Performance analysis of conventional and AI-based variant callers using short and long reads.

BACKGROUND: The accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use. RESULTS: In this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons. CONCLUSION: This study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

Genome-wide analysis of cold imbibition stress in soybean, Glycine max.

In Canada, the length of the frost-free season necessitates planting crops as early as possible to ensure that the plants have enough time to reach full maturity before they are harvested. Early planting carries inherent risks of cold water imbibition (specifically less than 4°C) affecting seed germination. A marker dataset developed for a previously identified Canadian soybean GWAS panel was leveraged to investigate the effect of cold water imbibition on germination. Seed from a panel of 137 soybean elite cultivars, grown in the field at Ottawa, ON, over three years, were placed on filter paper in petri dishes and allowed to imbibe water for 16 hours at either 4°C or 20°C prior to being transferred to a constant 20°C. Observations on seed germination, defined as the presence of a 1 cm radicle, were done from day two to seven. A three-parameter exponential rise to a maximum equation (3PERM) was fitted to estimate germination, time to the one-half maximum germination, and germination uniformity for each cultivar. Genotype-by-sequencing was used to identify SNPs in 137 soybean lines, and using genome-wide association studies (GWAS - rMVP R package, with GLM, MLM, and FarmCPU as methods), haplotype block analysis, and assumed linkage blocks of ±100 kbp, a threshold for significance was established using the qvalue package in R, and five significant SNPs were identified on chromosomes 1, 3, 4, 6, and 13 for maximum germination after cold water imbibition. Percent of phenotypic variance explained (PVE) and allele substitution effect (ASE) eliminated two of the five candidate SNPs, leaving three QTL regions on chromosomes 3, 6, and 13 (Chr3-3419152, Chr6-5098454, and Chr13-29649544). Based on the gene ontology (GO) enrichment analysis, 14 candidate genes whose function is predicted to include germination and cold tolerance related pathways were identified as candidate genes. The identified QTLs can be used to select future soybean cultivars tolerant to cold water imbibition and mitigate risks associated with early soybean planting.

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean.

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives.

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

Optimization of in vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation of soybean for visual screening of transformants using RUBY.

In vitro and ex vitro Agrobacterium rhizogenes-mediated hairy root transformation (HRT) assays are key components of the plant biotechnology and functional genomics toolkit. In this report, both in vitro and ex vitro HRT were optimized in soybean using the RUBY reporter. Different parameters including A. rhizogenes strain, optical density of the bacterial cell culture (OD600), co-cultivation media, soybean genotype, explant age, and acetosyringone addition and concentration were evaluated. Overall, the in vitro assay was more efficient than the ex vitro assay in terms of the percentage of induction of hairy roots and transformed roots (expressing RUBY). Nonetheless, the ex vitro technique was deemed faster and a less complicated approach. The highest transformation of RUBY was observed on 7-d-old cotyledons of cv. Bert inoculated for 30 minutes with the R1000 resuspended in » B5 medium to OD600 (0.3) and 150 µM of acetosyringone. The parameters of this assay also led to the highest percentage of RUBY through two-step ex vitro hairy root transformation. Finally, using machine learning-based modeling, optimal protocols for both assays were further defined. This study establishes efficient and reliable hairy root transformation protocols applicable for functional studies in soybean.

Use of the Endoleak-to-Aortic Density Ratio to Distinguish Direct Endoleaks from Indirect Endoleaks after Endovascular Aortic Aneurysm Repair.

