Assessment of genetic diversity and population structure in cultured Australian Pacific oysters

The recent development of Pacific oyster (Crassostrea gigas) SNP genotyping arrays has allowed detailed characterisation of genetic diversity and population structure within and between oyster populations. It also raises the potential of harnessing genomic selection for genetic improvement in oyster breeding programmes. The aim of this study was to characterise a breeding population of Australian oysters through genotyping and analysis of 18 027 SNPs, followed by comparison with genotypes of oyster sampled from Europe and Asia. This revealed that the Australian populations had similar population diversity (H_E) to oysters from New Zealand, the British Isles, France and Japan. Population divergence was assessed using PCA of genetic distance and revealed that Australian oysters were distinct from all other populations tested. Australian Pacific oysters originate from planned introductions sourced from three Japanese populations. Approximately 95% of these introductions were from geographically, and potentially genetically, distinct populations from the Nagasaki oysters assessed in this study. Finally, in preparation for the application of genomic selection in oyster breeding programmes, the strength of LD was evaluated and subsets of loci were tested for their ability to accurately infer relationships. Weak LD was observed on average; however, SNP subsets were shown to accurately reconstitute a genomic relationship matrix constructed using all loci. This suggests that low‐density SNP panels may have utility in the Australian population tested, and the findings represent an important first step towards the design and implementation of genomic approaches for applied breeding in Pacific oysters.     

Interaction between toxic dinoflagellate Alexandrium catenella exposure and disease associated with herpesvirus OsHV-1 μVar in Pacific oyster spat Crassostrea gigas

Blooms of toxic dinoflagellates can co-occur with mass mortality events associated with herpesvirus OsHV-1 μVar infection that have been decimating Pacific oyster Crassostrea gigas spat and juveniles every summer since 2008 in France. This study investigated the possible effect of a harmful dinoflagellate, Alexandrium catenella, a producer of Paralytic Shellfish Toxins (PSTs), upon the oyster spat–herpesvirus interaction. Oyster spat from a hatchery were challenged by cohabitation with oysters contaminated in the field with OsHV-1 μVar and possibly other pathogens. Simultaneously, the oysters were exposed to cultured A. catenella. Infection with OsHV-1 μVar and PST accumulation were measured after 4 days of experimental exposure.Exposure to Alexandrium catenella modified the host–pathogen interaction by reducing prevalence of OsHV-1 μVar infection. In addition, oysters challenged with OsHV-1 μVar and possibly other pathogens from the environment accumulated smaller amounts of PSTs than unchallenged oysters. Three possible mechanisms are suggested by these results: (i) possible direct interactions between A. catenella and herpesvirus (or associated pathogens) could reduce viral transmission and algal availability for oyster consumption; (ii) oyster feeding behavior or digestive functions may have been altered, thus decreasing both uptake of viral particles and consumption or digestion of toxic algae and consequent toxin accumulation; (iii) immuno-activation by A. catenella could enhance defense efficiency against OsHV-1 μVar infection. These findings suggest further research on relationships between OsHV-1 μVar and toxic dinoflagellates and their combined effects upon disease transmission and proliferation processes, as well as on oyster physiological and immunological involvement in this complex, tripartite interaction.     

Ostreid Herpesvirus 1 Infection among Pacific Oyster (Crassostrea gigas) Spat: Relevance of Water Temperature to Virus Replication and Circulation Prior to the Onset of Mortality

