Construction of two genetic linkage maps of taro using single nucleotide polymorphism and microsatellite markers

Linkage maps are needed for genetic studies and molecular breeding of taro (Colocasia esculenta). In this study, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphism (SNP) loci on two mapping populations: F31 (HLB11 × VU006) composed of 266 progenies and F32 [HLB01 × (VU370×ID316)] composed of 292 progenies. SNP calling generated an initial set of 22,734 SNPs for F31 and 16,744 for F32. A large proportion of individuals and loci were later removed by filtering on the proportion of missing data and segregation distortions. Linkage maps were constructed with filtered SNPs in association with 14 simple sequence repeat (SSR) markers, using the maximum likelihood method. In both populations, loci were successfully grouped into 14 linkage groups (LGs) with an independence logarithm of odds (LOD) threshold of 11.0 and 8.0 for F31 and F32, respectively. LGs ranged in size from 90 to 15 markers for F31 and from 92 to 12 markers for F32. Bridge markers (459 SNPs and 9 SSRs) were identified and revealed homologous groups between families. Although our maps presented unprecedented chromosome coverage, the colinearity between homologous groups was low (except for LG07), and map lengths were globally inflated. Putative effects of missing data, segregation distortion, and genotyping errors on map accuracy are discussed. This research work led to the identification of a reliable set of SNPs potentially useful as a tool for a wide range of genetic studies in taro.     

Development of single nucleotide polymorphism (SNP) markers for use in commercial maize (Zea mays L.) germplasm

The development of single nucleotide polymorphism (SNP) markers in maize offers the opportunity to utilize DNA markers in many new areas of population genetics, gene discovery, plant breeding and germplasm identification. However, the steps from sequencing and SNP discovery to SNP marker design and validation are lengthy and expensive. Access to a set of validated SNP markers is a significant advantage to maize researchers who wish to apply SNPs in scientific inquiry. We mined 1,088 loci sequenced across 60 public inbreds that have been used in maize breeding in North America and Europe. We then selected 640 SNPs using generalized marker design criteria that enable utilization with several SNP chemistries. While SNPs were found on average every 43 bases in 1,088 maize gene sequences, SNPs that were amenable to marker design were found on average every 623 bases; representing only 7% of the total SNPs discovered. We also describe the development of a 768 marker multiplex assay for use on the Illumina^® BeadArray? platform. SNP markers were mapped on the IBM2 intermated B73 × Mo17 high resolution genetic map using either the IBM2 segregating population, or segregation in multiple parent-progeny triplets. A high degree of colinearity was found with the genetic nested association map. For each SNP presented we give information on map location, polymorphism rates in different heterotic groups and performance on the Illumina^® platform.     

SNP marker diversity in common bean (<Emphasis Type="Italic">Phaseolus vulgaris</Emphasis> L.)

Single nucleotide polymorphism (SNP) markers have become a genetic technology of choice because of their automation and high precision of allele calls. In this study, our goal was to develop 94 SNPs and test them across well-chosen common bean (Phaseolus vulgaris L.) germplasm. We validated and accessed SNP diversity at 84 gene-based and 10 non-genic loci using KASPar technology in a panel of 70 genotypes that have been used as parents of mapping populations and have been previously evaluated for SSRs. SNPs exhibited high levels of genetic diversity, an excess of middle frequency polymorphism, and a within-genepool mismatch distribution as expected for populations affected by sudden demographic expansions after domestication bottlenecks. This set of markers was useful for distinguishing Andean and Mesoamerican genotypes but less useful for distinguishing within each gene pool. In summary, slightly greater polymorphism and race structure was found within the Andean gene pool than within the Mesoamerican gene pool but polymorphism rate between genotypes was consistent with genepool and race identity. Our survey results represent a baseline for the choice of SNP markers for future applications because gene-associated SNPs could themselves be causative SNPs for traits. Finally, we discuss that the ideal genetic marker combination with which to carry out diversity, mapping and association studies in common bean should consider a mix of both SNP and SSR markers.     

A comprehensive framework for detecting copy number variants from single nucleotide polymorphism data: ‘rCNV’, a versatile r package for paralogue and CNV detection

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy–Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package ‘rCNV’, which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon—Oncorhynchus tshawytscha), Rapture (American lobster—Homarus americanus), Exome-capture (Norway spruce—Picea abies) and WGS (Malaria mosquito—Anopheles gambiae).     

