Biology subjects

Diaz-Garcia, L.

Publications and source records attributed to Diaz-Garcia, L..

17 recordsLinked to original sources

bioRxiv2026

Genetic Mapping Reveals a Major Locus for Boron Exclusion in Wild Vitis

Boron toxicity constrains grapevine (Vitis) production in irrigated arid and semi-arid regions, yet most available rootstocks are sensitive to excessive boron. Although underutilized Vitis species may harbor sources of adaptation, the genetic basis of natural variation in boron exclusion remains poorly understood, limiting the development of boron-tolerant rootstocks. We screened 267 wild Vitis accessions representing 16 species for their ability to limit leaf boron accumulation under excess boron conditions. Leaf boron concentration varied 190-fold among accessions and was highly heritable (entry-mean H^2 = 0.71). Its correlation with stomatal conductance was modest (r = 0.34), indicating that transpiration rate explained only a small proportion of the variation in leaf boron accumulation. Leaf boron was correlated with leaf chloride (r = 0.36), but their largely non-overlapping association profiles supported distinct genetic control. Genome-wide association analysis identified a suggestive association on chromosome 11. Independent quantitative trait locus mapping in a full-sib F1 population (n = 158) derived from Longii-9018 (V. acerifolia) x GRN3 (commercial rootstock) identified qBEx11.1 in the same syntenic region on chromosome 11, which explained 19.5% of the phenotypic variance. Positional candidate genes near the association peak included a pectinesterase and a MATE efflux transporter, suggesting roles for cell wall modification and membrane transport. Leaf spectral reflectance was associated with leaf boron concentration in both populations, particularly at visible and red-edge wavelengths, indicating potential for high-throughput phenotyping. Together, these findings identify qBEx11.1 as a priority locus for boron exclusion and support integrated genetic and spectral approaches for breeding boron-tolerant grapevine rootstocks.

plant biology↗

bioRxiv2026

Sixteen thousand grafts reveal limited scion genetic control of grapevine bench-grafting success

Nurseries produce tens of millions of bench-grafted grapevines (Vitis vinifera L.) each year, yet the genetics underlying grafting success rate remain poorly understood. Here, we dissected the genetic architecture of this trait in a scion mapping population. We grafted 138 progeny from a Riesling x Cabernet Sauvignon cross, along with both parents, onto 1103P, with 40 grafts per genotype in each of three separate field blocks and grafted on separate days (16,645 grafts). Success averaged 91.1 %. Scion genotype explained 19 % of the variance among blocks (entry-mean heritability 0.42); genotype x block variance exceeded genotype variance, and a few genotypes failed almost completely in a single block. Success was uncorrelated with yield of the mother vines. QTL mapping across six phenotype definitions and four haplotype-resolved parental genome assemblies detected one modest, reproducible QTL on chromosome 7 (Cabernet Sauvignon alleles; +3.9 percentage points; 11 to 15 % of variance) and suggestive regions on chromosomes 5 and 9. Most variation in grafting success within this elite V. vinifera cross was non-genetic and attributable to events affecting entire bundles. These results set expectations for effect sizes, replication, and process control in genetic studies of grafting success.

genetics↗

bioRxiv2026

Low-cost rhizotron imaging and zero-shot deep-learning resolve temporal, spatial, and genetic variation in grapevine rootstock root systems

Root system architecture shapes how grapevine rootstocks take up water and nutrients, yet roots remain the least phenotyped grapevine organ because they are hidden and hard to image. We present a low-cost phenotyping pipeline that pairs custom acrylic rhizotrons (about US$30 each) with a consumer flatbed scanner and BiRefNet, a general-purpose deep-learning model used without training on root images, followed by automated mask cleaning, skeleton-based trait extraction, and soil moisture mapping. We tested it on nine commercial rootstocks scanned 16 times over 42 days after transplanting (DAT), with half under a ten-day water deficit. From 1,108 images we extracted 21 whole-root, depth-resolved, and topological traits. Genotypes differed in nearly every trait and in how they changed over time. Heritability of size and branching traits peaked at 0.92-0.93 between 21 and 31 DAT and fell for width, depth, and convex hull once roots reached the rhizotron walls, defining the best measurement window. The image-derived soil moisture map accurately tracked the deficit and its recovery. Deficit plants shifted new root growth to deeper soil without growing less overall, and the substrate dried fastest around older and denser roots. Root brightness decreased with root age and local moisture, and transport segments (axes serving several tips) were brighter than terminal laterals in every genotype. Root system size was associated with stomatal conductance in well-watered plants, and stomatal recovery after re-watering correlated with new root growth. The pipeline turns simple hardware into a quantitative, time-resolved root phenotyping platform suitable for breeding.