A number of bivalve species worldwide, including the Pacific oyster, Crassostrea gigas, have been affected by mass mortality events associated with herpesviruses, resulting in significant losses. A particular herpesvirus was purified from naturally infected larval Pacific oysters, and its genome was completely sequenced. This virus has been classified as Ostreid herpesvirus 1 (OsHV-1) within the family Malacoherpesviridae. Since 2008, mass mortality outbreaks among C. gigas in Europe have been related to the detection of a variant of OsHV-1 called μVar. Additional data are necessary to better describe mortality events in relation to environmental-parameter fluctuations and OsHV-1 detection. For this purpose, a single batch of Pacific oyster spat was deployed in 4 different locations in the Marennes-Oleron area (France): an oyster pond (“claire”), a shellfish nursery, and two locations in the field. Mortality rates were recorded based on regular observation, and samples were collected to search for and quantify OsHV-1 DNA by real-time PCR. Although similar massive mortality rates were reported at the 4 sites, mortality was detected earlier in the pond and in the nursery than at both field sites. This difference may be related to earlier increases in water temperature. Mass mortality was observed among oysters a few days after increases in the number of PCR-positive oysters and viral-DNA amounts were recorded. An initial increment in the number of PCR-positive oysters was reported at both field sites during the survey in the absence of significant mortality. During this period, the water temperature was below 16°C.     

Genomic selection for fruit quality traits in apple (Malus×domestica Borkh.)

The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r(2)?=?0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits.     

New Insight for the Genetic Evaluation of Resistance to Ostreid Herpesvirus Infection,a Worldwide Disease,in Crassostrea gigas

The Pacific oyster, Crassostrea gigas, is the most important commercial oyster species cultivated in the world. Meanwhile, the ostreid herpesvirus 1 (OsHV-1) is one of the major pathogens affecting the Pacific oyster, and numerous mortality outbreaks related to this pathogen are now reported worldwide. To assess the genetic basis of resistance to OsHV-1 infection in spat C. gigas and to facilitate breeding programs for such a trait, if any exist, we compared the mortality of half- and full-sib families using three field methods and a controlled challenge by OsHV-1 in the laboratory. In the field, three methods were tested: (A) one family per bag; (B) one family per small soft mesh bag and all families inside one bag; (C) same as the previous methods but the oysters were individually labelled and then mixed. The mean mortality ranged from 80 to 82% and was related to OsHV-1 based on viral DNA detection. The narrow-sense heritability for mortality, and thus OsHV-1 resistance, ranged from 0.49 to 0.60. The high positive genetic correlations across the field methods suggested no genotype by environment interaction. Ideally, selective breeding could use method B, which is less time- and space-consuming. The narrow sense heritability for mortality under OsHV-1 challenge was 0.61, and genetic correlation between the field and the laboratory was ranged from 0.68 to 0.75, suggesting a weak genotype by environment interaction. Thus, most of families showing the highest survival performed well in field and laboratory conditions, and a similar trend was also observed for families with the lowest survival. In conclusion, this is the first study demonstrating a large additive genetic variation for resistance to OsHV-1 infection in C. gigas, regardless of the methods used, which should help in selective breeding to improve resistance to viral infection in C. gigas.     

Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods

Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped. We aim to develop a new GP-based breeding system that can improve the efficiency of our cotton breeding program. This article presents a GP study on cotton fibre quality and yield traits using 1385 breeding lines from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) cotton breeding program which were genotyped using a high-density SNP chip that generated 12,296 informative SNPs. The aim of this study was twofold: (1) to identify the models and data sources (i.e. genomic and pedigree) that produce the highest prediction accuracies; and (2) to assess the effectiveness of GP as a selection tool in the CSIRO cotton breeding program. The prediction analyses were conducted under various scenarios using different Bayesian predictive models. Results highlighted that the model combining genomic and pedigree information resulted in the best cross validated prediction accuracies: 0.76 for fibre length, 0.65 for fibre strength, and 0.64 for lint yield. Overall, this work represents the largest scale genomic selection studies based on cotton breeding trial data. Prediction accuracies reported in our study indicate the potential of GP as a breeding tool for cotton. The study highlighted the importance of incorporating pedigree and environmental factors in GP models to optimise the prediction performance.Subject terms: Plant breeding, Genome     

Genomic prediction in CIMMYT maize and wheat breeding programs

Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center''s (CIMMYT''s) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT''s maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.     

