Marker Datasets Research Articles

18S rDNA sequence-structure phylogeny of the Euglenophyceae (Euglenozoa, Euglenida).

The phylogeny of Euglenophyceae (Euglenozoa, Euglenida) has been discussed for decades with new genera being described in the last few years. In this study, we reconstruct a phylogeny using 18S rDNA sequence and structural data simultaneously. Using homology modeling, individual secondary structures were predicted. Sequence-structure data are encoded and automatically aligned. Here, we present a sequence-structure neighbor-joining tree of more than 300 taxa classified as Euglenophyceae. Profile neighbor-joining was used to resolve the basal branching pattern. Neighbor-joining, maximum parsimony, and maximum likelihood analyses were performed using sequence-structure information for manually chosen subsets. All analyses supported the monophyly of Eutreptiella, Discoplastis, Lepocinclis, Strombomonas, Cryptoglena, Monomorphina, Euglenaria, and Colacium. Well-supported topologies were generally consistent with previous studies using a combined dataset of genetic markers. Our study supports the simultaneous use of sequence and structural data to reconstruct more accurate and robust trees. The average bootstrap value is significantly higher than the average bootstrap value obtained from sequence-only analyses, which is promising for resolving relationships between more closely related taxa.

Genomic prediction of drought tolerance during seedling stage in maize using low-cost molecular markers

Drought tolerance in maize is a complex and polygenic trait, especially in the seedling stage. In plant breeding, complex genetic traits can be improved by genomic selection (GS), which has become a practical and effective breeding tool. In the present study, a natural maize population named Northeast China core population (NCCP) consisting of 379 inbred lines were genotyped with diversity arrays technology (DArT) and genotyping-by-sequencing (GBS) platforms. Target traits of seedling emergence rate (ER), seedling plant height (SPH), and grain yield (GY) were evaluated under two natural drought stress environments in northeast China. Adequate genetic variations were observed for all the target traits, but they were divergent across environments. Similarly, the heritability of the target trait also varied across years and environments, the heritabilities in 2019 (0.88, 0.82, 0.85 for ER, SPH, GY) were higher than those in 2020 (0.65, 0.53, 0.33) and cross-2-years (0.32, 0.26, 0.33). In total, three marker datasets, 11,865 SilicoDArT markers obtained from the DArT-seq platform, 7837 SNPs obtained from the DArT-seq platform, and 91,003 SNPs obtained from the GBS platform, were used for GS analysis after quality control. The results of phylogenetic trees showed that broad genetic diversity existed in the NCCP population. Genomic prediction results showed that the average prediction accuracies estimated using the DArT SNP dataset under the two-fold cross-validation scheme were 0.27, 0.19, and 0.33, for ER, SPH, and GY, respectively. The result of SilicoDArT is close to the SNPs from DArT-seq, those were 0.26, 0.22, and 0.33. For the trait with lower heritability, the prediction accuracy can be improved using the dataset filtered by linkage disequilibrium. For the same trait, the prediction accuracies estimated with two DArT marker datasets were consistently higher than that estimated with the GBS SNP dataset under the same genotyping cost. The prediction accuracy was improved by controlling population structure and marker quality, even though the marker density was reduced. The prediction accuracies were improved by more than 30% using the significant-associated SNPs. Due to the complexity of drought tolerance under the natural stress environments, multiple years of data need to be accumulated to improve prediction accuracy by reducing genotype-by-environment interaction. Modeling genotype-by-environment interaction into genomic prediction needs to be further developed for improving drought tolerance in maize. The results obtained from the present study provides valuable pathway for improving drought tolerance in maize using GS.

Unraveling the hierarchical genetic structure of tea green leafhopper, Matsumurasca onukii, in East Asia based on SSRs and SNPs.