plant biology↗

bioRxiv2026

Harnessing Vitis germplasm diversity to dissect and predict adventitious rooting traits in grapevine

Adventitious root formation (ARF) is a critical trait for the cost-effective propagation of grapevines in commercial nurseries. Poor rooting ability can limit the use and adoption of new rootstocks derived from underutilized Vitis species, constraining breeding efforts largely to the traditional trio: Vitis riparia, V. rupestris, and V. berlandieri. Despite its agronomic relevance, the genetic basis of ARF remains poorly characterized across the broader Vitis genus. In this study, we evaluated 308 accessions representing 18 Vitis species over three growing seasons, quantifying rooting performance at two developmental stages, callus-stage and post-transplant, alongside root biomass, cutting weight, and a derived transplant-response index. We observed extensive phenotypic variation both within and across species, and species rankings depended on the trait considered. V. riparia, V. rupestris and V. californica ranked among the top five species for all four rooting traits, whereas V. cinerea and V. candicans ranked among the lowest for root weight and post-transplant rooting. V. arizonica and V. acerifolia rooted well at the callus stage but were intermediate after transplanting, and V. berlandieri was among the weakest at the callus stage yet intermediate for post-transplant rooting. Repeatability was moderate to high for root weight (0.74) and callus-stage rooting (0.66), and lower for post-transplant rooting (0.47), reflecting both genetic control and season-to-season variation. Between-species differences accounted for 68% of the genetic variance in callus-stage rooting but only 10% in cutting weight. Rooting was associated with the climate of each accession's wild site of origin: after removing differences among species, accessions originating from sites with lower dry-season precipitation rooted better and produced more root biomass. Genome-wide association analysis using 3.4 million SNPs identified 54 significant SNPs resolving into 18 independent loci across four traits, with root weight contributing 12 of them. Candidate genes in linkage with these loci include a mitogen-activated protein kinase, a SCARECROW-LIKE GRAS transcription factor, PASTICCINO1, expansin A1, an AP2/ERF-RAV1 transcription factor, a tandem array of caffeoyl-CoA O-methyltransferases, and several sugar, peptide and nitrate transporters, implicating auxin-linked cell proliferation, cell wall and lignin remodeling, and solute transport. Genomic and phenomic prediction models yielded moderate accuracies across traits and seasons; up to r = 0.67 for post-transplant rooting within a season and r = 0.65 for previously unevaluated accessions. Moreover, the integration of spectral and genotypic data further improved predictive performance. Prediction accuracy was essentially flat between 5,000 and 50,000 markers. This study establishes a foundational framework for the genetic improvement of grapevine rootstocks, promoting broader use of resilient, high-performing, and clonally-propagable germplasm in viticulture.

genetics↗

bioRxiv2026

Haplotype-specific expression of a terpene synthase underlies linalool variation in the grapevine cultivar Riesling