High‐throughput genomics in sorghum: from whole‐genome resequencing to a SNP screening array

With its small, diploid and completely sequenced genome, sorghum (Sorghum bicolor L. Moench) is highly amenable to genomics‐based breeding approaches. Here, we describe the development and testing of a robust single‐nucleotide polymorphism (SNP) array platform that enables polymorphism screening for genome‐wide and trait‐linked polymorphisms in genetically diverse S. bicolor populations. Whole‐genome sequences with 6× to 12× coverage from five genetically diverse S. bicolor genotypes, including three sweet sorghums and two grain sorghums, were aligned to the sorghum reference genome. From over 1 million high‐quality SNPs, we selected 2124 Infinium Type II SNPs that were informative in all six source genomes, gave an optimal Assay Design Tool (ADT) score, had allele frequencies of 50% in the six genotypes and were evenly spaced throughout the S. bicolor genome. Furthermore, by phenotype‐based pool sequencing, we selected an additional 876 SNPs with a phenotypic association to early‐stage chilling tolerance, a key trait for European sorghum breeding. The 3000 attempted bead types were used to populate half of a dual‐species Illumina iSelect SNP array. The array was tested using 564 Sorghum spp. genotypes, including offspring from four unrelated recombinant inbred line (RIL) and F₂ populations and a genetic diversity collection. A high call rate of over 80% enabled validation of 2620 robust and polymorphic sorghum SNPs, underlining the efficiency of the array development scheme for whole‐genome SNP selection and screening, with diverse applications including genetic mapping, genome‐wide association studies and genomic selection.     

Comparing RADseq and microsatellites for estimating genetic diversity and relatedness — Implications for brown trout conservation

The conservation and management of endangered species requires information on their genetic diversity, relatedness and population structure. The main genetic markers applied for these questions are microsatellites and single nucleotide polymorphisms (SNPs), the latter of which remain the more resource demanding approach in most cases. Here, we compare the performance of two approaches, SNPs obtained by restriction‐site‐associated DNA sequencing (RADseq) and 16 DNA microsatellite loci, for estimating genetic diversity, relatedness and genetic differentiation of three, small, geographically close wild brown trout (Salmo trutta) populations and a regionally used hatchery strain. The genetic differentiation, quantified as F_ST, was similar when measured using 16 microsatellites and 4,876 SNPs. Based on both marker types, each brown trout population represented a distinct gene pool with a low level of interbreeding. Analysis of SNPs identified half‐ and full‐siblings with a higher probability than the analysis based on microsatellites, and SNPs outperformed microsatellites in estimating individual‐level multilocus heterozygosity. Overall, the results indicated that moderately polymorphic microsatellites and SNPs from RADseq agreed on estimates of population genetic structure in moderately diverged, small populations, but RADseq outperformed microsatellites for applications that required individual‐level genotype information, such as quantifying relatedness and individual‐level heterozygosity. The results can be applied to other small populations with low or moderate levels of genetic diversity.     

Genome-wide single nucleotide polymorphism discovery and validation in adzuki bean

Adzuki bean, also known as red bean (Vigna angularis), with 2n = 22 chromosomes, is an important legume crop in East Asian countries, including China, Japan, and Korea. For single nucleotide polymorphism (SNP) discovery, we used Vigna accessions, V. angularis IT213134 and its wild relative V. nakashimae IT178530, because of the lack of DNA sequence polymorphism in the cultivated species. Short read sequences of IT213134 and IT178530 of approximately 37 billion and 35 billion bp were produced using the Illumina HiSeq 2000 system to a sequencing depth of 61.5× and 57.7×, respectively. After de novo assembly was carried out with trimmed HiSeq reads from IT213134, 98,441 contigs of various sizes were produced with N50 of 13,755 bp. Using Burrows–Wheeler Aligner software, trimmed short reads of V. nakashimae IT178530 were successfully mapped to IT213134 contigs. All sequence variations at the whole-genome level were examined between the two Vigna species. Of the 1,565,699 SNPs, 59.4 % were transitions and 40.6 % were transversions. A total of 213,758 SNPs, consisting of 122,327 non-synonymous and 91,431 synonymous SNPs, were identified in coding sequences. For SNP validation, 96 SNPs in the genic region were chosen from among IT213134 contigs longer than 10 kb. Of these 96 SNPs, 88 were confirmed by Sanger sequencing of 10 adzuki bean genotypes from various geographic origins as well as IT213134 and its wild relative IT178530. These genome-wide SNP markers will enrich the existing Vigna resources and, specifically, could be of value for constructing a genetic map and evaluating the genetic diversity of adzuki bean.     

Gene-associated single nucleotide polymorphism discovery in perennial ryegrass (Lolium perenne L.)