PURPOSE: To define criteria to distinguish direct (type 1 or 3) from indirect endoleaks (type 2) in the arterial phase of contrast-enhanced computed tomography (CT) scans in patients with abdominal aortic aneurysms treated with endovascular aortic repair. MATERIALS AND METHODS: This retrospective study was conducted from January 2009 to October 2020 and included consecutive patients treated endovascularly for a direct endoleak or an indirect endoleak associated with an enlarging aneurysm. The following characteristics were evaluated using contrast-enhanced CT: location, size, contact with the endograft, density, morphologic criteria, collateral artery enhancement, and endoleak-to-aortic density ratio. Statistical analysis included the Mann-Whitney U test, Pearson χ2 test, Fisher exact test, receiver operating characteristic curve analysis, and multivariable logistic regression. RESULTS: Contrast-enhanced CT scans from 71 patients (87% men), who presented with 87 endoleaks (44 indirect and 43 direct endoleaks), treated by endovascular techniques were analyzed. Using visual criteria, 56% of the endoleaks were not characterizable as direct or indirect. An endoleak-to-aortic density ratio of >0.77 could properly distinguish direct from indirect endoleaks, with a theoretical accuracy of 98% (area under the receiver operating characteristic curve, 0.99), sensitivity of 95%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 96%. CONCLUSION: An endoleak-to-aortic density ratio of >0.77 in the arterial phase of contrast-enhanced CT could be a strong discriminant of a direct-type endoleak.

The Genotypic Variability among Short-Season Soybean Cultivars for Nitrogen Fixation under Drought Stress.

Soybean fixes atmospheric nitrogen through the symbiotic rhizobia bacteria that inhabit root nodules. Drought stress negatively affect symbiotic nitrogen fixation (SNF) in soybean. The main objective of this study was to identify allelic variations associated with SNF in short-season Canadian soybean varieties under drought stress. A diversity panel of 103 early-maturity Canadian soybean varieties was evaluated under greenhouse conditions to determine SNF-related traits under drought stress. Drought was imposed after three weeks of plant growth, where plants were maintained at 30% field capacity (FC) (drought) and 80% FC (well-watered) until seed maturity. Under drought stress, soybean plants had lower seed yield, yield components, seed nitrogen content, % nitrogen derived from the atmosphere (%Ndfa), and total seed nitrogen fixed compared to those under well-watered conditions. Significant genotypic variability among soybean varieties was found for yield, yield parameters, and nitrogen fixation traits. A genome-wide association study (GWAS) was conducted using 2.16 M single nucleotide single nucleotide polymorphisms (SNPs) for different yield and nitrogen fixation related parameters for 30% FC and their relative performance (30% FC/80% FC). In total, five quantitative trait locus (QTL) regions, including candidate genes, were detected as significantly associated with %Ndfa under drought stress and relative performance. These genes can potentially aid in future breeding efforts to develop drought-resistant soybean varieties.

3D-GBS: a universal genotyping-by-sequencing approach for genomic selection and other high-throughput low-cost applications in species with small to medium-sized genomes.

Despite the increased efficiency of sequencing technologies and the development of reduced-representation sequencing (RRS) approaches allowing high-throughput sequencing (HTS) of multiplexed samples, the per-sample genotyping cost remains the most limiting factor in the context of large-scale studies. For example, in the context of genomic selection (GS), breeders need genome-wide markers to predict the breeding value of large cohorts of progenies, requiring the genotyping of thousands candidates. Here, we introduce 3D-GBS, an optimized GBS procedure, to provide an ultra-high-throughput and ultra-low-cost genotyping solution for species with small to medium-sized genome and illustrate its use in soybean. Using a combination of three restriction enzymes (PstI/NsiI/MspI), the portion of the genome that is captured was reduced fourfold (compared to a "standard" ApeKI-based protocol) while reducing the number of markers by only 40%. By better focusing the sequencing effort on limited set of restriction fragments, fourfold more samples can be genotyped at the same minimal depth of coverage. This GBS protocol also resulted in a lower proportion of missing data and provided a more uniform distribution of SNPs across the genome. Moreover, we investigated the optimal number of reads per sample needed to obtain an adequate number of markers for GS and QTL mapping (500-1000 markers per biparental cross). This optimization allows sequencing costs to be decreased by ~ 92% and ~ 86% for GS and QTL mapping studies, respectively, compared to previously published work. Overall, 3D-GBS represents a unique and affordable solution for applications requiring extremely high-throughput genotyping where cost remains the most limiting factor.

