Map Distance Research Articles

A consensus and saturated genetic map provides insight into genome anchoring, synteny of Solanaceae and leaf- and fruit-related QTLs in wolfberry (Lycium Linn.)

BackgroundLycium Linn. (Solanaceae) is a genus of economically important plants producing fruits and leaves with high nutritional value and medicinal benefits. However, genetic analysis of this plant and molecular breeding for quality improvement are limited by the lack of sufficient molecular markers.ResultsIn this study, two parental strains, ‘Ningqi No. 1’ (Lycium barbarum L.) and ‘Yunnan Gouqi’ (Lycium yunnanense Kuang et A.M. Lu), and 200 F1 hybrid individuals were resequenced for genetic analysis. In total, 8,507 well-selected SNPs were developed, and a high-density genetic map (NY map) was constructed with a total genetic distance of 2,122.24 cM. A consensus genetic map was established by integrating the NY map and a previously published genetic map (NC map) containing 15,240 SNPs, with a total genetic distance of 3,058.19 cM and an average map distance of 0.21 cM. The 12 pseudochromosomes of the Lycium reference genome were anchored using this consensus genetic map, with an anchoring rate of 64.3%. Moreover, weak collinearities between the consensus map and the pepper, potato, and tomato genomes were observed. Twenty-five stable QTLs were identified for leaf- and fruit-related phenotypes, including fruit weight, fruit longitude, leaf length, the fruit index, and the leaf index; these stable QTLs were mapped to four different linkage groups, with LOD scores ranging from 2.51 to 19.37 and amounts of phenotypic variance explained from 6.2% to 51.9%. Finally, 82 out of 188 predicted genes underlying stable QTLs for fruit-related traits were differentially expressed according to RNA-seq analysis.ConclusionsA chromosome-level assembly can provide a foundation for further functional genomics research for wolfberry. The genomic regions of these stably expressed QTLs could be used as targets for further fine mapping and development of molecular markers for marker-assisted selection (MAS). The present study provided valuable information on saturated SNP markers and reliable QTLs for map-based cloning of functional genes related to yield and morphological traits in Lycium spp.

Investigating drivers’ geospatial abilities in unfamiliar environments

Abstract. The prominence of landmarks in aiding pedestrian navigation has been highlighted in various studies; people rely strongly on visual landmarks, especially when navigating in unfamiliar environments. The paper describes the design and implementation of a study for assessing drivers’ spatial abilities, when navigating in an unfamiliar environment. Two types of route directions based on references to either landmarks or street names were given to two groups of participants. Three geospatial learning tasks are used to evaluate these abilities: map sketching, distance, and direction estimation. The findings showed that landmark-based route instructions help drivers develop a better cognitive map of the route. On the other hand, instructions either based on landmarks or on street information do not have an effect on distance or direction estimates. Nonetheless, qualitative analysis of directions and distances estimations gave interesting results. Findings associated with self-assessment of environmental spatial abilities using the Santa Barbara Sense of Direction Scale (SBSOD) seem to support prediction of at least one of the drivers’ abilities among those assessed in this study.

Photoacoustic-Guided Laparoscopic and Open Hysterectomy Procedures Demonstrated With Human Cadavers.

Hysterectomy (i.e., surgical removal of the uterus) requires severing the main blood supply to the uterus (i.e., the uterine arteries) while preserving the nearby, often overlapping, ureters. In this paper, we investigate dual-wavelength and audiovisual photoacoustic imaging-based approaches to visualize and differentiate the ureter from the uterine artery and to provide the real-time information needed to avoid accidental ureteral injuries during hysterectomies. Dual-wavelength 690/750 nm photoacoustic imaging was implemented during laparoscopic and open hysterectomies performed on human cadavers, with a custom display approach designed to visualize the ureter and uterine artery. The proximity of the surgical tool to the ureter was calculated and conveyed by tracking the surgical tool in photoacoustic images and mapping distance to auditory signals. The dual-wavelength display showed up to 10 dB contrast differences between the ureter and uterine artery at three separation distances (i.e., 4 mm, 5 mm, and 6 mm) during the open hysterectomy. During the laparoscopic hysterectomy, the ureter and uterine artery were visualized in the dual-wavelength image with up to 24 dB contrast differences. Distances between the ureter and the surgical tool ranged from 2.47 to 7.31 mm. These results are promising for the introduction of dual-wavelength photoacoustic imaging to differentiate the ureter from the uterine artery, estimate the position of the ureter relative to a surgical tool tip, map photoacoustic-based distance measurements to auditory signals, and ultimately guide hysterectomy procedures to reduce the risk of accidental ureteral injuries.