Matsumurasca onukii (Matsuda, R. (1952). Oyo‐Kontyu Tokyo, 8(1): 19–21), one of the dominant pests in major tea production areas in Asia, currently is known to occur in Japan, Vietnam, and China, and severely threatens tea production, quality, and international trade. To elucidate the population genetic structure of this species, 1633 single nucleotide polymorphisms (SNPs) and 18 microsatellite markers (SSRs) were used to genotype samples from 27 sites representing 18 geographical populations distributed throughout the known range of the species in East Asia. Analyses of both SNPs and SSRs showed that M. onukii populations in Yunnan exhibit high‐genetic differentiation and structure compared with the other populations. The Kagoshima (JJ) and Shizuoka (JS) populations from Japan were separated from populations from China by SNPs, but clustered with populations from Jinhua (JH), Yingde (YD), Guilin (GL), Fuzhou (FZ), Hainan (HQ), Leshan (CT), Chongqing (CY), and Zunyi (ZY) tea plantations in China and the Vietnamese Vinh Phuc (VN) population based on the SSR data. In contrast, CT, CY, ZY, and Shaanxi (SX) populations clustered together based on SNPs, but were separated by SSRs. Both marker datasets identified significant geographic differentiation among the 18 populations. Various environmental and anthropogenic factors, including geographical barriers to migration, human transport of hosts (Camellia sinesis [L.] O. Kuntze) and adaptation of M. onukii to various local climatic zones possibly account for the rapid spread of this pest in Asia. The results demonstrate that SNPs from high‐throughput genotyping data can be used to reveal subtle genetic substructure at broad scales in r‐strategist insects.

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection (GS) is a powerful tool for improving genetic gain in maize breeding. However, its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms. Although sequencing-based and array-based genotyping platforms have been used for GS, few studies have compared prediction performance among platforms. In this study, we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing (GBS), a 40K SNP array, and target sequence capture (TSC) using eight GS models. The GBS marker dataset yielded the highest predictabilities for all traits, followed by TSC and SNP array datasets. We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs, and BayesB, GBLUP, and RKHS performed well, while XGBoost performed poorly in most cases. We also selected significant SNP subsets using genome-wide association study (GWAS) analyses in three panels to predict hybrid performance. GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost, but depended heavily on the GWAS panel. We conclude that there is still room for optimization of the existing SNP array, and using genotyping by target sequencing (GBTS) techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Genetic diversity and population structure of Venturia inaequalis isolates in apple orchards from Turkey

Apple scab caused by Venturia inaequalis is one of the major diseases of apple orchards worldwide. Genetic variability in pathogen populations present new strategies for management the disease and breeding studies. In this study aimed to evaluate genetic diversity and population structure of 81 V. inaequalis isolates obtained from six provinces of Turkey. Twenty-six iPBS and 20 ISSR primers were investigated for their ability to produce polymorphic bands among the pathogen isolates. The mean percentage of polymorphic bands per primer was 85.48 and 84.87 for iPBS and ISSR markers, respectively. The dendrogram derived from UPGMA cluster analysis based on the combined dataset of iPBS and ISSR markers classified the isolates into two main groups with a genetic similarity of 69%, which no relationship between distribution and geographical origin of the isolates. The dendrogram of Nei's genetic distances Isparta and Sivas populations clustered separately other populations. STRUCTURE analysis showed four populations (K = 4) with the highest mean value of Ln likelihood (−3067.0). The average genetic differentiation (GST) among all populations was 0.105. The overall gene flow (NM) was high level of 4.260. The analysis of molecular variance (AMOVA) showed that 96% of the variation was between the isolates within populations, compared to only 4% was among populations. iPBS marker based on the LTR retrotransposons has been tried for the first time to evaluate intraspecific variation among the isolates of V. inaequalis. Both iPBS and ISSR markers were useful, informative and reliable tools for investigating the genetic diversity of V. inaequalis. The results obtained from the present study provide detailed information about population genetic structure of V. inaequalis in Turkey. These study also will provide new approaches for plant breeders and disease management strategies.

A multi-omics framework reveals strawberry flavor genes and their regulatory elements.

Flavor is essential to consumer preference of foods and is an increasing focus of plant breeding programs. In fruit crops, identifying genes underlying volatile organic compounds has great promise to accelerate flavor improvement, but polyploidy and heterozygosity in many species have slowed progress. Here we use octoploid cultivated strawberry to demonstrate how genomic heterozygosity, transcriptomic intricacy and fruit metabolomic diversity can be treated as strengths and leveraged to uncover fruit flavor genes and their regulatory elements. Multi-omics datasets were generated including an expression quantitative trait loci map with 196 diverse breeding lines, haplotype-phased genomes of a highly-flavored breeding selection, a genome-wide structural variant map using five haplotypes, and volatile genome-wide association study (GWAS) with > 300 individuals. Overlaying regulatory elements, structural variants and GWAS-linked allele-specific expression of numerous genes to variation in volatile compounds important to flavor. In one example, the functional role of anthranilate synthase alpha subunit 1 in methyl anthranilate biosynthesis was supported via fruit transient gene expression assays. These results demonstrate a framework for flavor gene discovery in fruit crops and a pathway to molecular breeding of cultivars with complex and desirable flavor.