Grapevine cultivars vary widely in monoterpenoid content, yet the genetic and regulatory mechanisms underlying this variation remain poorly characterized beyond highly aromatic Muscat types. We profiled free volatiles and monoterpenoid glycosides in a Riesling x Cabernet Sauvignon F1 mapping population, revealing extensive variation and transgressive segregation consistent with multigenic control. QTL mapping identified 59 significant loci associated with 47 volatile compounds and monoterpene glycosides, including two major QTLs explaining 33.8% and 32.4% of phenotypic variance in (3S)-linalool accumulation. Integration of haplotype-resolved transcriptomics with metabolite data, enabled by a chromosome-scale diploid Riesling genome assembly, resolved a (3S)-linalool/nerolidol synthase cluster on chromosome 10 and identified VviTPS54 as the strongest candidate underlying linalool variation. VviTPS54 exhibited haplotype-specific expression strongly correlated with (3S)-linalool accumulation across genotypes, while no QTL was detected at the VviDXS1 locus and VviDXS1 expression was not correlated with terpene levels, indicating that regulatory variation within terpene synthase clusters, rather than methylerythritol phosphate (MEP) pathway flux, drives monoterpenoid composition in this population. These results establish regulatory variation of terpene synthases as a key mechanism underlying monoterpenoid diversity in grapevine and demonstrate that resolving such variation requires haplotype-phased genome assemblies coupled with haplotype-resolved transcriptomics to detect allele-specific expression differences at complex, heterozygous loci.

plant biology↗

bioRxiv2026

Disentangling blade and vasculature shape in grapevine leaves

The leaf blade and vasculature develop together within a shared morphological space. Despite shared molecular patterning pathways, it is unknown if developmental and evolutionary variation affect these tissues separately or together in a coordinated way. Grapevine leaves have a morphometric history and abundant data measuring the shape of the blade and vasculature together. Using a combination of topological data analysis and deep learning, we perform reciprocal semantic segmentation of leaf blade and vasculature. Each tissue contains sufficient information to predict the other. We hypothesize that this is due to a one-to-one relationship between blade and vein. Using thin plate splines to swap and warp different combinations of blade and vein shapes, we show that a set of leaves with a many-to-one relationship of blade and vein are distinguishable from true leaves. We also swap blade and vein across the developmental series and between species and show that only reversing the developmental series disrupts the relationship between blade and vasculature. We end by discussing the evolutionary and developmental implications that there is a unique, one-to-one mapping between blade and vein that allows each to be predicted from the other. Author summaryLeaves are made of two closely connected parts: the flat blade that captures light and the network of veins that transports water, nutrients, and developmental signals. Although these tissues grow together and share common molecular patterning pathways, it has remained unclear whether a particular blade shape is uniquely linked to a specific vein pattern. In this study, we use grapevine leaves as a model system and combine mathematical shape analysis with deep learning to examine this relationship. We show that the shape of the blade alone can accurately predict the vein network, and that the vein network can likewise predict the blade. This finding suggests a near one-to-one relationship between these two tissues. To test this idea, we created artificial leaves in which blade and vein shapes were deliberately mismatched. Although these synthetic leaves appeared realistic at a global level, a neural network was able to distinguish them from real leaves based on subtle differences. We further show that this tight coupling is maintained by the developmental sequence of leaf growth rather than by species identity, revealing a conserved constraint linking leaf form and internal structure.

plant biology↗

bioRxiv2025

Leveraging foundation models to dissect the genetic basis of cluster compactness and yield in grapevine

Grape cluster compactness is a key trait that influence fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling x Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

genetics↗

bioRxiv2025

Hyperspectral Sensing for High-Throughput Screening of Boron Tolerance in Grapevines

Boron (B) is an essential micronutrient for grapevine growth, yet excessive levels can impair photosynthesis, reduce yields, and diminish fruit quality. In this study, we evaluated the potential of hyperspectral radiometry combined with machine learning to identify B-tolerant rootstocks rapidly and cost-effectively. We screened both commercial grapevine rootstocks and wild Vitis germplasm under B treatments ranging from 0.5 to 8 ppm, measuring leaf B accumulation, stomatal conductance, photosystem II efficiency, and leaf reflectance (R380-R1100 nm). Our results revealed substantial genotypic variation in B exclusion, with some genotypes maintaining low leaf B content despite high external concentrations. Classification models (Partial Least Squares Discriminant Analysis and Random Forest classification) outperformed regression models (Partial Least Squares Regression and Random Forest regression) in distinguishing B-excluding genotypes, achieving moderate to high accuracy within just eight days after stress initiation. Vegetation indices such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), Structure Insensitive Pigment Index (SIPI), and Chlorophyll Index (CI) indicated that B stress reduces chlorophyll levels and may induce carotenoid accumulation, suggesting a photosynthetic tolerance mechanism. Although quantitative prediction of leaf B content proved more challenging, simulations showed that even modest prediction accuracies can substantially boost genetic gains if larger populations are screened, and selection intensities are increased. These findings underscore the value of hyperspectral radiometry for high-throughput phenotyping, allowing breeders to rapidly identify and advance B-tolerant rootstocks.