The construction of a high-density consensus genetic map for soybean based on SNP markers derived from genotyping-by-sequencing.

Genetic linkage maps are used to localize markers on the genome based on the recombination frequency. Most often, these maps are based on the segregation observed within a single biparental population of limited size (n < 300) where relatively few recombination events are sampled and in which some genomic regions are monomorphic because both parents carry the same alleles. Together, these two limitations affect both the resolution and extent of genome coverage of such maps. Consensus genetic maps overcome the limitations of individual genetic maps by merging the information from multiple segregating populations derived from a greater diversity of parental combinations, thus increasing the number of recombination events and reducing the number of monomorphic regions. The aim of this study was to construct a high-density consensus genetic map for single nucleotide polymorphism (SNP) markers obtained through a genotyping-by-sequencing (GBS) approach. Individual genetic maps were generated from six F4:5 mapping populations (n = 278-365), totaling 1857 individuals. The six linkage maps were then merged to produce a consensus map comprising a total of 16 311 mapped SNPs that jointly cover 99.5% of the soybean genome with only two gaps larger than 10 cM. Compared to previous soybean consensus maps, it offers a more extensive and uniform coverage.

The SoyaGen Project: Putting Genomics to Work for Soybean Breeders.

The SoyaGen project was a collaborative endeavor involving Canadian soybean researchers and breeders from academia and the private sector as well as international collaborators. Its aims were to develop genomics-derived solutions to real-world challenges faced by breeders. Based on the needs expressed by the stakeholders, the research efforts were focused on maximizing realized yield through optimization of maturity and improved disease resistance. The main deliverables related to molecular breeding in soybean will be reviewed here. These include: (1) SNP datasets capturing the genetic diversity within cultivated soybean (both within a worldwide collection of > 1,000 soybean accessions and a subset of 102 short-season accessions (MG0 and earlier) directly relevant to this group); (2) SNP markers for selecting favorable alleles at key maturity genes as well as loci associated with increased resistance to key pathogens and pests (Phytophthora sojae, Heterodera glycines, Sclerotinia sclerotiorum); (3) diagnostic tools to facilitate the identification and mapping of specific pathotypes of P. sojae; and (4) a genomic prediction approach to identify the most promising combinations of parents. As a result of this fruitful collaboration, breeders have gained new tools and approaches to implement molecular, genomics-informed breeding strategies. We believe these tools and approaches are broadly applicable to soybean breeding efforts around the world.

Designing a Genome-Wide Association Study: Main Steps and Critical Decisions.

In this introductory chapter, we seek to provide the reader with a high-level overview of what goes into designing a genome-wide association study (GWAS) in the context of crop plants. After introducing some general concepts regarding GWAS, we divide the contents of this overview into four main sections that reflect the key components of a GWAS: assembly and phenotyping of an association panel, genotyping, association analysis and candidate gene identification. These sections largely reflect the structure of the chapters which follow later in the book, and which provide detailed discussions of these various steps. In each section, in addition to providing external references from the literature, we also often refer the reader to the appropriate chapters in this book in which they can further explore a topic. We close by summarizing some of the key questions that a prospective user of GWAS should answer prior to undertaking this type of experiment.

Genome-Wide Association Study Statistical Models: A Review.

Statistical models are at the core of the genome-wide association study (GWAS). In this chapter, we provide an overview of single- and multilocus statistical models, Bayesian, and machine learning approaches for association studies in plants. These models are discussed based on their basic methodology, cofactors adjustment accounted for, statistical power and computational efficiency. New statistical models and machine learning algorithms are both showing improved performance in detecting missed signals, rare mutations and prioritizing causal genetic variants; nevertheless, further optimization and validation studies are required to maximize the power of GWAS.