Histone-Net: a multi-paradigm computational framework for histone occupancy and modification prediction

Deep exploration of histone occupancy and covalent post-translational modifications (e.g., acetylation, methylation) is essential to decode gene expression regulation, chromosome packaging, DNA damage, and transcriptional activation. Existing computational approaches are unable to precisely predict histone occupancy and modifications mainly due to the use of sub-optimal statistical representation of histone sequences. For the establishment of an improved histone occupancy and modification landscape for multiple histone markers, the paper in hand presents an end-to-end computational multi-paradigm framework “Histone-Net”. To learn local and global residue context aware sequence representation, Histone-Net generates unsupervised higher order residue embeddings (DNA2Vec) and presents a different application of language modelling, where it encapsulates histone occupancy and modification information while generating higher order residue embeddings (SuperDNA2Vec) in a supervised manner. We perform an intrinsic and extrinsic evaluation of both presented distributed representation learning schemes. A comprehensive empirical evaluation of Histone-Net over ten benchmark histone markers data sets for three different histone sequence analysis tasks indicates that SuperDNA2Vec sequence representation and softmax classifier-based approach outperforms state-of-the-art approach by an average accuracy of 7%. To eliminate the overhead of training separate binary classifiers for all ten histone markers, Histone-Net is evaluated in multi-label classification paradigm, where it produces decent performance for simultaneous prediction of histone occupancy, acetylation, and methylation.

Optimal estimation of complex aerial movements using dynamic optimisation

ABSTRACT When estimating full-body motion from experimental data, inverse kinematics followed by inverse dynamics does not guarantee dynamical consistency of the resulting motion, especially in movements where the trajectory depends heavily on the initial state, such as in free-fall. Our objective was to estimate dynamically consistent joint kinematics and kinetics of complex aerial movements. A 42-degrees-of-freedom model with 95 markers was personalised for five elite trampoline athletes performing various backward and forward twisting somersaults. Using dynamic optimisation, our algorithm estimated joint angles, velocities and torques by tracking the recorded marker positions. Kinematics, kinetics, angular and linear momenta, and marker tracking difference were compared to results of an Extended Kalman Filter (EKF) followed by inverse dynamics. Angular momentum and horizontal linear momentum were conserved throughout the estimated motion, as per free-fall dynamics. Marker tracking difference went from 17 ± 4 mm for the EKF to 36 ± 11 mm with dynamic optimisation tracking the experimental markers, and to 49 ± 9 mm with dynamic optimisation tracking EKF joint angles. Joint angles from the dynamic optimisations were similar to those of the EKF, and joint torques were smoother. This approach satisfies the dynamics of complex aerial rigid-body movements while remaining close to the experimental 3D marker dataset.

Estimation of gait parameters using leg velocity for amputee population.

Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.

HMD-Net: A Vehicle Hazmat Marker Detection Benchmark.

Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.

Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods.

Downy mildew, commercially the most important disease of spinach, is caused by the obligate oomycete Peronospora effusa. In the past two decades, new pathogen races have repeatedly overcome the resistance used in newly released cultivars, urging the need for more durable resistance. Commercial spinach cultivars are bred with major R genes to impart resistance to downy mildew pathogens and are effective against some pathogen races/isolates. This work aimed to evaluate the worldwide USDA spinach germplasm collections and commercial cultivars for resistance to downy mildew pathogen in the field condition under natural inoculum pressure and conduct genome wide association analysis (GWAS) to identify resistance-associated genomic regions (alleles). Another objective was to evaluate the prediction accuracy (PA) using several genomic prediction (GP) methods to assess the potential implementation of genomic selection (GS) to improve spinach breeding for resistance to downy mildew pathogen. More than four hundred diverse spinach genotypes comprising USDA germplasm accessions and commercial cultivars were evaluated for resistance to downy mildew pathogen between 2017-2019 in Salinas Valley, California and Yuma, Arizona. GWAS was performed using single nucleotide polymorphism (SNP) markers identified via whole genome resequencing (WGR) in GAPIT and TASSEL programs; detected 14, 12, 5, and 10 significantly associated SNP markers with the resistance from four tested environments, respectively; and the QTL alleles were detected at the previously reported region of chromosome 3 in three of the four experiments. In parallel, PA was assessed using six GP models and seven unique marker datasets for field resistance to downy mildew pathogen across four tested environments. The results suggest the suitability of GS to improve field resistance to downy mildew pathogen. The QTL, SNP markers, and PA estimates provide new information in spinach breeding to select resistant plants and breeding lines through marker-assisted selection (MAS) and GS, eventually helping to accumulate beneficial alleles for durable disease resistance.