plant biology↗

bioRxiv2025

The Genetic Basis of Chloride Exclusion in Grapevines

Mediterranean regions are among the most important areas for global grape production, characterized by dry climates and frequent challenges associated with soil salinity. In these environments, chloride toxicity is a major factor limiting vine growth and fruit quality. Despite the critical role of chloride exclusion in salinity tolerance, the genetic mechanisms underlying this trait remain poorly understood. In this study, we leveraged one of the largest Vitis germplasm collections, comprising 335 accessions from 18 wild and cultivated Vitis species, to characterize natural variation in chloride exclusion. This diverse panel, which includes accessions from the southwestern United States and Mexico, captures a broad range of evolutionary adaptations to abiotic stress, providing an unprecedented opportunity to investigate the genetic basis of salinity tolerance. Using genome-wide association (GWA) and quantitative trait loci (QTL) mapping, we identified a major QTL on chromosome 8 containing candidate genes encoding cation/H exchangers (CHX), which are involved in ion transport and homeostasis. To validate these findings, we analyzed a mapping population derived from V. acerifolia longii 9018 and the commercial rootstock GRN3, confirming the chromosome 8 locus as a major determinant of chloride exclusion. Structural variant analysis revealed key non-synonymous substitutions within CHX genes, suggesting potential functional roles in salinity tolerance. Additionally, we discovered a novel QTL on chromosome 19 enriched with G-type lectin S-receptor-like serine/threonine-protein kinases, known regulators of stress signaling. By integrating extensive phenotypic and genomic data across a diverse Vitis collection, this study provides novel insights into the genetic architecture of chloride exclusion and identifies valuable candidate genes for breeding salt-tolerant rootstocks.

genetics↗

bioRxiv2024

Hyperspectral Sensing for High-Throughput Chloride Detection in Grapevines

Soil salinity affects major viticultural areas worldwide with chloride ions being the primary source of salt toxicity in grapevines. This toxicity impacts vine health and reduces fruit yield and quality. Current breeding efforts to improve grapevine salinity tolerance are limited by the low throughput of available phenotyping methods, which are time-consuming, labor-intensive, and destructive. This study demonstrated that hyperspectral proximal sensing can be utilized as a high-throughput, non-destructive screening technique to identify salinity-tolerant grapevine germplasm. The predictive abilities of two different hyperspectral devices, which varied in price, resolution, and sensitivity, were compared across 23 Vitis accessions spanning eight species. Prediction models were built using hyperspectral reflectance and leaf chloride content measured with a lab chloridometer. Three distinct approaches were studied: 1) analyzing the correlation between individual wavelengths and chloride content; 2) employing machine learning models, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machine (SVM), utilizing all wavelengths; and 3) classification-based prediction using Partial Least Squares Discriminant Analysis (PLSDA). Multiple regions in the spectrum, including 613-660 nm, 689-696 nm, and 1357-1358 nm, showed a medium correlation (0.30-0.50) with chloride content in the leaves. PLSR was the most effective machine learning approach, demonstrating moderate predictive capability for chloride content (maximum R{superscript 2} = 0.67), though performance varied between the two devices tested. With PLSDA, predictions increased considerably, up to an accuracy of 0.97, depending on the instrument used and the spectral data transformation. Overall, the more expensive and sensitive device with a wider spectral range outperformed the more affordable, shorter-range device. However, when the prediction model was based on classes (chloride excluders vs. non-excluders) rather than chloride content, the differences in prediction abilities were minimal, with both instruments performing very well. This is promising for identifying breeding materials with chloride exclusion capabilities at low cost and high throughput.

plant biology↗

bioRxiv2024

A high resolution model of the grapevine leaf morphospace predicts synthetic leaves

