estimated against a background of spatio-temporal noise processes. We propose a two-stage approach for the analysis of such longitudinal HTP data. In a first stage, we correct for design features and spatial trends per time point. In a second stage, we focus on the longitudinal modelling of the spatially corrected data, thereby taking advantage of shared longitudinal features between genotypes and plants within genotypes. We propose a flexible hierarchical three-level P-spline growth curve model, with plants/plots nested in genotypes, and genotypes nested in populations. For selection of genotypes in a plant breeding context, we show how to extract new phenotypes, like growth rates, from the estimated genotypic growth curves and their first-order derivatives. We illustrate our approach on HTP data from the PhenoArch greenhouse platform at INRAE Montpellier and the outdoor Field Phenotyping platform at ETH Zürich.

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = −0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = −0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.

Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding

BackgroundDeveloping a systematic phenotypic data analysis pipeline, creating enhanced visualizations, and interpreting the results is crucial to extract meaningful insights from data in making better breeding decisions. Here, we provide an overview of how the Rainfed Rice Breeding (RRB) program at IRRI has leveraged R computational power with open-source resource tools like R Markdown, plotly, LaTeX, and HTML to develop an open-source and end-to-end data analysis workflow and pipeline, and re-designed it to a reproducible document for better interpretations, visualizations and easy sharing with collaborators.ResultsWe reported the state-of-the-art implementation of the phenotypic data analysis pipeline and workflow embedded into a well-descriptive document. The developed analytical pipeline is open-source, demonstrating how to analyze the phenotypic data in crop breeding programs with step-by-step instructions. The analysis pipeline shows how to pre-process and check the quality of phenotypic data, perform robust data analysis using modern statistical tools and approaches, and convert it into a reproducible document. Explanatory text with R codes, outputs either in text, tables, or graphics, and interpretation of results are integrated into the unified document. The analysis is highly reproducible and can be regenerated at any time. The analytical pipeline source codes and demo data are available at https://github.com/whussain2/Analysis-pipeline.ConclusionThe analysis workflow and document presented are not limited to IRRI’s RRB program but are applicable to any organization or institute with full-fledged breeding programs. We believe this is a great initiative to modernize the data analysis of IRRI’s RRB program. Further, this pipeline can be easily implemented by plant breeders or researchers, helping and guiding them in analyzing the breeding trials data in the best possible way.

Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data.

The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec.2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.

Preparation and Curation of Phenotypic Datasets.

Based on case studies, in this chapter we discuss the extent to which the number and identity of quantitative trait loci (QTL) identified from genome-wide association studies (GWAS) are affected by curation and analysis of phenotypic data. The chapter demonstrates through examples the impact of (1) cleaning of outliers, and of (2) the choice of statistical method for estimating genotypic mean values of phenotypic inputs in GWAS. No cleaning of outliers resulted in the highest number of dubious QTL, especially at loci with highly unbalanced allelic frequencies. A trade-off was identified between the risk of false positives and the risk of missing interesting, yet rare alleles. The choice of the statistical method to estimate genotypic mean values also affected the output of GWAS analysis, with reduced QTL overlap between methods. Using mixed models that capture spatial trends, among other features, increased the narrow-sense heritability of traits, the number of identified QTL and the overall power of GWAS analysis. Cleaning and choosing robust statistical models for estimating genotypic mean values should be included in GWAS pipelines to decrease both false positive and false negative rates of QTL detection.

Spikelet abortion in six-rowed barley is mainly influenced by final spikelet number, with potential spikelet number acting as a suppressor trait.

The potential to increase barley grain yield lies in the indeterminate nature of its inflorescence meristem, which produces spikelets, the basic reproductive unit in grasses that are linked to reproductive success. During early reproductive growth, barley spikes pass through the maximum yield potential-a stage after which no new spikelet ridges are produced. Subsequently, spikelet abortion (SA), a phenomenon in which spikelets abort during spike growth, imposes a bottleneck for increasing the grain yield potential. Here, we studied the potential of main culm spikes by counting potential spikelet number (PSN) and final spikelet number (FSN), and computed the corresponding SA (%) in a panel of 417 six-rowed spring barleys. Our phenotypic data analyses showed a significantly large within- and across-years genotypic variation with high broad-sense heritability estimates for all the investigated traits, including SA. Asian accessions displayed the lowest SA, indicating the presence of favourable alleles that may be exploited in breeding programs. A significantly negative Pearson's product-moment correlation was observed between FSN and SA. Our path analysis revealed that PSN and FSN explain 93% of the observed phenotypic variability for SA, with PSN behaving as a suppressor trait that magnifies the effect of FSN. Based on a large set of diverse barley accessions, our results provide a deeper phenotypic understanding of the quantitative genetic nature of SA, its association with traits of high agronomic importance, and a resource for further genetic analyses.