A Novel Algorithm for Automatic Estimation of Gait Parameters Using Lower-Body Kinematics

Accurate estimation of gait parameters from the limb kinematics data remains a challenge for researchers. Several deterministic algorithms have been proposed in the literature for this purpose but their applicability is limited by their accuracy, especially for gait-impaired subjects. Gait parameter estimation relies on the accurate detection of key gait events of initial contact (IC) and terminal contact (TC) for gait segmentation. This study aimed to evaluate literature algorithms for IC and TC prediction for the amputee population and to introduce a novel algorithm to improve gait parameter estimation. A marker data set of 12 amputee subjects during treadmill walking was used. Seven algorithms, including one novel algorithm, detecting the landmarks (maxima, minima, and zero-crossings) in the foot, shank, and thigh velocity data were implemented and their IC and TC estimations were compared against the force platform data. IC was best detected by foot and shank velocity methods with median bias in the range of -3 to -27ms. However, the literature algorithms produced inferior accuracy for the TC event with consistent early prediction. The newly-proposed thigh velocity-based algorithm produced the best result for the TC prediction -14 to 19ms median bias. By combining the IC prediction from foot velocity data and the TC prediction from the thigh velocity, the algorithm produced the best results for temporal gait parameters with a median bias of within 5% of gait cycle time. Statistical analysis revealed the robustness of the algorithm across most walking speeds.

Improving random forest predictions in small datasets from two-phase sampling designs

BackgroundWhile random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases—a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive.MethodsUsing an immunologic marker dataset from a phase III HIV vaccine efficacy trial, we seek to optimize random forest prediction performance using combinations of variable screening, class balancing, weighting, and hyperparameter tuning.ResultsOur experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative impact on performance in the presence of variable screening. The impact of the weighting similarly depends on whether variable screening is applied. Hyperparameter tuning is ineffective in situations with small sample sizes. We further show that random forests under-perform generalized linear models for some subsets of markers, and prediction performance on this dataset can be improved by stacking random forests and generalized linear models trained on different subsets of predictors, and that the extent of improvement depends critically on the dissimilarities between candidate learner predictions.ConclusionIn small datasets from two-phase sampling design, variable screening and inverse sampling probability weighting are important for achieving good prediction performance of random forests. In addition, stacking random forests and simple linear models can offer improvements over random forests.

Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach

In this paper, we propose a triplet siamese model for lithium-ion battery defects classification. It is a difficult task to detect the surface defects of lithium-ion batteries with stainless steel surface. The lack of three-dimensional information and the lack of marker datasets due to reflections prevent two-dimensional computer vision detection methods from meeting classification needs. In this work, the multiple exposure structured light method is utilized to obtain the three-dimensional shape of a lithium-ion battery with a stainless steel surface. The defect point cloud with three-dimensional information is obtained by this method, and then the 3D information of the defect point cloud is converted into grayscale information, and the grayscale image is used as the target domain data of the triplet siamese network. The public dataset MiniImageNet is utilized as the training data of the triplet siamese network model. The accuracies of the experimental results are 88.9%, 95.6%, and 97.8% for 1-shot, 5-shot, and 10-shot respectively. This result proves that our method can be used for lithium-ion battery defect detection.

Molecular Acquisition, Cleaning and Evaluation in R (MACER) - A tool to assemble molecular marker datasets from BOLD and GenBank.