Identification of consistent QTL for time to maturation in Virginia-type Peanut (Arachis hypogaea L.)

BackgroundTime-to-maturation (TTM) is an important trait contributing to adaptability, yield and quality in peanut (Arachis hypogaea L). Virginia market-type peanut belongs to the late-maturing A. hypogaea subspecies with considerable variation in TTM within this market type. Consequently, planting and harvesting schedule of peanut cultivars, including Virginia market-type, need to be optimized to maximize yield and grade. Little is known regarding the genetic control of TTM in peanut due to the challenge of phenotyping and limited DNA polymorphism. Here, we investigated the genetic control of TTM within the Virginia market-type peanut using a SNP-based high-density genetic map. A recombinant inbred line (RIL) population, derived from a cross between two Virginia-type cultivars ‘Hanoch’ and ‘Harari’ with contrasting TTM (12–15 days on multi-years observations), was phenotyped in the field for 2 years following a randomized complete block design. TTM was estimated by maturity index (MI). Other agronomic traits like harvest index (HI), branching habit (BH) and shelling percentage (SP) were recorded as well.ResultsMI was highly segregated in the population, with 13.3–70.9% and 28.4–80.2% in years 2018 and 2019. The constructed genetic map included 1833 SNP markers distributed on 24 linkage groups, covering a total map distance of 1773.5 cM corresponding to 20 chromosomes on the tetraploid peanut genome with 1.6 cM mean distance between the adjacent markers. Thirty QTL were identified for all measured traits. Among the four QTL regions for MI, two consistent QTL regions (qMIA04a,b and qMIB03a,b) were identified on chromosomes A04 (118680323–125,599,371; 6.9Mbp) and B03 (2839591–4,674,238; 1.8Mbp), with LOD values of 5.33–6.45 and 5–5.35 which explained phenotypic variation of 9.9–11.9% and 9.3–9.9%, respectively. QTL for HI were found to share the same loci as MI on chromosomes B03, B05, and B06, demonstrating the possible pleiotropic effect of HI on TTM. Significant but smaller effects on MI were detected for BH, pod yield and SP.ConclusionsThis study identified consistent QTL regions conditioning TTM for Virginia market-type peanut. The information and materials generated here can be used to further develop molecular markers to select peanut idiotypes suitable for diverse growth environments.

Genome‐wide SNP discovery and QTL mapping for economic traits in a recombinant inbred line of Oryza sativa

AbstractThe objective of the study was to understand the genetic basis of eight traits conferring grain appearance and yield in a recombinant inbred line (RIL8) derived from a cross between cultivated Indica rice varieties Dexiang074B and Pujiang6. We conducted whole genome re‐sequencing for the two parental lines and genotyping by sequencing (GBS) for the RIL8 population. Next, we aligned the clean reads to the rice genome Nipponbare IRGSP 1.0, constructed the genetic linkage map (GLM), and performed the quantitative trait loci (QTL) analysis. In total, 2631 high‐quality markers were generated for 315 RIL8 samples. We identified a rice GLM containing 12 linkage groups (total map distance: 1946.71 cM; average map distance: 0.74 cM) and 98 QTLs related to the rice yield, including 31 high credible QTLs. In addition, 39 QTLs were identified to be associated with the G*E interaction. While most of the QTLs were co‐localized with previously reported rice QTLs, some novel QTLs were identified for the first time in this study. These findings provide a genetic basis for developing cultivated rice with good economic characteristics based on molecular breeding technologies.

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

BackgroundDriven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction.ResultsTo explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment.ConclusionsDeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

A practical approach for self-potential data acquisition, processing, and visualization