CRISPR/Cas9 in Planta Hairy Root Transformation: A Powerful Platform for Functional Analysis of Root Traits in Soybean.

Sequence and expression data obtained by next-generation sequencing (NGS)-based forward genetics methods often allow the identification of candidate causal genes. To provide true experimental evidence of a gene's function, reverse genetics techniques are highly valuable. Site-directed mutagenesis through transfer DNA (T-DNA) delivery is an efficient reverse screen method in plant functional analysis. Precise modification of targeted crop genome sequences is possible through the stable and/or transient delivery of clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (CRISPR/Cas) reagents. Currently, CRISPR/Cas9 is the most powerful reverse genetics approach for fast and precise functional analysis of candidate genes/mutations of interest. Rapid and large-scale analyses of CRISPR/Cas-induced mutagenesis is achievable through Agrobacterium rhizogenes-mediated hairy root transformation. The combination of A. rhizogenes hairy root-CRISPR/Cas provides an extraordinary platform for rapid, precise, easy, and cost-effective "in root" functional analysis of genes of interest in legume plants, including soybean. Both hairy root transformation and CRISPR/Cas9 techniques have their own complexities and considerations. Here, we discuss recent advancements in soybean hairy root transformation and CRISPR/Cas9 techniques. We highlight the critical factors required to enhance mutation induction and hairy root transformation, including the new generation of reporter genes, methods of Agrobacterium infection, accurate gRNA design strategies, Cas9 variants, gene regulatory elements of gRNAs and Cas9 nuclease cassettes and their configuration in the final binary vector to study genes involved in root-related traits in soybean.

Combined use of Oxford Nanopore and Illumina sequencing yields insights into soybean structural variation biology.

BACKGROUND: Structural variants (SVs), including deletions, insertions, duplications, and inversions, are relatively long genomic variations implicated in a diverse range of processes from human disease to ecology and evolution. Given their complex signatures, tendency to occur in repeated regions, and large size, discovering SVs based on short reads is challenging compared to single-nucleotide variants. The increasing availability of long-read technologies has greatly facilitated SV discovery; however, these technologies remain too costly to apply routinely to population-level studies. Here, we combined short-read and long-read sequencing technologies to provide a comprehensive population-scale assessment of structural variation in a panel of Canadian soybean cultivars. RESULTS: We used Oxford Nanopore long-read sequencing data (~12× mean coverage) for 17 samples to both benchmark SV calls made from Illumina short-read data and predict SVs that were subsequently genotyped in a population of 102 samples using Illumina data. Benchmarking results show that variants discovered using Oxford Nanopore can be accurately genotyped from the Illumina data. We first use the genotyped deletions and insertions for population genetics analyses and show that results are comparable to those based on single-nucleotide variants. We observe that the population frequency and distribution within the genome of deletions and insertions are constrained by the location of genes. Gene Ontology and PFAM domain enrichment analyses also confirm previous reports that genes harboring high-frequency deletions and insertions are enriched for functions in defense response. Finally, we discover polymorphic transposable elements from the deletions and insertions and report evidence of the recent activity of a Stowaway MITE. CONCLUSIONS: We show that structural variants discovered using Oxford Nanopore data can be genotyped with high accuracy from Illumina data. Our results demonstrate that long-read and short-read sequencing technologies can be efficiently combined to enhance SV analysis in large populations, providing a reusable framework for their study in a wider range of samples and non-model species.

RXLR effector gene Avr3a from Phytophthora sojae is recognized by Rps8 in soybean.

