Development and testing of a combined species SNP array for the European seabass (Dicentrarchus labrax) and gilthead seabream (Sparus aurata)

SNP arrays are powerful tools for high-resolution studies of the genetic basis of complex traits, facilitating both selective breeding and population genomic research. The European seabass (Dicentrarchus labrax) and the gilthead seabream (Sparus aurata) are the two most important fish species for Mediterranean aquaculture. While selective breeding programmes increasingly underpin stock supply for this industry, genomic selection is not yet widespread. Genomic selection has major potential to expedite genetic gain, particularly for traits practically impossible to measure on selection candidates, such as disease resistance and fillet characteristics. The aim of our study was to design a combined-species 60 K SNP array for European seabass and gilthead seabream, and to test its performance on farmed and wild populations from numerous locations throughout the species range. To achieve this, high coverage Illumina whole-genome sequencing of pooled samples was performed for 24 populations of European seabass and 27 populations of gilthead seabream. This resulted in a database of ~20 million SNPs per species, which were then filtered to identify high-quality variants and create the final set for the development of the ‘MedFish’ SNP array. The array was then tested by genotyping a subset of the discovery populations, highlighting a high conversion rate to functioning polymorphic assays on the array (92% in seabass; 89% in seabream) and repeatability (99.4–99.7%). The platform interrogates ~30 K markers in each species, includes features such as SNPs previously shown to be associated with performance traits, and is enriched for SNPs predicted to have high functional effects on proteins. The array was demonstrated to be effective at detecting population structure across a wide range of fish populations from diverse geographical origins, and to examine the extent of haplotype sharing among Mediterranean farmed fish populations. In conclusion, the new MedFish array enables efficient and accurate high-throughput genotyping for genome-wide distributed SNPs for each fish species, and will facilitate stock management, population genomics approaches, and acceleration of selective breeding through genomic selection.     

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.     

A whole‐genome SNP array (RICE6K) for genomic breeding in rice

The advances in genotyping technology provide an opportunity to use genomic tools in crop breeding. As compared to field selections performed in conventional breeding programmes, genomics‐based genotype screen can potentially reduce number of breeding cycles and more precisely integrate target genes for particular traits into an ideal genetic background. We developed a whole‐genome single nucleotide polymorphism (SNP) array, RICE6K, based on Infinium technology, using representative SNPs selected from more than four million SNPs identified from resequencing data of more than 500 rice landraces. RICE6K contains 5102 SNP and insertion–deletion (InDel) markers, about 4500 of which were of high quality in the tested rice lines producing highly repeatable results. Forty‐five functional markers that are located inside 28 characterized genes of important traits can be detected using RICE6K. The SNP markers are evenly distributed on the 12 chromosomes of rice with the average density of 12 SNPs per 1 Mb and can provide information for polymorphisms between indica and japonica subspecies as well as varieties within indica and japonica groups. Application tests of RICE6K showed that the array is suitable for rice germplasm fingerprinting, genotyping bulked segregating pools, seed authenticity check and genetic background selection. These results suggest that RICE6K provides an efficient and reliable genotyping tool for rice genomic breeding.     

Genomic selection prediction models comparing sequence capture and SNP array genotyping methods

The successful application of genomic selection (GS) approaches is dependent on genetic makers derived from high-throughput and low-cost genotyping methods. Recent GS studies in trees have predominantly relied on SNP arrays as the source of genotyping, though this technology has a high entry cost. The recent development of alternative genotyping platforms, tailored to specific species and with low entry cost, has become possible due to advances in next-generation sequencing and genome complexity reduction methods such as sequence capture. However, the performance of these new platforms in GS models has not yet been evaluated, or compared to models developed from SNP arrays. Here, we evaluate the impact of these genotyping technologies on the development of GS prediction models for a Eucalyptus breeding population composed of 739 trees phenotyped for 13 wood quality and growth traits. Genotyping data obtained with both methods were compared for linkage disequilibrium, minor allele frequency, and missing data. Phenotypic prediction methods RR-BLUP and BayesB were employed, while predictive ability using cross validation was used to evaluate the performance of GS models derived from the different genotyping platforms. Differences in linkage disequilibrium patterns, minor allele frequency, missing data, and marker distribution were detected between sequence capture and SNP arrays. However, RR-BLUP and BayesB GS models resulted in similar predictive abilities. These results demonstrate that both genotyping methods are equivalent for genomic prediction of the traits evaluated. Sequence capture offers an alternative for species where SNP arrays are not available, or for when the initial development cost is too high.     