We have developed a comprehensive methodology for the acquisition and processing of self-potential (SP) data, as well as some keys for the interpretation of the results. The wide applicability of the SP method and its low cost make it a popular method to use in a variety of natural environments. Despite its versatility and the fact that various published journal papers describe the method and its applications, we believe that there is an important need for a dedicated, peer-reviewed SP acquisition, processing, and visualization/interpretation paper in the scientific literature. We have identified great interest from the scientific community for such a journal paper as a guide for existing and new practitioners with their SP survey design, data acquisition, robust processing, and initial interpretation steps. We have developed a step-by-step methodology for SP data acquisition and processing, combined with practical guidance for the interpretation of collected and processed SP data, including an evaluation of common errors and typical sources of uncertainty. Our examples are based on studies in volcanic environments (e.g., hydrothermal systems); however, the processing steps and methodology are fully applicable and transferable across disciplines to SP data acquired in any environment, and for a wide variety of applications. We evaluated the field acquisition method and the low-cost equipment, the reference and closure corrections, their meaning for the SP signal, and their effect on the data set. The benefits of interpolating SP data in two steps are examined. Combining map visualization, SP versus distance, and SP versus elevation graphs appears to be a highly effective strategy to interpret the signal in terms of hydrogeologic and hydrothermal domains and to highlight structural limits in volcanic contexts as well as in other environments.

Debris flow susceptibility and propagation assessment in West Koyulhisar, Turkey

Turkey is highly prone to landslides because of the geological and geographic location. The study area, which is located in a tectonically active region, has been significantly affected by mass movements. Flow type landslides are frequently observed due to this location. This study aims at determining the source area and propagation of debris flows in the study area. We used the heuristic method to extract source areas of debris flow, and then used receiver operating characteristic (ROC) curve analysis to assess the performance of the method, and finally calculated the Area under curve (AUC) values being 83.64% and 80.39% for the success rate and prediction rate, respectively. We calculated potential propagation area and runout distance with Flow-R software. In conclusion, the obtained results (susceptibility map, propagation and runout distance) are very important for decision-makers at the region located on an active fault zone, which is highly prone to natural disasters. The outputs of this study could be used in site selection studies, designing erosion prevention systems and protecting existing human-made structures.

QTL mapping and GWAS for field kernel water content and kernel dehydration rate before physiological maturity in maize

Kernel water content (KWC) and kernel dehydration rate (KDR) are two main factors affecting maize seed quality and have a decisive influence on the mechanical harvest. It is of great importance to map and mine candidate genes related to KWCs and KDRs before physiological maturity in maize. 120 double-haploid (DH) lines constructed from Si287 with low KWC and JiA512 with high KWC were used as the mapping population. KWCs were measured every 5 days from 10 to 40 days after pollination, and KDRs were calculated. A total of 1702 SNP markers were used to construct a linkage map, with a total length of 1,309.02 cM and an average map distance of 0.77 cM. 10 quantitative trait loci (QTLs) and 27 quantitative trait nucleotides (QTNs) were detected by genome-wide composite interval mapping (GCIM) and multi-locus random-SNP-effect mixed linear model (mrMLM), respectively. One and two QTL hotspot regions were found on Chromosome 3 and 7, respectively. Analysis of the Gene Ontology showed that 2 GO terms of biological processes (BP) were significantly enriched (P ≤ 0.05) and 6 candidate genes were obtained. This study provides theoretical support for marker-assisted breeding of mechanical harvest variety in maize.

Construction of a High-Density Genetic Map Based on SLAF Markers and QTL Analysis of Leaf Size in Rice.

Leaf shape is an important agronomic trait for constructing an ideal plant type in rice, and high-density genetic map is facilitative in improving accuracy and efficiency for quantitative trait loci (QTL) analysis of leaf trait. In this study, a high-density genetic map contained 10,760 specific length amplified fragment sequencing (SLAF) markers was established based on 149 recombinant inbred lines (RILs) derived from the cross between Rekuangeng (RKG) and Taizhong1 (TN1), which exhibited 1,613.59 cM map distance with an average interval of 0.17 cM. A total of 24 QTLs were detected and explained the phenotypic variance ranged from 9% to 33.8% related to the leaf morphology across two areas. Among them, one uncloned major QTL qTLLW1 (qTLL1 and qTLLW1) involved in regulating leaf length and leaf width with max 33.8% and 22.5% phenotypic variance respectively was located on chromosome 1, and another major locus qTLW4 affecting leaf width accounted for max 25.3% phenotypic variance was mapped on chromosome 4. Fine mapping and qRT-PCR expression analysis indicated that qTLW4 may be allelic to NAL1 (Narrow leaf 1) gene.

High-density genetic map construction and identification of loci controlling flower-type traits in Chrysanthemum (Chrysanthemum \xd7 morifolium Ramat.)