The use of resistance genes in elite soybean cultivars is one of the most widely used methods to manage Phytophthora sojae. This method relies on effector-triggered immunity, where a Resistant to P. sojae (Rps) gene product from the plant recognizes a specific effector from the pathogen, encoded by an avirulence (Avr) gene. Many Avr genes from P. sojae have been identified in the last decade, allowing a better exploitation of this type of resistance. The objective of the present study was to identify the Avr gene triggering immunity derived from the soybean resistance gene Rps8. The analysis of a segregating F2 progeny coupled with a genotyping-by-sequencing approach led to the identification of a putative Avr8 locus. The investigation of this locus using whole-genome sequencing data from 31 isolates of P. sojae identified Avr3a as the likely candidate for Avr8. Long-read sequencing also revealed that P. sojae isolates can carry up to five copies of the Avr3a gene, compared to the four previously reported. Haplotype and transcriptional analyses showed that amino acid changes and absence of Avr3a transcripts from P. sojae isolates caused changes in virulence towards Rps8. Functional analyses using CRISPR/Cas9 knockout and constitutive expression demonstrated that Rps8 interacted with Avr3a. We also showed that a specific allele of Avr3a is recognized by Rps3a but not Rps8. While Rps3a and Rps8 have been previously described as closely linked, this is the first report of a clear distinction hitherto undefined between these two resistance genes.

A high-resolution consensus linkage map for barley based on GBS-derived genotypes.

As genotyping-by-sequencing (GBS) is widely used in barley genetic studies, the translation of the physical position of GBS-derived SNPs into accurate genetic positions has become relevant. The main aim of this study was to develop a high-resolution consensus linkage map based on GBS-derived SNPs. The construction of this integrated map involved 11 bi-parental populations composed of 3743 segregating progenies. We adopted a uniform set of SNP-calling and filtering conditions to identify 50 875 distinct SNPs segregating in at least one population. These SNPs were grouped into 18 580 non-redundant SNPs (bins). The resulting consensus linkage map spanned 1050.1 cM, providing an average density of 17.7 bins and 48.4 SNPs per cM. The consensus map is characterized by the absence of large intervals devoid of marker coverage (significant gaps), the largest interval between bins was only 3.7 cM and the mean distance between adjacent bins was 0.06 cM. This high-resolution linkage map will contribute to several applications in genomic research, such as providing useful information on the recombination landscape for QTLs/genes identified via GWAS or ensuring a uniform distribution of SNPs when developing low-cost genotyping tools offering a limited number of markers.

Mapping of partial resistance to Phytophthora sojae in soybean PIs using whole-genome sequencing reveals a major QTL.

In the last decade, more than 70 quantitative trait loci (QTL) related to soybean [Glycine max (L.) Merr.] partial resistance (PR) against Phytophthora sojae have been identified by genome-wide association studies (GWAS). However, most of them have either a minor effect on the resistance level or are specific to a single phenotypic variable or one isolate, thereby limiting their use in breeding programs. In this study, we have used an analytical approach combining (a) the phenotypic characterization of a diverse panel of 357 soybean accessions for resistance to P. sojae captured through a single variable, corrected dry weight; (b) a new hydroponic assay allowing the inoculation of a combination of P. sojae isolates covering the spectrum of commercially relevant Rps genes; and (c) exhaustive genotyping through whole-genome resequencing (WGS). This led to the identification of a novel P. sojae resistance QTL with a relatively major effect compared with the previously reported QTL. The QTL interval, spanning ∼500 kb on chromosome (Chr) 15, does not colocalize with previously reported QTL for P. sojae resistance. Plants carrying the favorable allele at this QTL were 60% more resistant. Eight genes were found to reside in the linkage disequilibrium (LD) block containing the peak single-nucleotide polymorphism (SNP) including Glyma.15G217100, which encodes a major latex protein (MLP)-like protein, with a functional annotation related to pathogen resistance. Expression analysis of Glyma.15G217100 indicated that it was nearly eight times more highly expressed in a group of plant introductions (PIs) carrying the resistant (R) allele compared with those carrying the susceptible (S) allele within a short period after inoculation. These results offer new and valuable options to develop improved soybean cultivars with broad resistance to P. sojae through marker-assisted selection.

GWAS identifies an ortholog of the rice D11 gene as a candidate gene for grain size in an international collection of hexaploid wheat.