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。     

Towards genomic selection in apple (Malus ×domestica Borkh.) breeding programmes: Prospects, challenges and strategies

The apple genome sequence and the availability of high-throughput genotyping technologies have initiated a new era where SNP markers are abundant across the whole genome. Genomic selection (GS) is a statistical approach that utilizes all available genome-wide markers simultaneously to estimate breeding values or total genetic values. For breeding programmes, GS is a promising alternative to the traditional marker-assisted selection for manipulating complex polygenic traits often controlled by many small-effect genes. Various factors, such as genetic architecture of selection traits, population size and structure, genetic evaluation systems, density of SNP markers and extent of linkage disequilibrium, have been shown to be the key drivers of the accuracy of GS. In this paper, we provide an overview of the status of these aspects in current apple-breeding programmes. Strategies for GS for fruit quality and disease resistance are discussed, and an update on an empirical genomic selection study in a New Zealand apple-breeding programme is provided, along with a foresight of expected accuracy from such selection.     

Development of a 135K SNP genotyping array for Actinidia arguta and its applications for genetic mapping and QTL analysis in kiwifruit

Kiwifruit (Actinidia spp) is a woody, perennial and deciduous vine. In this genus, there are multiple ploidy levels but the main cultivated cultivars are polyploid. Despite the availability of many genomic resources in kiwifruit, SNP genotyping is still a challenge given these different levels of polyploidy. Recent advances in SNP array technologies have offered a high-throughput genotyping platform for genome-wide DNA polymorphisms. In this study, we developed a high-density SNP genotyping array to facilitate genetic studies and breeding applications in kiwifruit. SNP discovery was performed by genome-wide DNA sequencing of 40 kiwifruit genotypes. The identified SNPs were stringently filtered for sequence quality, predicted conversion performance and distribution over the available Actinidia chinensis genome. A total of 134 729 unique SNPs were put on the array. The array was evaluated by genotyping 400 kiwifruit individuals. We performed a multidimensional scaling analysis to assess the diversity of kiwifruit germplasm, showing that the array was effective to distinguish kiwifruit accessions. Using a tetraploid F1 population, we constructed an integrated linkage map covering 3060.9 cM across 29 linkage groups and performed QTL analysis for the sex locus that has been identified on Linkage Group 3 (LG3) in Actinidia arguta. Finally, our dataset presented evidence of tetrasomic inheritance with partial preferential pairing in A. arguta. In conclusion, we developed and evaluated a 135K SNP genotyping array for kiwifruit. It has the advantage of a comprehensive design that can be an effective tool in genetic studies and breeding applications in this high-value crop.     

<Emphasis Type="Bold">A user guide to the</Emphasis><Emphasis Type="BoldItalic">Brassica</Emphasis><Emphasis Type="Bold">60K Illumina Infinium</Emphasis>™ <Emphasis Type="Bold">SNP genotyping array</Emphasis>

The Brassica napus 60K Illumina Infinium? SNP array has had huge international uptake in the rapeseed community due to the revolutionary speed of acquisition and ease of analysis of this high-throughput genotyping data, particularly when coupled with the newly available reference genome sequence. However, further utilization of this valuable resource can be optimized by better understanding the promises and pitfalls of SNP arrays. We outline how best to analyze Brassica SNP marker array data for diverse applications, including linkage and association mapping, genetic diversity and genomic introgression studies. We present data on which SNPs are locus-specific in winter, semi-winter and spring B. napus germplasm pools, rather than amplifying both an A-genome and a C-genome locus or multiple loci. Common issues that arise when analyzing array data will be discussed, particularly those unique to SNP markers and how to deal with these for practical applications in Brassica breeding applications.     

Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture

Key Message

Genomic prediction using the Brassica 60 k genotyping array is efficient in oilseed rape hybrids. Prediction accuracy is more dependent on trait complexity than on the prediction model.

Abstract

In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F₁-hybrid performance, and selection of well-paired parents requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrids, in general, dramatically exceeds breeding capacity and budget. Integration of genomic selection (GS) could substantially increase the number of potential combinations that can be evaluated. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously predicted in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. In this study, we evaluated the usefulness of the Illumina Brassica 60 k SNP array for genomic prediction and compared three alternative approaches based on a homoscedastic ridge regression BLUP and three Bayesian prediction models that considered general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal doubled haploid lines and five male-sterile testers. Predictive ability was evaluated for seven agronomic traits. We demonstrate that the Brassica 60 k genotyping array is an adequate and highly valuable platform to implement genomic prediction of hybrid performance in oilseed rape. Furthermore, we present first insights into the application of established statistical models for prediction of important agronomical traits with contrasting patterns of polygenic control.