Flower type is an important and extremely complicated trait of chrysanthemum. The corolla tube merged degree (CTMD) and the relative number of ray florets (RNRF) are the two key factors affecting chrysanthemum flower type. However, few reports have clarified the inheritance of these two complex traits, which limits directed breeding for flower-type improvement. In this study, 305 F1 hybrids were obtained from two parents with obvious differences in CTMD and RNRF performance. Using specific-locus amplified fragment sequencing (SLAF-seq) technology, we constructed a high-density genetic linkage map with an average map distance of 0.76 cM. Three major QTLs controlling CTMD and four major QTLs underlying RNRF were repeatedly detected in the 2 years. Moreover, the synteny between the genetic map and other Compositae species was investigated, and weak collinearity was observed. In QTL regions with a high degree of genomic collinearity, eight annotated genes were probed in the Helianthus annuus L. and Lactuca sativa L. var. ramosa Hort. genomes. Furthermore, 20 and 11 unigenes were identified via BLAST searches between the SNP markers of the QTL regions and the C. vestitum and C. lavandulifolium transcriptomes, respectively. These results lay a foundation for molecular marker-assisted breeding and candidate gene exploration in chrysanthemum without a reference assembly.

Identification of QTL for resistance to root rot in sweetpotato (Ipomoea batatas (L.) Lam) with SSR linkage maps

BackgroundSweetpotato root rot is a devastating disease caused by Fusarium solani that seriously endangers the yield of sweetpotato in China. Although there is currently no effective method to control the disease, breeding of resistant varieties is the most effective and economic option. Moreover, quantitative trait locus (QTL) associated with resistance to root rot have not yet been reported, and the biological mechanisms of resistance remain unclear in sweetpotato. Thus, increasing our knowledge about the mechanism of disease resistance and identifying resistance loci will assist in the development of disease resistance breeding.ResultsIn this study, we constructed genetic linkage maps of sweetpotato using a mapping population consisting of 300 individuals derived from a cross between Jizishu 1 and Longshu 9 by simple sequence repeat (SSR) markers, and mapped seven QTLs for resistance to root rot. In total, 484 and 573 polymorphic SSR markers were grouped into 90 linkage groups for Jizishu 1 and Longshu 9, respectively. The total map distance for Jizishu 1 was 3974.24 cM, with an average marker distance of 8.23 cM. The total map distance for Longshu 9 was 5163.35 cM, with an average marker distance of 9.01 cM. Five QTLs (qRRM_1, qRRM_2, qRRM_3, qRRM_4, and qRRM_5) were located in five linkage groups of Jizishu 1 map explaining 52.6–57.0% of the variation. Two QTLs (qRRF_1 and qRRF_2) were mapped on two linkage groups of Longshu 9 explaining 57.6 and 53.6% of the variation, respectively. Furthermore, 71.4% of the QTLs positively affected the variation. Three of the seven QTLs, qRRM_3, qRRF_1, and qRRF_2, were colocalized with markers IES43-5mt, IES68-6 fs**, and IES108-1 fs, respectively.ConclusionsTo our knowledge, this is the first report on the construction of a genetic linkage map for purple sweetpotato (Jizishu 1) and the identification of QTLs associated with resistance to root rot in sweetpotato using SSR markers. These QTLs will have practical significance for the fine mapping of root rot resistance genes and play an important role in sweetpotato marker-assisted breeding.

Bayesian interval mapping of count trait loci based on zero-inflated generalized Poisson regression model.

Count phenotypes with excessive zeros are often observed in the biological world. Researchers have studied many statistical methods for mapping the quantitative trait loci (QTLs) of zero-inflated count phenotypes. However, most of the existing methods consist of finding the approximate positions of the QTLs on the chromosome by genome-wide scanning. Additionally, most of the existing methods use the EM algorithm for parameter estimation. In this paper, we propose a Bayesian interval mapping scheme of QTLs for zero-inflated count data. The method takes advantage of a zero-inflated generalized Poisson (ZIGP) regression model to study the influence of QTLs on the zero-inflated count phenotype. The MCMC algorithm is used to estimate the effects and position parameters of QTLs. We use the Haldane map function to realize the conversion between recombination rate and map distance. Monte Carlo simulations are conducted to test the applicability and advantage of the proposed method. The effects of QTLs on the formation of mouse cholesterol gallstones were demonstrated by analyzing an mouse dataset.