Molecular sequence data is an essential component for many biological fields of study. The strength of these data is in their ability to be centralised and compared across research studies. There are many online repositories for molecular sequence data, some of which are very large accumulations of varying data types like NCBI’s GenBank. Due to the size and the complexity of the data in these repositories, challenges arise in searching for data of interest. While data repositories exist for molecular markers, taxa and other specific research interests, repositories may not contain, or be suitable for, more specific applications. Manually accessing, searching, downloading, accumulating, dereplicating and cleaning data to construct project-specific datasets is time-consuming. In addition, the manual assembly of datasets presents challenges with reproducibility. Here, we present the MACER package to assist researchers in assembling molecular datasets and provide reproducibility in the process.

Impact of Genomic and Transcriptomic Resources on Apiaceae Crop Breeding Strategies

The Apiaceae taxon is one of the most important families of flowering plants and includes thousands of species used for food, flavoring, fragrance, medical and industrial purposes. This study had the specific intent of reviewing the main genomics and transcriptomic data available for this family and their use for the constitution of new varieties. This was achieved starting from the description of the main reproductive systems and barriers, with particular reference to cytoplasmic (CMS) and nuclear (NMS) male sterility. We found that CMS and NMS systems have been discovered and successfully exploited for the development of varieties only in Foeniculum vulgare, Daucus carota, Apium graveolens and Pastinaca sativa; whereas, strategies to limit self-pollination have been poorly considered. Since the constitution of new varieties benefits from the synergistic use of marker-assisted breeding in combination with conventional breeding schemes, we also analyzed and discussed the available SNP and SSR marker datasets (20 species) and genomes (8 species). Furthermore, the RNA-seq studies aimed at elucidating key pathways in stress tolerance or biosynthesis of the metabolites of interest were limited and proportional to the economic weight of each species. Finally, by aligning 53 plastid genomes from as many species as possible, we demonstrated the precision offered by the super barcoding approach to reconstruct the phylogenetic relationships of Apiaceae species. Overall, despite the impressive size of this family, we documented an evident lack of molecular data, especially because genomic and transcriptomic resources are circumscribed to a small number of species. We believe that our contribution can help future studies aimed at developing molecular tools for boosting breeding programs in crop plants of the Apiaceae family.

The evolution and biogeographic history of epiphytic thalloid liverworts

Among liverworts, the epiphytic lifestyle is not only present in leafy forms but also in thalloid liverworts, which so far has received little attention in evolutionary and biogeographical studies. Metzgeria, with about 107 species worldwide, is the only genus of thalloid liverworts that comprises true epiphytes. In the present study, we provide the first comprehensive molecular phylogeny, including estimated divergence times and ancestral ranges of this genus. Analyses are based on a plastid marker dataset representing about half of the Metzgeria species diversity. We show for the first time with molecular data that Austrometzgeria is indeed a member of Metzgeria and that two morpho-species M. furcata and M. leptoneura are not monophyletic, but rather represent geographically well-defined clades. Our analyses indicate that Metzgeria started to diversify in the Cretaceous in an area encompassing today’s South America and Australasia. Thus, Metzgeria is one of the few known epiphytic liverwort genera whose biogeographic history was directly shaped by Gondwana vicariance. Subsequent dispersal events in the Cenozoic resulted in the colonization of Asia, Africa, North America, and Europe and led to today’s worldwide distribution of its species. We also provide the first reliable stem age estimate for Metzgeria due to the inclusion of its sister taxon Vandiemenia in our dating analyses. Additionally, this stem age estimate of about 240 million years most likely marks the starting point of a transition from a terrestrial to an epiphytic lifestyle in thalloid liverworts of the Metzgeriales. We assume that the Cretaceous Terrestrial Revolution played a key role in the evolution of epiphytic thalloid liverworts similar to that known for leafy liverworts.