Practical application of genomic selection in a doubled-haploid winter wheat breeding program

Crop improvement is a long-term, expensive institutional endeavor. Genomic selection (GS), which uses single nucleotide polymorphism (SNP) information to estimate genomic breeding values, has proven efficient to increasing genetic gain by accelerating the breeding process in animal breeding programs. As for crop improvement, with few exceptions, GS applicability remains in the evaluation of algorithm performance. In this study, we examined factors related to GS applicability in line development stage for grain yield using a hard red winter wheat (Triticum aestivum L.) doubled-haploid population. The performance of GS was evaluated in two consecutive years to predict grain yield. In general, the semi-parametric reproducing kernel Hilbert space prediction algorithm outperformed parametric genomic best linear unbiased prediction. For both parametric and semi-parametric algorithms, an upward bias in predictability was apparent in within-year cross-validation, suggesting the prerequisite of cross-year validation for a more reliable prediction. Adjusting the training population’s phenotype for genotype by environment effect had a positive impact on GS model’s predictive ability. Possibly due to marker redundancy, a selected subset of SNPs at an absolute pairwise correlation coefficient threshold value of 0.4 produced comparable results and reduced the computational burden of considering the full SNP set. Finally, in the context of an ongoing breeding and selection effort, the present study has provided a measure of confidence based on the deviation of line selection from GS results, supporting the implementation of GS in wheat variety development.     

Genome‐wide association and genomic prediction for biomass yield in a genetically diverse Miscanthus sinensis germplasm panel phenotyped at five locations in Asia and North America

To improve the efficiency of breeding of Miscanthus for biomass yield, there is a need to develop genomics‐assisted selection for this long‐lived perennial crop by relating genotype to phenotype and breeding value across a broad range of environments. We present the first genome‐wide association (GWA) and genomic prediction study of Miscanthus that utilizes multilocation phenotypic data. A panel of 568 Miscanthus sinensis accessions was genotyped with 46,177 single nucleotide polymorphisms (SNPs) and evaluated at one subtropical and five temperate locations over 3 years for biomass yield and 14 yield‐component traits. GWA and genomic prediction were performed separately for different years of data in order to assess reproducibility. The analyses were also performed for individual field trial locations, as well as combined phenotypic data across groups of locations. GWA analyses identified 27 significant SNPs for yield, and a total of 504 associations across 298 unique SNPs across all traits, sites, and years. For yield, the greatest number of significant SNPs was identified by combining phenotypic data across all six locations. For some of the other yield‐component traits, greater numbers of significant SNPs were obtained from single site data, although the number of significant SNPs varied greatly from site to site. Candidate genes were identified. Accounting for population structure, genomic prediction accuracies for biomass yield ranged from 0.31 to 0.35 across five northern sites and from 0.13 to 0.18 for the subtropical location, depending on the estimation method. Genomic prediction accuracies of all traits were similar for single‐location and multilocation data, suggesting that genomic selection will be useful for breeding broadly adapted M. sinensis as well as M. sinensis optimized for specific climates. All of our data, including DNA sequences flanking each SNP, are publicly available. By facilitating genomic selection in M. sinensis and Miscanthus × giganteus, our results will accelerate the breeding of these species for biomass in diverse environments.     

Development of a high-throughput SNP array for sea cucumber (Apostichopus japonicus) and its application in genomic selection with MCP regularized deep neural networks