Support Vector Machine Based Route Classification and Description

Traveling is very much important in one's life. Location-based services have developed a lot due to the development of communication technologies. It confines services that execute programs that use geographical data. Map services authorize travelers to look for the information surrounding them and organize an outing to his/her best-loved spot. Google Maps API is convenient for locating the shortest route information. However, the map system doesn't supply any illustration about air quality or congestion in a path. At times, a substitute route with less congestion can take you quickly to your spot than a shorter route. Numerous crucial health concerns for human beings are caused due to pollution. The motive is to develop a system that offers the textual explanation of routes utilizing the sub-routes information from Google map and BreezoMeter. The end-user can choose the starting and ending points of his/her travel and the route map showing various routes from source to destination is exhibited along with a small description of each route. The illustration of the routes is obtained depending on three factors such as air quality, congestion and distance gathered from BreezoMeter, Google map traffic API and distance matrix API respectively. Multicategory Support Vector Machine (SVM) is an organized and guided categorization technique and is used here to classify factors into various levels. Since the textual illustration of the route is accessible, the end-user can effortlessly understand the details about the route and they can choose a particular route.

Support Vector Machine Based Route Classification and Description

Traveling is very much important in one's life. Location-based services have developed a lot due to the development of communication technologies. It confines services that execute programs that use geographical data. Map services authorize travelers to look for the information surrounding them and organize an outing to his/her best-loved spot. Google Maps API is convenient for locating the shortest route information. However, the map system doesn't supply any illustration about air quality or congestion in a path. At times, a substitute route with less congestion can take you quickly to your spot than a shorter route. Numerous crucial health concerns for human beings are caused due to pollution. The motive is to develop a system that offers the textual explanation of routes utilizing the sub-routes information from Google map and BreezoMeter. The end-user can choose the starting and ending points of his/her travel and the route map showing various routes from source to destination is exhibited along with a small description of each route. The illustration of the routes is obtained depending on three factors such as air quality, congestion and distance gathered from BreezoMeter, Google map traffic API and distance matrix API respectively. Multicategory Support Vector Machine (SVM) is an organized and guided categorization technique and is used here to classify factors into various levels. Since the textual illustration of the route is accessible, the end-user can effortlessly understand the details about the route and they can choose a particular route.

Accelerated Protein Folding Using Greedy-Proximal A.

The protein folding problem has been studied in the field of molecular biophysics and biochemistry for many years. Even small changes in folding patterns may lead to serious diseases such as Alzheimer's or Parkinson's where proteins are folded either too quickly or too slowly. Molecular dynamics (MD) is one of the tools used to understand how proteins fold into native conformations. While it captures sequences of conformations that lead over time to the folded state, limitations in simulation timescales remain problematic. Although many approaches have been suggested to speed up the simulation process using rapid changes in temperature or pressure, we propose a rational approach, Greedy-proximal A* (GPA*), derived from path finding algorithms to explore the supposed shortest path folding pathway from the unfolded to a given folded conformation. We introduce several new protein structure comparison metrics based on the contact map distance to help mitigate the challenges faced by "standard" metrics. We test our approach on proteins which represent the two main types of secondary structure: (a) the Trp-cage miniprotein construct TC5b (1L2Y) which is a short, fast-folding protein that represents an α-helical secondary structure formed because of a locked tryptophan in the middle, (b) the immunoglobulin binding domain of the streptococcal protein G (1GB1), containing an α-helix and several β-sheets, and (c) the chicken villin subdomain HP-35, N68H protein (1YRF)-one of the fastest folding proteins which forms three α-helices. We compare our algorithm to replica-exchange MD and steered MD methods which represent the main algorithms used for accelerating folding proteins with MD. We find that GPA* not only reduces the computational time needed to obtain the folded conformation without adding artificial energy bias but also makes it possible to generate trajectories which contain minimal motions needed for the folding transition.

Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

Genetics and molecular mapping for salinity stress tolerance at seedling stage in lentil (Lens culinaris Medik)

AbstractAn investigation was conducted to understand the genetics and molecular mapping for salinity stress tolerance in lentil at the seedling stage. The populations were developed through crossing between salt sensitive (L‐4147 and L‐4076) and salt‐tolerant (PDL‐1 and PSL‐9) genotypes. The parents, F1, F2, F3 and backcross populations were assayed in salt solution at 120 mM NaCl for assessing salinity stress tolerance based on seedling survival and a Fluorescein diacetate (FDA) signal. The F1s were found tolerant to salinity stress indicating their dominance over the sensitive ones. The F2 segregation fitted well with the expected monogenic frequency ratio of salt‐tolerant: salt‐sensitive plants, which indicates that salinity stress tolerance is governed by a single dominant gene. This was also confirmed in F3 and backcross segregation data. Allelism test supported the hypothesis that the same gene was conferring stress tolerance in tolerant genotypes (PDL‐1 and PSL‐9). This matched with major QTL of seedling survival under salinity stress. Four hundred and ninety‐five SSR markers were analyzed for polymorphism and 11 of them were found polymorphic between the parents. Among eleven polymorphic markers, seven were associated with seedling survival under salinity stress. The QTL of this trait was mapped within a map distance of 133.02 cM in F2 mapping population (L‐4147 × PDL‐1) and it was found located on linkage group 1 (LG_1) and explained phenotypic variance of 65.6%. This report on QTL mapping should be useful for dissection of candidate genes and development of molecular markers for improvement of salinity stress tolerance in lentil.