Molecular genetic marker development in perennial ryegrass has largely been dependent on anonymous sequence variation. The availability of a large-scale EST resource permits the development of functionally-associated genetic markers based on SNP variation in candidate genes. Genic SNP loci and associated haplotypes are suitable for implementation in molecular breeding of outbreeding forage species. Strategies for in vitro SNP discovery through amplicon cloning and sequencing have been designed and implemented. Putative SNPs were identified within and between the parents of the F₁(NA₆ × AU₆) genetic mapping family and were validated among progeny individuals. Proof-of-concept for the process was obtained using the drought tolerance-associated LpASRa2 gene. SNP haplotype structures were determined and correlated with predicted amino acid changes. Gene-length LD was evaluated across diverse germplasm collections. A survey of SNP variation across 100 candidate genes revealed a high frequency of SNP incidence (c. 1 per 54 bp), with similar proportions in exons and introns. A proportion (c. 50%) of the validated genic SNPs were assigned to the F₁(NA₆ × AU₆) genetic map, showing high levels of coincidence with previously mapped RFLP loci. The perennial ryegrass SNP resource will enable genetic map integration, detailed LD studies and selection of superior allele content during varietal development.     

These aren’t the loci you’e looking for: Principles of effective SNP filtering for molecular ecologists

Sequencing reduced‐representation libraries of restriction site‐associated DNA (RADseq) to identify single nucleotide polymorphisms (SNPs) is quickly becoming a standard methodology for molecular ecologists. Because of the scale of RADseq data sets, putative loci cannot be assessed individually, making the process of filtering noise and correctly identifying biologically meaningful signal more difficult. Artefacts introduced during library preparation and/or bioinformatic processing of SNP data can create patterns that are incorrectly interpreted as indicative of population structure or natural selection. Therefore, it is crucial to carefully consider types of errors that may be introduced during laboratory work and data processing, and how to minimize, detect and remove these errors. Here, we discuss issues inherent to RADseq methodologies that can result in artefacts during library preparation and locus reconstruction resulting in erroneous SNP calls and, ultimately, genotyping error. Further, we describe steps that can be implemented to create a rigorously filtered data set consisting of markers accurately representing independent loci and compare the effect of different combinations of filters on four RAD data sets. At last, we stress the importance of publishing raw sequence data along with final filtered data sets in addition to detailed documentation of filtering steps and quality control measures.     

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation.     

A high-throughput SNP marker system for parental polymorphism screening, and diversity analysis in common bean (Phaseolus vulgaris L.)

Single nucleotide polymorphism (SNP) detection has become a marker system of choice, because of the high abundance of source polymorphisms and the ease with which allele calls are automated. Various technologies exist for the evaluation of SNP loci and previously we validated two medium throughput technologies. In this study, our goal was to utilize a 768 feature, Illumina GoldenGate assay for common bean (Phaseolus vulgaris L.) developed from conserved legume gene sequences and to use the new technology for (1) the evaluation of parental polymorphisms in a mini-core set of common bean accessions and (2) the analysis of genetic diversity in the crop. A total of 736 SNPs were scored on 236 diverse common bean genotypes with the GoldenGate array. Missing data and heterozygosity levels were low and 94 % of the SNPs were scorable. With the evaluation of the parental polymorphism genotypes, we estimated the utility of the SNP markers in mapping for inter-genepool and intra-genepool populations, the latter being of lower polymorphism than the former. When we performed the diversity analysis with the diverse genotypes, we found Illumina GoldenGate SNPs to provide equivalent evaluations as previous gene-based SNP markers, but less fine-distinctions than with previous microsatellite marker analysis. We did find, however, that the gene-based SNPs in the GoldenGate array had some utility in race structure analysis despite the low polymorphism. Furthermore the SNPs detected high heterozygosity in wild accessions which was probably a reflection of ascertainment bias. The Illumina SNPs were shown to be effective in distinguishing between the genepools, and therefore were most useful in saturation of inter-genepool genetic maps. The implications of these results for breeding in common bean are discussed as well as the advantages and disadvantages of the GoldenGate system for SNP detection.     

SNP discovery in nonmodel organisms: strand bias and base‐substitution errors reduce conversion rates

Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep‐sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize ‘bycatch’—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand‐bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single‐copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.     