Grain size is a key agronomic trait that contributes to grain yield in hexaploid wheat. Grain length and width were evaluated in an international collection of 157 wheat accessions. These accessions were genetically characterized using a genotyping-by-sequencing (GBS) protocol that produced 73,784 single nucleotide polymorphism (SNP) markers. GBS-derived genotype calls obtained on Chinese Spring proved extremely accurate when compared to the reference (> 99.9%) and showed > 95% agreement with calls made at SNP loci shared with the 90 K SNP array on a subset of 71 Canadian wheat accessions for which both types of data were available. This indicates that GBS can yield a large amount of highly accurate SNP data in hexaploid wheat. The genetic diversity analysis performed using this set of SNP markers revealed the presence of six distinct groups within this collection. A GWAS was conducted to uncover genomic regions controlling variation for grain length and width. In total, seven SNPs were found to be associated with one or both traits, identifying three quantitative trait loci (QTLs) located on chromosomes 1D, 2D and 4A. In the vicinity of the peak SNP on chromosome 2D, we found a promising candidate gene (TraesCS2D01G331100), whose rice ortholog (D11) had previously been reported to be involved in the regulation of grain size. These markers will be useful in breeding for enhanced wheat productivity.

Paclitaxel and mortality in patients with claudication and de novo femoropopliteal lesions: a historical cohort study.

OBJECTIVE: To compare the mortality rates of patients with claudication and de novo femoropopliteal lesions treated with and without paclitaxel coated devices (PCD). BACKGROUND: A recent meta-analysis, mostly including patients with claudication and de novo femoropopliteal lesions but also with recurrent stenoses and critical limb ischemia, has shown a significant excess mortality in patients treated with PCD. METHODS: Comparison of two historical cohorts of patients presenting with claudication and de novo femoropopliteal lesions treated with and without PCD between 2008 and 2018. RESULTS: After review of 5219 arteriograms in patients presenting with peripheral artery disease, 700 consecutive patients were included consisting in 72.6% of male (n = 508). Mean age was 68.1 ± 8.5 years. 45.7% of the patients (n = 320) had a treatment including a PCD. Mean femoropopliteal lesion length was 123 ± 91 mm including 44.6% of occlusions. Patients of the control group were censored at crossover to paclitaxel when applicable. Mortality rates at 1, 2 and 5 years were 4.6%, 7.5%, 19.4% and 1.6%, 6.2%, 16.6% in the non-PCD and PCD groups respectively. The relative risks of death when using PCD were 0.35 (p = 0.03), 0.83 (p = NS) and 0.86 (p = NS) at 1, 2 and 5 years respectively. CONCLUSION: There was no excess mortality in patients with claudication and de novo femoropopliteal lesions treated with paclitaxel coated devices at 1, 2 and 5 years of follow-up in this cohort. The current study suggests that additional prospective randomized studies properly powered to study mortality are necessary.

The pan-genome of the cultivated soybean (PanSoy) reveals an extraordinarily conserved gene content.

Studies on structural variation in plants have revealed the inadequacy of a single reference genome for an entire species and suggest that it is necessary to build a species-representative genome called a pan-genome to better capture the extent of both structural and nucleotide variation. Here, we present a pan-genome of cultivated soybean (Glycine max), termed PanSoy, constructed using the de novo genome assembly of 204 phylogenetically and geographically representative improved accessions selected from the larger GmHapMap collection. PanSoy uncovers 108 Mb (˜11%) of novel nonreference sequences encompassing 3621 protein-coding genes (including 1659 novel genes) absent from the soybean 'Williams 82' reference genome. Nonetheless, the core genome represents an exceptionally large proportion of the genome, with >90.6% of genes being shared by >99% of the accessions. A majority of PAVs encompassing genes could be confirmed with long-read sequencing on a subset of accessions. The PanSoy is a major step towards capturing the extent of genetic variation in cultivated soybean and provides a resource for soybean genomics research and breeding.