Mutants in mitochondrial DNA repair genes in Arabidopsis thaliana generated by using CRISPR‐Cas9

The genomes of plant mitochondria exhibit unique mutation rates and structural variability. Compared to their animal counterparts, plant mitochondria have low mutation rates, high recombination rates, and non‐circular genomes. Recent evidence suggests the prevalence of double‐strand break repair (DSBR) in plant mitochondria as one possible cause of these traits. Interestingly, all of the genes required for plant mitochondrial DSBR are located in the nucleus.In order to study DSBR, it is necessary to be able to reliably generate single and double‐mutants for DSBR genes. Using traditional methods, such as T‐DNA insertions, double mutant generation has proven cumbersome due to the occasional difficulty in PCR‐based identification of single mutants, as well as the low map distance between several genes of interest. CRISPR‐induced knockouts allow us to overcome some of these difficulties and generate a greater variety of mutants in a shorter period of time. In this study, we selected five proteins related to the processing of double strand breaks (DSBs) in plant mitochondria for mutagenesis: MSH1, RECA3, RNaseH1, SUV3, and OTP87. We prepared CRISPR Cas9 vectors to produce knockouts for each of these genes in Arabidopsis thaliana. The reliable production of mutants for these 5 genes will allow for in depth study of DSBR in the future. CRISPR Cas9 constructs were designed to produce two guide RNAs for each gene, targeting two different exons in the gene of interest, to give a better chance of successful knockouts, or deletions, between the two sites. These constructs were then transformed into Agrobacterium. Successful transformants were selected using a spectinomycin resistance gene in the construct. We then used a standard floral dip protocol to generate mutant Arabidopsis plants. PCR analysis confirmed Arabidopsis knockout mutations in all five lines.The mutants generated in this study will allow us to investigate several avenues of research on DSBR. We plan to expose plants with knockouts in either one or two DSBR genes to ciprofloxacin, an antibiotic which induces double strand breaks in plant mitochondrial DNA. From there we can observe the mitochondrial genome recombination rates using PCR and Illumina sequencing for each of the mutants.Support or Funding InformationThis work was supported in part by grants from the National Science Foundation to A.C.C. (MCB‐1413152 and MCB‐1933590) and a grant from the UNL UCARE program (26‐0101‐8001‐001)

Identification of genomic regions associated with early plant vigour in lentil (Lens culinaris)

Lentil is one of the most important food legume species, however its genetic and genomic resources remained largely uncharacterized and unexploited. In the past few years, a number of genetic maps have been constructed and marker resources have been developed in lentil. These resources could be exploited for understanding the extent and distribution of genetic variation in genus Lens and also for developing saturated and consensus genetic maps suitable for quantitative trait loci (QTL) mapping and marker-assisted selection. The present study aims to enrich polymerase chain reaction-based linkage map of F10 recombinant inbred lines (RILs) population of 94 individuals derived from cross WA8649090 9 Precoz and identification of QTLs linked to early plant vigour traits. Of the 268 polymorphic markers (93 simple sequence repeats (SSR), three inter-simple sequence repeats (ISSRs) and 172 random amplified polymorphic DNA (RAPDs)), 265 (90 SSRs, three ISSRs and 172 RAPDs) were mapped on seven linkage groups, varying in length between 25.6 and 210.3 cM, coverage of 809.4 cM with an average marker spacing of 3.05 cM. The study also reported assigning of 24 new cross-genera SSRs of Trifolium pratense on the present linkage map. The RILs along with the parents were screened for shoot length, root length, seedling length, dry weight, number of leaves and number of branches based on two replications under polyhouse conditions. A QTLhotspot consisting of six QTLs for shoot length (cm), root length (cm) and seedling length (cm) was observed between a map distances of56.61 and 86.81 cM on LG1.