Two Different High Throughput Sequencing Approaches Identify Thousands of De Novo Genomic Markers for the Genetically Depleted Bornean Elephant

High throughput sequencing technologies are being applied to an increasing number of model species with a high-quality reference genome. The application and analyses of whole-genome sequence data in non-model species with no prior genomic information are currently under way. Recent sequencing technologies provide new opportunities for gathering genomic data in natural populations, laying the empirical foundation for future research in the field of conservation and population genomics. Here we present the case study of the Bornean elephant, which is the most endangered subspecies of Asian elephant and exhibits very low genetic diversity. We used two different sequencing platforms, the Roche 454 FLX (shotgun) and Illumina, GAIIx (Restriction site associated DNA, RAD) to evaluate the feasibility of the two methodologies for the discovery of de novo markers (single nucleotide polymorphism, SNPs and microsatellites) using low coverage data. Approximately, 6,683 (shotgun) and 14,724 (RAD) SNPs were detected within our elephant sequence dataset. Genotyping of a representative sample of 194 SNPs resulted in a SNP validation rate of ∼ 83 to 94% and 17% of the loci were polymorphic with a low diversity (H _o = 0.057). Different numbers of microsatellites were identified through shotgun (27,226) and RAD (868) techniques. Out of all di-, tri-, and tetra-microsatellite loci, 1,706 loci had sufficient flanking regions (shotgun) while only 7 were found with RAD. All microsatellites were monomorphic in the Bornean but polymorphic in another elephant subspecies. Despite using different sample sizes, and the well known differences in the two platforms used regarding sequence length and throughput, the two approaches showed high validation rate. The approaches used here for marker development in a threatened species demonstrate the utility of high throughput sequencing technologies as a starting point for the development of genomic tools in a non-model species and in particular for a species with low genetic diversity.     

Characterization of genetic diversity and population structure in wheat using array based SNP markers

Genetic diversity is crucial for successful adaptation and sustained improvement in crops. India is bestowed with diverse agro-climatic conditions which makes it rich in wheat germplasm adapted to various niches. Germplasm repository consists of local landraces, trait specific genetic stocks including introgressions from wild relatives, exotic collections, released varieties, and improved germplasm. Characterization of genetic diversity is done using morpho-physiological characters as well as by analyzing variations at DNA level. However, there are not many reports on array based high throughput SNP markers having characteristics of genome wide coverage employed in Indian spring wheat germplasm. Amongst wheat SNP arrays, 35K Axiom Wheat Breeder’s Array has the highest SNP polymorphism efficiency suitable for genetic mapping and genetic diversity characterization. Therefore, genotyping was done using 35K in 483 wheat genotypes resulting in 14,650 quality filtered SNPs, that were distributed across the B (~?50%), A (~?39%), and D (~?10%) genomes. The total genetic distance coverage was 4477.85 cM with 3.27 SNP/cM and 0.49 cM/SNP as average marker density and average inter-marker distance, respectively. The PIC ranged from 0.09 to 0.38 with an average of 0.29 across genomes. Population structure and Principal Coordinate Analysis resulted in two subpopulations (SP1 and SP2). The analysis of molecular variance revealed the genetic variation of 2% among and 98% within subpopulations indicating high gene flow between SP1 and SP2. The subpopulation SP2 showed high level of genetic diversity based on genetic diversity indices viz. Shannon’s information index (I)?=?0.648, expected heterozygosity (He)?=?0.456 and unbiased expected heterozygosity (uHe)?=?0.456. To the best of our knowledge, this study is the first to include the largest set of Indian wheat genotypes studied exclusively for genetic diversity. These findings may serve as a potential source for the identification of uncharacterized QTL/gene using genome wide association studies and marker assisted selection in wheat breeding programs.

     

In silico single nucleotide polymorphism discovery and application to marker-assisted selection in soybean

The general approach to discovering single nucleotide polymorphisms (SNPs) requires locus-specific PCR amplification. To enhance the efficiency of SNP discovery in soybean, we used in silico analysis prior to re-sequencing as it is both rapid and inexpensive. In silico analysis was performed to detect putative SNPs in expressed sequence tag (EST) contigs assembled using publicly available ESTs from 18 different soybean genotypes. SNP validation by direct sequencing of six soybean cultivars and a wild soybean genotype was performed with PCR primers designed from EST contigs aligned with at least 5 out of 18 soybean genotypes. The efficiency of SNP discovery among the confirmation genotypes was 81.2%. Furthermore, the efficiency of SNP discovery between Pureunkong and Jinpumkong 2 genotypes was 47.4%, a great improvement on our previous finding based on direct sequencing (22.3%). Using SNPs between Pureunkong and Jinpumkong 2 in EST contigs, which were linked to target traits, we were able to genotype 90 recombinant inbred lines by high-resolution melting (HRM) analysis. These SNPs were mapped onto the expected locations near quantitative trait loci for water-logging tolerance and seed pectin concentration. Thus, our protocol for HRM analysis can be applied successfully not only to genetic diversity studies, but also to marker-assisted selection (MAS). Our study suggests that a combination of in silico analysis and HRM can reduce the cost and labor involved in developing SNP markers and genotyping SNPs. The markers developed in this study can also easily be applied to MAS if the markers are associated with the target traits.     