High-throughput single nucleotide polymorphism (SNP) genotyping assays are powerful tools for genetic studies and genomic breeding applications for many species. Though large numbers of SNPs have been identified in sea cucumber (Apostichopus japonicus), but, as yet, no high-throughput genotyping platform is available for this species. In this study, we designed and developed a high-throughput 24 K SNP genotyping array named HaishenSNP24K for A. japonicus, based on the multi-objective-local optimization (MOLO) algorithm and HD-Marker genotyping method. The SNP array exhibited a relatively high genotyping call rate (> 96%), genotyping accuracy (>95%) and exhibited highly polymorphic in sea cucumber populations. In addition, we also assessed its application in genomic selection (GS). Deep neural networks (DNN) that can capture the complicated interactions of genes have been proposed as a promising tool in GS for SNP-based genomic prediction of complex traits in animal breeding. To overcome the problem of over-fitting when using the HaishenSNP24K array as high-dimensional DNN input, we developed minmax concave penalty (MCP) regularization for sparse deep neural networks (DNN-MCP) that finds an optimal sparse structure of a DNN by minimizing the square error subject to the non-convex penalty MCP on the parameters (weights and biases). Compared to two linear models, namely RR-GBLUP and Bayes B, and the nonlinear model DNN, DNN-MCP has greatly improved the genomic prediction ability for three quantitative traits (e.g., wet weight, dry weight and survival time) in the sea cucumber population. To the best of our knowledge, this is the first work to develop a high-throughput SNP array for A. japonicus and a new model DNN-MCP for genomic prediction of complex traits in GS. The present results provide evidence that supports the HaishenSNP24K array with DNN-MCP will be valuable for genetic studies and molecular breeding in A. japonicus.     

A new genomic tool for walnut (Juglans regia L.): development and validation of the high‐density Axiom™ J. regia 700K SNP genotyping array

Over the last 20 years, global production of Persian walnut (Juglans regia L.) has grown enormously, likely reflecting increased consumption due to its numerous benefits to human health. However, advances in genome‐wide association (GWA) studies and genomic selection (GS) for agronomically important traits in walnut remain limited due to the lack of powerful genomic tools. Here, we present the development and validation of a high‐density 700K single nucleotide polymorphism (SNP) array in Persian walnut. Over 609K high‐quality SNPs have been thoroughly selected from a set of 9.6 m genome‐wide variants, previously identified from the high‐depth re‐sequencing of 27 founders of the Walnut Improvement Program (WIP) of University of California, Davis. To validate the effectiveness of the array, we genotyped a collection of 1284 walnut trees, including 1167 progeny of 48 WIP families and 26 walnut cultivars. More than half of the SNPs (55.7%) fell in the highest quality class of ‘Poly High Resolution’ (PHR) polymorphisms, which were used to assess the WIP pedigree integrity. We identified 151 new parent‐offspring relationships, all confirmed with the Mendelian inheritance test. In addition, we explored the genetic variability among cultivars of different origin, revealing how the varieties from Europe and California were differentiated from Asian accessions. Both the reconstruction of the WIP pedigree and population structure analysis confirmed the effectiveness of the Applied Biosystems? Axiom? J. regia 700K SNP array, which initiates a novel genomic and advanced phase in walnut genetics and breeding.     

Genetic Mapping and QTL Analysis of Growth-Related Traits in the Pacific Oyster

The Pacific oyster (Crassostrea gigas) is one of the most important oysters cultured worldwide. To analyze the oyster genome and dissect growth-related traits, we constructed a sex-averaged linkage map by combining 64 genomic simple sequence repeats, 42 expressed sequence tag-derived SSRs, and 320 amplified fragment length polymorphism markers in an F₁ full-sib family. A total of 426 markers were assigned to 11 linkage groups, spanning 558.2 cM with an average interval of 1.3 cM and 94.7% of genome coverage. Segregation distortion was significant for 18.8% of the markers (P < 0.05), and distorted markers tended to occur on some genetic regions or linkage groups. Most growth-related quantitative traits were highly significantly (P < 0.01) correlated, and principal component analysis obtained four principal components. Quantitative trait locus (QTL) analysis identified three significant QTLs for two principal components, which explained 0.6–13.9% of the phenotypic variation. One QTL for sex was detected on linkage group 6, and the inheritabilities of sex for parental alleles and maternal alleles on that locus C15 are 39.8% and 0.01%, respectively. The constructed linkage map and determined QTLs can provide a tool for further genetic analysis of the traits and be potential for marker-assisted selection in C. gigas breeding.     