A roadmap to Durable BCTV Resistance Using Long-Read Genome Assembly of Genetic Stock KDH13

Beet Curly Top (BCT) is a viral disease which negatively impacts crop productivity for sugar beet growers and the sugar beet industry in the western USA and dry regions worldwide. Current varieties exhibit little genetic resistance to the Beet Curly Top Virus (BCTV), suggesting there is a large potential for improvement. KDH13 (PI 663862) is a double-haploid line created from a population (C762-17/PI 560130) which segregates for resistance to BCTV and was identified as genetic stock for the improvement of sugar beet varieties. PacBio sequences were generated and assembled to better define the content and organization of variation within the KDH13 genome and to provide resources to identify specific variation underpinning durable genetic resistance. Using ab initio predicted proteins as anchors, the assembled KDH13 contigs were placed in a more contiguous order using the EL10.1 reference genome, which leveraged Bio-Nano optical maps and Hi-C proximity information for chromosome level scaffolding. In total, 4681 (75%) of the 6245 contigs were placed in the order and orientation of the EL10.1 genome. The anchored contigs represented 502,929,268 bp (87.7%), the KDH13 genome assembly. An F1 hybrid and parental lines KDH13 (resistant) and KDH19-17 (susceptible) were sequenced using Illumina technology in order to characterize the SNP, indel, and structural variation between parental lines and allow for a more detailed investigation into causal variation linked to important phenotypes. In total, 3,086,720 variants were detected, including 2,259,324 single-nucleotide polymorphisms, 191,448 insertions, 198,057 deletions, 268,090 complex substitutions, 90,004 multi allelic variants, and 79,797 structural variants. Of the total variation, 1,158,491 were informative in the F1 and were able to discriminate between the two parents. This information represents a high-density marker dataset distributed globally across the sugar beet genome and can be used to track genomic segments in populations where KDH13 is used as parental material to improve BCTV resistance.

Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum.

Community association populations are composed of phenotypically and genetically diverse accessions. Once these populations are genotyped, the resulting marker data can be reused by different groups investigating the genetic basis of different traits. Because the same genotypes are observed and scored for a wide range of traits in different environments, these populations represent a unique resource to investigate pleiotropy. Here, we assembled a set of 234 separate trait datasets for the Sorghum Association Panel, a group of 406 sorghum genotypes widely employed by the sorghum genetics community. Comparison of genome-wide association studies (GWAS) conducted with two independently generated marker sets for this population demonstrate that existing genetic marker sets do not saturate the genome and likely capture only 35–43% of potentially detectable loci controlling variation for traits scored in this population. While limited evidence for pleiotropy was apparent in cross-GWAS comparisons, a multivariate adaptive shrinkage approach recovered both known pleiotropic effects of existing loci and new pleiotropic effects, particularly significant impacts of known dwarfing genes on root architecture. In addition, we identified new loci with pleiotropic effects consistent with known trade-offs in sorghum development. These results demonstrate the potential for mining existing trait datasets from widely used community association populations to enable new discoveries from existing trait datasets as new, denser genetic marker datasets are generated for existing community association populations.

CountfitteR: efficient selection of count distributions to assess DNA damage.

DNA double-strand breaks can be counted as discrete foci by imaging techniques. In personalized medicine and pharmacology, the analysis of counting data is relevant for numerous applications, e.g., for cancer and aging research and the evaluation of drug efficacy. By default, it is assumed to follow the Poisson distribution. This assumption, however, may lead to biased results and faulty conclusions in datasets with excess zero values (zero-inflation), a variance larger than the mean (overdispersion), or both. In such cases, the assumption of a Poisson distribution would skew the estimation of mean and variance, and other models like the negative binomial (NB), zero-inflated Poisson or zero-inflated NB distributions should be employed. The model chosen has an influence on the parameter estimation (mean value and confidence interval). Yet the choice of the suitable distribution model is not trivial. To support, simplify and objectify this process, we have developed the countfitteR software as an R package. We used a Bayesian approach for distribution model selection and the shiny web application framework for interactive data analysis. We show the application of our software based on examples of DNA double-strand break count data from phenotypic imaging by multiplex fluorescence microscopy. In analyzing numerous datasets of molecular pharmacological markers (phosphorylated histone H2AX and p53 binding protein), countfitteR demonstrated an equal or superior statistical performance compared to the usually employed two-step procedure, with an overall power of up to 98%. In addition, it still gave information in cases with no result at all from the two-step procedure. In our data sample we found that the NB distribution was the most frequent, with the Poisson distribution taking second place. countfitteR can perform an automated distribution model selection and thus support the data analysis and lead to objective statistically verifiable estimated values. Originally designed for the analysis of foci in biomedical image data, countfitteR can be used in a variety of areas where non-Poisson distributed counting data is prevalent.