     

A System for Distinguishing Octoploid Strawberry Cultivars Using High-Throughput SNP Genotyping

Cultivated strawberry (Fragaria × ananassa) is an important commercial berry crop grown throughout the world. Improved strawberry cultivars are developed to meet the needs of consumers and breeders. Strawberries are usually propagated through runners, which sometimes lead to mislabeling or misinterpretation of cultivars. However, perfect identification of strawberry cultivars is essential for germplasm maintenance and for breeding programs. Molecular marker technology has been widely used to distinguish cultivars of other crops, but marker development in octoploid strawberries is complicated. Therefore, SNP marker with high-density and even distribution in the genome has been used currently as efficient DNA markers. In this report, previously published high-quality poly high resolution (PHR) SNPs from the 90 K Axiom® SNP array were utilized to develop a Fluidigm 24 SNPs genotyping system. Hundred nine (109) octoploid strawberry cultivars were screened using this 24 SNPs chip set. In addition, 24 SNPs were mapped to six chromosomes of diploid strawberry (Fragaria vesca). Our developed SNPs fluidigm genotyping is automatable, easy and reliable for processing and interpretation of data. Thus, this high-throughput SNP genotyping system will be a useful tool for distinguishing strawberry cultivars and find out parent-offspring relationship.     

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.     

Large-scale Genotyping and Genetic Mapping in Plasmodium Parasites

The completion of many malaria parasite genomes provides great opportunities for genomewide characterization of gene expression and high-throughput genotyping. Substantial progress in malaria genomics and genotyping has been made recently, particularly the development of various microarray platforms for large-scale characterization of the Plasmodium falciparum genome. Microarray has been used for gene expression analysis, detection of single nucleotide polymorphism (SNP) and copy number variation (CNV), characterization of chromatin modifications, and other applications. Here we discuss some recent advances in genetic mapping and genomic studies of malaria parasites, focusing on the use of high-throughput arrays for the detection of SNP and CNV in the P. falciparum genome. Strategies for genetic mapping of malaria traits are also discussed.     

Development and evaluation of a 9K SNP array for peach by internationally coordinated SNP detection and validation in breeding germplasm

Although a large number of single nucleotide polymorphism (SNP) markers covering the entire genome are needed to enable molecular breeding efforts such as genome wide association studies, fine mapping, genomic selection and marker-assisted selection in peach [Prunus persica (L.) Batsch] and related Prunus species, only a limited number of genetic markers, including simple sequence repeats (SSRs), have been available to date. To address this need, an international consortium (The International Peach SNP Consortium; IPSC) has pursued a coordinated effort to perform genome-scale SNP discovery in peach using next generation sequencing platforms to develop and characterize a high-throughput Illumina Infinium® SNP genotyping array platform. We performed whole genome re-sequencing of 56 peach breeding accessions using the Illumina and Roche/454 sequencing technologies. Polymorphism detection algorithms identified a total of 1,022,354 SNPs. Validation with the Illumina GoldenGate® assay was performed on a subset of the predicted SNPs, verifying ∼75% of genic (exonic and intronic) SNPs, whereas only about a third of intergenic SNPs were verified. Conservative filtering was applied to arrive at a set of 8,144 SNPs that were included on the IPSC peach SNP array v1, distributed over all eight peach chromosomes with an average spacing of 26.7 kb between SNPs. Use of this platform to screen a total of 709 accessions of peach in two separate evaluation panels identified a total of 6,869 (84.3%) polymorphic SNPs.The almost 7,000 SNPs verified as polymorphic through extensive empirical evaluation represent an excellent source of markers for future studies in genetic relatedness, genetic mapping, and dissecting the genetic architecture of complex agricultural traits. The IPSC peach SNP array v1 is commercially available and we expect that it will be used worldwide for genetic studies in peach and related stone fruit and nut species.     