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical P<0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167–0.198) and BLUP (0.105–0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.     

Ontogeny and water temperature influences the antiviral response of the Pacific oyster,Crassostrea gigas

Disease is caused by a complex interaction between the pathogen, environment, and the physiological status of the host. Determining how host ontogeny interacts with water temperature to influence the antiviral response of the Pacific oysters, Crassostrea gigas, is a major goal in understanding why juvenile Pacific oysters are dying during summer as a result of the global emergence of a new genotype of the Ostreid herpesvirus, termed OsHV-1 μvar. We measured the effect of temperature (12 vs 22 °C) on the antiviral response of adult and juvenile C. gigas injected with poly I:C. Poly I:C up-regulated the expression of numerous immune genes, including TLR, MyD88, IκB-1, Rel, IRF, MDA5, STING, SOC, PKR, Viperin and Mpeg1. At 22 °C, these immune genes showed significant up-regulation in juvenile and adult oysters, but the majority of these genes were up-regulated 12 h post-injection for juveniles compared to 26 h for adults. At 12 °C, the response of these genes was completely inhibited in juveniles and delayed in adults. Temperature and age had no effect on hemolymph antiviral activity against herpes simplex virus (HSV-1). These results suggest that oysters rely on a cellular response to minimise viral replication, involving recognition of virus-associated molecular patterns to induce host cells into an antiviral state, as opposed to producing broad-spectrum antiviral compounds. This cellular response, measured by antiviral gene expression of circulating hemocytes, was influenced by temperature and oyster age. We speculate whether the vigorous antiviral response of juveniles at 22 °C results in an immune-mediated disorder causing mortality.     

Accuracy of genomic selection models in a large population of open-pollinated families in white spruce

Genomic selection (GS) is of interest in breeding because of its potential for predicting the genetic value of individuals and increasing genetic gains per unit of time. To date, very few studies have reported empirical results of GS potential in the context of large population sizes and long breeding cycles such as for boreal trees. In this study, we assessed the effectiveness of marker-aided selection in an undomesticated white spruce (Picea glauca (Moench) Voss) population of large effective size using a GS approach. A discovery population of 1694 trees representative of 214 open-pollinated families from 43 natural populations was phenotyped for 12 wood and growth traits and genotyped for 6385 single-nucleotide polymorphisms (SNPs) mined in 2660 gene sequences. GS models were built to predict estimated breeding values using all the available SNPs or SNP subsets of the largest absolute effects, and they were validated using various cross-validation schemes. The accuracy of genomic estimated breeding values (GEBVs) varied from 0.327 to 0.435 when the training and the validation data sets shared half-sibs that were on average 90% of the accuracies achieved through traditionally estimated breeding values. The trend was also the same for validation across sites. As expected, the accuracy of GEBVs obtained after cross-validation with individuals of unknown relatedness was lower with about half of the accuracy achieved when half-sibs were present. We showed that with the marker densities used in the current study, predictions with low to moderate accuracy could be obtained within a large undomesticated population of related individuals, potentially resulting in larger gains per unit of time with GS than with the traditional approach.     