Prauserella cavernicola sp. nov., isolated from cave rock.

A novel actinomycete, designated strain ASG 168T, was isolated from cave rock collected from Stegodon Sea Cave in Thailand. Long chains of non-motile spores that were oval or spherical in shape with a smooth surface developed on aerial mycelia. Substrate mycelia fragmented into irregular rod-shaped elements. A polyphasic taxonomic study showed that strain ASG 168T had typical characteristics of members of the genus Prauserella. 16S rRNA gene sequence analysis indicated that strain ASG 168T shared 97.5 % similarity with Prauserella marina MS498T and 96.7 % with Prauserella coralliicola SCSIO 11529T. Average nucleotide identity values with P. coralliicola SCSIO 11529T and P. marina MS498T were 82.98 and 76.08 %, respectively. The cell-wall peptidoglycan contained meso-diaminopimelic acid. The whole-cell sugars contained ribose, arabinose and galactose. The predominant menaquinone was MK-9(H4). The predominant fatty acids were iso-C16 : 0, C16 : 0 and summed feature 3 (C16 : 1 ω7c/C16 : 1 ω6c). The phospholipid profile consisted of phosphatidylethanolamine, phosphatidylmethylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, phosphatidylinositol mannosides and two unknown phospholipids. The G+C content of the genomic DNA was 70.6 mol%. Differentiation of strain ASG 168T from closely related species was evident from digital DNA-DNA hybridization values of 29.2 and 21.3 % with P. coralliicola and P. marina, respectively. Based on comparative analysis of phenotypic, chemotaxonomic and genotypic data, the novel actinomycete strain ASG 168T (=TBRC 13679T=NBRC 114887T) is proposed to be the type strain of a novel species, Prauserella cavernicola sp. nov.

Species as a Heuristic: Reconciling Theory and Practice.

Species are crucial to most branches of biological research, yet remain controversial in terms of definition, delimitation, and reality. The difficulty of resolving the “species problem” stems from the tension between their theoretical concept as groups of evolving and highly variable organisms and the practical need for a stable and comparable unit of biology. Here, we suggest that treating species as a heuristic can be consistent with a theoretical definition of what species are and with the practical means by which they are identified and delimited. Specifically, we suggest that theoretically species are heuristic since they comprise clusters of closely related individuals responding in a similar manner to comparable sets of evolutionary and ecological forces, whilst they are practically heuristic because they are identifiable by the congruence of contingent properties indicative of those forces. This reconciliation of the theoretical basis of species with their practical applications in biological research allows for a loose but relatively consistent definition of species based on the strategic analysis and integration of genotypic, phenotypic, and ecotypic data. [Cohesion; heuristic; homeostasis; lineage; species problem.]

Characterization of PmBN418, a wheat powdery mildew resistance gene on the rye 1RS chromosome arm

AbstractPowdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a disease of global importance, and new powdery mildew resistance genes that can be directly used in cultivar development are urgently needed. A recombinant inbred line population from ‘CI 17884’ × ‘Bainong 418’ was genotyped using single nucleotide polymorphism markers generated from genotyping‐by‐sequencing, and evaluated for responses to Great Plains Bgt isolates OKS(14)‐B‐3‐1 and NEI‐1‐3. Genetic mapping identified a powdery mildew resistance gene, designated PmBN418, on a 1BL.1RS translocation segment in Bainong 418. Given that PmBN418 conferred resistance to Bgt isolates virulent to Pm8 that resides on the chromosome arm 1RS translocated from rye (Secale cereale L.) cv. ‘Petkus’, PmBN418 is either a new gene or a new allele of Pm8. Phenotypic and genomic data analyses indicated segregation distortion of the powdery mildew resistance gene due to reduced transmission of the wheat (Triticum aestivum L.)–rye 1BL.1RS translocation segment from Bainong 418, while the wheat–Triticum speltoides translocation segment 7S#1 in CI 17884 was preferentially transmitted. Bainong 418, a high‐yielding cultivar with good resistance to several U.S. Bgt isolates, is a useful resistance source for wheat breeding, and the availability of the Kompetitive Allele Specific PCR (KASP) marker KASP‐1RS‐1 makes marker‐assisted introgression of PmBN418 into locally adapted cultivars feasible.