Genetic diversity,population structure,and linkage disequilibrium of elite and local apple accessions from Belgium using the IRSC array

The identification of molecular markers associated with economic and quality traits will help improve breeding for new apple (Malus × domestica Borkh.) cultivars. Tools such as the 8K apple SNP array developed by the RosBREED consortium allow for high-throughput genotyping of SNP polymorphisms within collections. However, genetic characterization and the identification of population stratification and kinship within germplasm collections is a fundamental prerequisite for identifying robust marker–trait associations. In this study, a collection of apple germplasm originally developed for plant architectural studies and consisting of both non-commercial/local and elite accessions was genotyped using the 8K apple SNP array to identify cryptic relationships between accessions, to analyze population structure and to calculate the linkage disequilibrium (LD). A total of nine pairs of synonyms and several triploids accessions were identified within the 130 accessions genotyped. In addition, most of the known parent-child relations were confirmed, and several putative, previously unknown parent-child relations were identified among the local accessions. No clear subgroups could be identified although some separation between local and elite accessions was evident. The study of LD showed a rapid decay in our collection, indicating that a larger number of SNPs is necessary to perform whole genome association mapping. Finally, an association mapping effort for architectural traits was carried out on a small number of accessions to estimate the feasibility of this approach.     

Genomic predictions of breeding values in a cloned <Emphasis Type="Italic">Eucalyptus globulus</Emphasis> population in Chile

In Chile, an intensive Eucalyptus globulus clonal selection program is being carried out to increase forest productivity for pulp production. A breeding population was used to investigate the predicted ability of single nucleotide polymorphism (SNP) markers for genomic selection (GS). A total of 310 clones from 53 families were used. Stem volume and wood density were measured on all clones. Trees were genotyped at 12 K polymorphic markers using the EUChip60K genotype array. Genomic best linear unbiased prediction, Bayesian lasso regression, Bayes B, and Bayes C models were used to predict genomic estimated breeding values (GEBV). For cross-validation, 260 individuals were sampled for model training and 50 individuals for model validation, using 2 folds and 10 replications each. The average predictive ability estimates for wood density and stem volume across the models were 0.58 and 0.75, respectively. The average rank correlations were 0.59 and 0.71, respectively. Models produced very similar bias for both traits. When clones were ranked based on their GEBV, models had similar phenotypic mean for the top 10% of the clones. The predicted ability of markers will likely decrease if the models are used to predict GEBV of new material coming from the breeding program, because of a different marker–trait phase introduced by recombination. The results should be validated with larger populations and across two generations before routine applications of GS in E. globulus. We suggest that GS is a viable strategy to accelerate clonal selection program of E. globulus in Chile.     

Development of a 38 K single nucleotide polymorphism array and application in genomic selection for resistance against Vibrio harveyi in Chinese tongue sole,Cynoglossus semilaevis

Based on 1572 re-sequenced Chinese tongue sole (Cynoglossus semilaevis), we investigated the accuracy of four genomic methods at predicting genomic estimated breeding values (GEBVs) of Vibrio harveyi resistance in C. semilaevis when SNPs varying from 500 to 500 k. All methods outperformed the pedigree-based best linear unbiased prediction when SNPs reached 50 k or more. Then, we developed an SNP array “Solechip No.1” for C. semilaevis breeding using the Affymetrix Axiom technology. This array contains 38,295 SNPs with an average of 10.5 kb inter-spacing between two adjacent SNPs. We selected 44 candidates as the parents of 23 families and genotyped them by the array. The challenge survival rates of offspring families had a correlation of 0.706 with the mid-parental GEBVs. This SNP array is a convenient and reliable tool in genotyping, which could be used for improving V. harveyi resistance in C. semilaevis coupled with the genomic selection methods.     

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.     

QTL Scanning for Rice Yield Using a Whole Genome SNP Array

High-throughput SNP genotyping is widely used for plant genetic studies. Recently, a RICE6K SNP array has been developed based on the Illumina Bead Array platform and Infinium SNP assay technology for genome-wide evaluation of allelic variations and breeding applications. In this study, the RICE6K SNP array was used to genotype a recombinant inbred line （RIL） population derived from the cross between the indica variety, Zhenshan 97, and the japonica variety, Xizang 2. A total of 3324 SNP markers of high quality were identified and were grouped into 1495 recombination bins in the RIL population. A high-density linkage map, consisting of the 1495 bins, was developed, covering 1591.2 cM and with average length ofl.1 cM per bin. Segregation distortions were observed in 24 regions of the 11 chromosomes in the RILs. One half of the distorted regions contained fertility genes that had been previously reported. A total of 23 QTLs were identified for yield. Seven QTLs were firstly detected in this study. The positive alleles from about half of the identified QTLs came from Zhenshan 97 and they had lower phenotypic values than Xizang 2. This indicated that favorable alleles for breeding were dispersed in both parents and pyramiding favorable alleles could develop elite lines. The size of the mapping population for QTL analysis using high throughput SNP genotyping platform is also discussed.