Temperature,energy metabolism,and adaptive divergence in two oyster subspecies

Comparisons of related species that have diverse spatial distributions provide an efficient way to investigate adaptive evolution in face of increasing global warming. The oyster subjected to high environmental selections is a model species as sessile marine invertebrate. This study aimed to detect the adaptive divergence of energy metabolism in two oyster subspecies from the genus Crassostrea—C. gigas gigas and C. gigas angulata—which are broadly distributed along the northern and southern coasts of China, respectively. We examined the effects of acute thermal stress on energy metabolism in two oyster subspecies after being common gardened for one generation in identical conditions. Thermal responses were assessed by incorporating physiological, molecular, and genomic approaches. Southern oysters exhibited higher fluctuations in metabolic rate, activities of key energetic enzymes, and levels of thermally induced gene expression than northern oysters. For genes involved in energy metabolism, the former displayed higher basal levels of gene expression and a more pronounced downregulation of thermally induced expression, while the later exhibited lower basal levels and a less pronounced downregulation of gene expression. Contrary expression pattern was observed in oxidative stress gene. Besides, energy metabolic tradeoffs were detected in both subspecies. Furthermore, the genetic divergence of a nonsynonymous SNP (SOD‐132) and five synonymous SNPs in other genes was identified and validated in these two subspecies, which possibly affects downstream functions and explains the aforementioned phenotypic variations. Our study demonstrates that differentiations in energy metabolism underlie the plasticity of adaptive divergence in two oyster subspecies and suggest C. gigas angulata with moderate phenotypic plasticity has higher adaptive potential to cope with exacerbated global warming.     

基因组选择技术在农业动物育种中的应用

基因组选择(genomic selection, GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。     

Herpes virus in juvenile Pacific oysters Crassostrea gigas from Tomales Bay, California, coincides with summer mortality episodes

Pacific Crassostrea gigas and eastern C. virginica oysters were examined between June 2002 and April 2003 from 8 locations along the east, west and south USA coasts for oyster herpes virus (OsHV) infections using the A primer set in a previously developed PCR test. Only surviving Pacific oysters from a mortality event in Tomales Bay, California, USA, where annual losses of oysters have occurred each summer since 1993, were infected with a herpes-like virus in 2002. PCR examination using template amounts of both 50 and 500 ng were essential for OsHV detection. Sequence analysis indicated that the Tomales Bay OsHV was similar to that identified in France with the exception of a single base pair substitution in a 917 bp fragment of the viral genome. However, unlike the French OsHV-1, the Tomales Bay OsHV did not amplify with the primer pair of a second OsHV-1 PCR assay, suggesting that further characterization of these viruses is warranted. No evidence of Cowdry type A viral infections characteristic of herpes virus infections or other pathogens were observed in OsHV-infected oysters. Hemocytosis, diapedesis and hemocyte degeneration characterized by nuclear pycnosis and fragmentation were observed in infected oysters, which is consistent with previous observations of OsHV infections in France. Together these data suggest that OsHV may be associated with the annual summer Pacific oyster seed mortality observed in Tomales Bay, but establishment of a causal relationship warrants further investigation.     

Development of an integrated 200K SNP genotyping array and application for genetic mapping,genome assembly improvement and genome wide association studies in pear (Pyrus)

Pear (Pyrus; 2n = 34), the third most important temperate fruit crop, has great nutritional and economic value. Despite the availability of many genomic resources in pear, it is challenging to genotype novel germplasm resources and breeding progeny in a timely and cost‐effective manner. Genotyping arrays can provide fast, efficient and high‐throughput genetic characterization of diverse germplasm, genetic mapping and breeding populations. We present here 200K AXIOM^® PyrSNP, a large‐scale single nucleotide polymorphism (SNP) genotyping array to facilitate genotyping of Pyrus species. A diverse panel of 113 re‐sequenced pear genotypes was used to discover SNPs to promote increased adoption of the array. A set of 188 diverse accessions and an F₁ population of 98 individuals from ‘Cuiguan’ × ‘Starkrimson’ was genotyped with the array to assess its effectiveness. A large majority of SNPs (166 335 or 83%) are of high quality. The high density and uniform distribution of the array SNPs facilitated prediction of centromeric regions on 17 pear chromosomes, and significantly improved the genome assembly from 75.5% to 81.4% based on genetic mapping. Identification of a gene associated with flowering time and candidate genes linked to size of fruit core via genome wide association studies showed the usefulness of the array in pear genetic research. The newly developed high‐density SNP array presents an important tool for rapid and high‐throughput genotyping in pear for genetic map construction, QTL identification and genomic selection.