O_LIGrapevine leaves are a model morphometric system. Sampling over ten thousand leaves using dozens of landmarks, the genetic, developmental, and environmental basis of leaf shape has been studied and a morphospace for the genus Vitis predicted. Yet, these representations of leaf shape fail to capture the exquisite features of leaves at high resolution. C_LIO_LIWe measure the shapes of 139 grapevine leaves using 1672 pseudo-landmarks derived from 90 homologous landmarks with Procrustean approaches. From hand traces of the vasculature and blade, we have derived a method to automatically detect landmarks and place pseudo-landmarks that results in a high-resolution representation of grapevine leaf shape. Using polynomial models, we create continuous representations of leaf development in 10 Vitis spp. C_LIO_LIWe visualize a high-resolution morphospace in which genetic and developmental sources of leaf shape variance are orthogonal to each other. Using classifiers, V. vinifera, Vitis spp., rootstock and dissected leaf varieties as well as developmental stages are accurately predicted. Theoretical eigenleaf representations sampled from across the morphospace that we call synthetic leaves can be classified using models. C_LIO_LIBy predicting a high-resolution morphospace and delimiting the boundaries of leaf shapes that can plausibly be produced within the genus Vitis, we can sample synthetic leaves with realistic qualities. From an ampelographic perspective, larger numbers of leaves sampled at lower resolution can be projected onto this high-resolution space; or, synthetic leaves can be used to increase the robustness and accuracy of machine learning classifiers. C_LI Societal Impact StatementGrapevine leaves are emblematic of the strong visual associations people make with plants. At a glance, leaf shape is immediately recognizable, and it is because of this reason it is used to distinguish grape varieties. In an era of computationally-enabled, machine learning-derived representations of reality, we can revisit how we view and use the shapes and forms that plants display to understand our relationship with them. Using computational approaches combined with time-honored methods, we can predict theoretical leaves that are possible to understand the genetics, development, and environmental responses of plants in new ways.

plant biology↗

bioRxiv2023

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

genomics↗

bioRxiv2021

A B73 x Palomero Toluqueo mapping population reveals local adaptation in Mexican highland maize

Generations of farmer selection have produced a unique collection of traditional maize varieties adapted to the environmental challenges of the central Mexican highlands. In addition to agronomic and cultural value, Mexican highland maize represents a good system for the study of local adaptation and acquisition of adaptive phenotypes under cultivation. In this study, we characterized a recombinant inbred line population derived from the cross of the B73 reference line and the Mexican highland maize variety Palomero Toluqueno. Evaluation over multiple years in lowland and highland field sites in Mexico identified genomic regions linked to yield components and putatively adaptive morphological traits. A region on chromosome 7 associated with ear weight showed antagonistic allelic effects in lowland and highland fields, suggesting a trade-off consistent with local adaptation. We identified several alleles of highland origin associated with characteristic highland traits, including reduced tassel branching, increased stem pigmentation and the presence of stem macrohairs. The oligogenic architecture of characteristic morphological traits supports their role in adaptation, suggesting they have arisen from consistent directional selection acting at distinct points across the genome. We discuss these results in the context of the origin of phenotypic novelty during selection, commenting on the role of de novo mutation and the acquisition of adaptive variation by gene flow from endemic wild relatives.

plant biology↗

bioRxiv2019

Pacbio sequencing reveals identical organelle genomes between American cranberry (Vaccinium macrocarpon Ait.) and a wild relative