Genome-wide SNP discovery in mungbean by Illumina HiSeq

Mungbean [Vigna radiata (L.) Wilczek], a self-pollinated diploid plant with 2n = 22 chromosomes, is an important legume crop with a high-quality amino acid profile. Sequence variation at the whole-genome level was examined by comparing two mungbean cultivars, Sunhwanokdu and Gyeonggijaerae 5, using Illumina HiSeq sequencing data. More than 40 billion bp from both mungbean cultivars were sequenced to a depth of 72×. After de novo assembly of Sunhwanokdu contigs by ABySS 1.3.2 (N50 = 9,958 bp), those longer than 10 kb were aligned with Gyeonggijaerae 5 reads using the Burrows–Wheeler Aligner. SAMTools was used for retrieving single nucleotide polymorphisms (SNPs) between Sunhwanokdu and Gyeonggijaerae 5, defining the lowest and highest depths as 5 and 100, respectively, and the sequence quality as 100. Of the 305,504 single-base changes identified, 40,503 SNPs were considered heterozygous in Gyeonggijaerae 5. Among the remaining 265,001 SNPs, 65.9 % (174,579 cases) were transitions and 34.1 % (90,422 cases) were transversions. For SNP validation, a total of 42 SNPs were chosen among Sunhwanokdu contigs longer than 10 kb and sharing at least 80 % sequence identity with common bean expressed sequence tags as determined with est2genome. Using seven mungbean cultivars from various origins in addition to Sunhwanokdu and Gyeonggijaerae 5, most of the SNPs identified by bioinformatics tools were confirmed by Sanger sequencing. These genome-wide SNP markers could enrich the current molecular resources and might be of value for the construction of a mungbean genetic map and the investigation of genetic diversity.     

Signatures of adaptation and genetic structure among the mainland populations of Pinus radiata (D. Don) inferred from SNP loci

Insights into the relative contributions of locus specific and genome-wide effects on population genetic diversity can be gained through separation of their resulting genetic signals. Here we explore patterns of adaptive and neutral genetic diversity in the disjunct natural populations of Pinus radiata (D. Don) from mainland California. A first-generation common garden of 447 individuals revealed significant differentiation of wood phenotypes among populations (P _ST), possibly reflecting local adaptation in response to environment. We subsequently screened all trees for genetic diversity at 149 candidate gene single nucleotide polymorphism (SNP) loci for signatures of adaptation. Ten loci were identified as being possible targets of diversifying selection following F _ST outlier tests. Multivariate canonical correlation performed on a data set of 444 individuals identified significant covariance between environment, adaptive phenotypes and outlier SNP diversity, lending support to the case for local adaptation suggested from F _ST and P _ST tests. Covariation among discrete sets of outlier SNPs and adaptive phenotypes (inferred from multivariate loadings) with environment are supported by existing studies of candidate gene function and genotype–phenotype association. Canonical analyses failed to detect significant correlations between environment and 139 non-outlier SNP loci, which were applied to estimate neutral patterns of genetic differentiation among populations (F _ST 4.3 %). Using this data set, significant hierarchical structure was detected, indicating three populations on the mainland. The hierarchical relationships based on neutral SNP markers (and SSR) were in contrast with those inferred from putatively adaptive loci, potentially highlighting the independent action of selection and demography in shaping genetic structure in this species.     

A major SNP resource for dissection of phenotypic and genetic variation in Pacific white shrimp (Litopenaeus vannamei)

Bioinformatics and re-sequencing approaches were used for the discovery of sequence polymorphisms in Litopenaeus vannamei . A total of 1221 putative single nucleotide polymorphisms (SNPs) were identified in a pool of individuals from various commercial populations. A set of 211 SNPs were selected for further molecular validation and 88% showed variation in 637 samples representing three commercial breeding lines. An association analysis was performed between these markers and several traits of economic importance for shrimp producers including resistance to three major viral diseases. A small number of SNPs showed associations with test weekly gain, grow-out survival and resistance to Taura Syndrome Virus. Very low levels of linkage disequilibrium were revealed between most SNP pairs, with only 11% of SNPs showing an r ²-value above 0.10 with at least one other SNP. Comparison of allele frequencies showed small changes over three generations of the breeding programme in one of the commercial breeding populations. This unique SNP resource has the potential to catalyse future studies of genetic dissection of complex traits, tracing relationships in breeding programmes, and monitoring genetic diversity in commercial and wild populations of L. vannamei .