Breeding efforts in the American cranberry (Vaccinium macrocarpon Ait.), a North American perennial fruit crop of great importance, have been hampered by the limited genetic and phenotypic variability observed among cultivars and experimental materials. Most of the cultivars commercially used by cranberry growers today were derived from a few wild accessions bred in the 1950s. In different crops, wild germplasm has been used as an important genetic resource to incorporate novel traits and increase the phenotypic diversity of breeding materials. Vaccinium microcarpum (Turcz. ex Rupr.) Schmalh. and V. oxycoccos L., two closely related species, may be cross-compatible with the American cranberry and could be useful to improve fruit quality such as phytochemical content, and given their northern distribution, could also help develop cold hardy cultivars. Although these species have previously been analyzed in diversity studies, genomic characterization and comparative studies are still lacking. In this study, we sequenced and assembled the organelle genomes of the cultivated American cranberry and its wild relative, V. microcarpum. PacBio sequencing technology allowed us to assemble both mitochondrial and plastid genomes at very high coverage and in a single circular scaffold. A comparative analysis revealed that the mitochondrial genome sequences were identical between both species and that the plastids presented only two synonymous single nucleotide polymorphisms (SNPs). Moreover, Illumina resequencing of additional accessions of V. microcarpum and V. oxycoccos revealed high genetic variation in both species. Based on these results, we provided a hypothesis involving the extension and dynamics of the last glaciation period in North America, and how this could have shaped the distribution and dispersal of V. microcarpum. Finally, we provided important data regarding the polyploid origin of V. oxycoccos.

genomics↗

bioRxiv2019

Comprehensive analysis of the internal structure and firmness in American cranberry (Vaccinium macrocarpon Ait.) fruit

Cranberry (Vaccinium macrocarpon Ait.) fruit quality traits encompass many properties. Among these, fruit firmness has become a quality standard due to the recent demand increase for sweetened and dried cranberries (SDC). Traditionally, this trait has been measured by the cranberry industry using compression tests; however, it is poorly understood how fruit firmness is influenced by other characteristics. In this study, we developed a high-throughput computer-vision method to measure the internal structure of cranberry fruit, which may in turn influence cranberry fruit firmness. We measured the internal structure of 16 cranberry cultivars measured over a 40-day period. Internal structure data paired with fruit firmness values at each evaluation period allowed us to explore the correlations between firmness and internal morphological characteristics.

plant biology↗

bioRxiv2016

SOFIA: an R package for enhancing genetic visualization with Circos

Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Currently software such as Circos, ClicO FS, and RCircos, among others, provide tools for plotting a variety of genetic data types in a concise manner for data exploration and presentation. However, each of the programs have one or more disadvantages that limit their usability in data exploration or construction of publication quality figures, such as inflexibility in formatting and configuration, reduced image quality, lack of potential for automation, or requirements of high-level computational expertise. Therefore, we developed the R package SOFIA, which leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. The advantages of integrating both R and Circos into SOFIA are numerous. R is a very powerful, mid-level programming language widely used among the genetic and genomic research community, while Circos has proven to be a novel software for arranging genomic data to create aesthetical publication quality circular figures. Producing Circos figures in R with SOFIA is simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data in a single programming environment.

genetics↗

bioRxiv2016

Construction of a high-density American cranberry (Vaccinium macrocarpon Ait.) composite map using genotyping-by-sequencing for multi-pedigree linkage mapping

The American cranberry (Vaccinium macrocarpon Ait.) is a recently domesticated, economically important, fruit crop with limited molecular resources. New genetic resources could accelerate genetic gain in cranberry through characterization of its genomic structure and by enabling molecular-assisted breeding strategies. To increase the availability of cranberry genomic resources, genotyping-by-sequencing (GBS) was used to discover and genotype thousands of single nucleotide polymorphisms (SNPs) within three inter-related cranberry full-sib populations. Additional simple sequence repeat (SSR) loci were added to the SNP datasets and used to construct bin maps for the parents of the populations, which were then merged to create the first high-density cranberry composite map containing 6073 markers (5437 SNPs and 636 SSRs) on 12 linkage groups (LGs) spanning 1124 cM. Interestingly, higher rates of recombination were observed in maternal than paternal gametes. The large number of markers in common (mean of 57.3) and the high degree of observed collinearity (mean Pair-wise Spearman Rank Correlations > 0.99) between the LGs of the parental maps demonstrates the utility of GBS in cranberry for identifying polymorphic SNP loci that are transferable between pedigrees and populations in future trait-association studies. Furthermore, the high-density of markers anchored within the component maps allowed identification of segregation distortion regions, placement of centromeres on each of the 12 LGs, and anchoring of genomic scaffolds. Collectively, the results represent an important contribution to the current understanding of cranberry genomic structure and to the availability of molecular tools for future genetic research and breeding efforts in cranberry.

genomics↗