An Efficient Genotyping Method in Chicken Based on Genome Reducing and Sequencing

Single nucleotide polymorphisms (SNPs) are essential for identifying the genetic mechanisms of complex traits. In the present study, we applied genotyping by genome reducing and sequencing (GGRS) method to construct a 252-plex sequencing library for SNP discovery and genotyping in chicken. The library was successfully sequenced on an Illumina HiSeq 2500 sequencer with a paired-end pattern; approximately 400 million raw reads were generated, and an average of approximately 1.4 million good reads per sample were generated. A total of 91,767 SNPs were identified after strict filtering, and all of the 252 samples and all of the chromosomes were well represented. Compared with the Illumina 60K chicken SNP chip data, approximately 34,131 more SNPs were identified using GGRS, and a higher SNP density was found using GGRS, which could be beneficial for downstream analysis. Using the GGRS method, more than 3528 samples can be sequenced simultaneously, and the cost is reduced to $18 per sample. To the best of our knowledge, this study describes the first report of such highly multiplexed sequencing in chicken, indicating potential applications for genome-wide association and genomic selection in chicken.     

Genotyping by Genome Reducing and Sequencing for Outbred Animals

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only $80 (USD)/sample.     

Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations

Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate de novo marker discovery across entire genomes, and a uniform bioinformatics workflow strategy to achieve genotyping goals tailored to individual species, regardless of the availability of a reference sequence. The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n?=?222 samples) and lettuce (n?=?87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike.     

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.     

Application of Genotyping-by-Sequencing on Semiconductor Sequencing Platforms: A Comparison of Genetic and Reference-Based Marker Ordering in Barley

The rapid development of next-generation sequencing platforms has enabled the use of sequencing for routine genotyping across a range of genetics studies and breeding applications. Genotyping-by-sequencing (GBS), a low-cost, reduced representation sequencing method, is becoming a common approach for whole-genome marker profiling in many species. With quickly developing sequencing technologies, adapting current GBS methodologies to new platforms will leverage these advancements for future studies. To test new semiconductor sequencing platforms for GBS, we genotyped a barley recombinant inbred line (RIL) population. Based on a previous GBS approach, we designed bar code and adapter sets for the Ion Torrent platforms. Four sets of 24-plex libraries were constructed consisting of 94 RILs and the two parents and sequenced on two Ion platforms. In parallel, a 96-plex library of the same RILs was sequenced on the Illumina HiSeq 2000. We applied two different computational pipelines to analyze sequencing data; the reference-independent TASSEL pipeline and a reference-based pipeline using SAMtools. Sequence contigs positioned on the integrated physical and genetic map were used for read mapping and variant calling. We found high agreement in genotype calls between the different platforms and high concordance between genetic and reference-based marker order. There was, however, paucity in the number of SNP that were jointly discovered by the different pipelines indicating a strong effect of alignment and filtering parameters on SNP discovery. We show the utility of the current barley genome assembly as a framework for developing very low-cost genetic maps, facilitating high resolution genetic mapping and negating the need for developing de novo genetic maps for future studies in barley. Through demonstration of GBS on semiconductor sequencing platforms, we conclude that the GBS approach is amenable to a range of platforms and can easily be modified as new sequencing technologies, analysis tools and genomic resources develop.     

Optimization of the genotyping‐by‐sequencing strategy for population genomic analysis in conifers

Flexibility and low cost make genotyping‐by‐sequencing (GBS) an ideal tool for population genomic studies of nonmodel species. However, to utilize the potential of the method fully, many parameters affecting library quality and single nucleotide polymorphism (SNP) discovery require optimization, especially for conifer genomes with a high repetitive DNA content. In this study, we explored strategies for effective GBS analysis in pine species. We constructed GBS libraries using HpaII, PstI and EcoRI‐MseI digestions with different multiplexing levels and examined the effect of restriction enzymes on library complexity and the impact of sequencing depth and size selection of restriction fragments on sequence coverage bias. We tested and compared UNEAK, Stacks and GATK pipelines for the GBS data, and then developed a reference‐free SNP calling strategy for haploid pine genomes. Our GBS procedure proved to be effective in SNP discovery, producing 7000–11 000 and 14 751 SNPs within and among three pine species, respectively, from a PstI library. This investigation provides guidance for the design and analysis of GBS experiments, particularly for organisms for which genomic information is lacking.     

Analysis of genetic relatedness among Indian cattle (Bos indicus) using genotyping‐by‐sequencing markers

Genetic relatedness of 24 animals belonging to seven Indian cattle breeds was studied using high throughput genotyping‐by‐sequencing (GBS) markers. GBS produced 93.6 million reads with an average of about 3.9 million reads per animal. A total of 107 488 SNPs were identified in these individuals. When only one SNP per read was considered, a total of 60 261 SNPs representing independent reads were identified with an average SNP‐to‐SNP distance of 45 kb across the bovine reference genome. About 24% of the GBS‐SNP markers were more than 100 kb apart. Of these, 58 322 SNPs mapped to autosomes, 1645 to the X chromosome and 28 to the Y chromosome. The average SNP‐to‐SNP distance on the X chromosome was 91.3 kb, whereas on the Y chromosome it was 1546.4 kb. The minor allele frequency within the Indian cattle varied from 0.103 (Ongole) to 0.177 (Siri), whereas Holstein cattle had the lowest value of 0.089. This is the first application of GBS in cattle of South Asia. The baseline information generated in this study might prompt implementation of GBS in breeding of cattle belonging to this region.     

Assessment of two flexible and compatible SNP genotyping platforms: TaqMan SNP Genotyping Assays and the SNPlex Genotyping System

In this review we describe the principles, protocols, and applications of two commercially available SNP genotyping platforms, the TaqMan SNP Genotyping Assays and the SNPlex Genotyping System. Combined, these two technologies meet the requirements of multiple SNP applications in genetics research and pharmacogenetics. We also describe a set of SNP selection tools and validated assay resources which we developed to accelerate the cycle of experimentation on these platforms. Criteria for selecting the more appropriate of these two genotyping technologies are presented: the genetic architecture of the trait of interest, the throughput required, and the number of SNPs and samples needed for a successful study. Overall, the TaqMan assay format is suitable for low- to mid-throughput applications in which a high assay conversion rate, simple assay workflow, and low cost of automation are desirable. The SNPlex Genotyping System, on the other hand, is well suited for SNP applications in which throughput and cost-efficiency are essential, e.g., applications requiring either the testing of large numbers of SNPs and samples, or the flexibility to select various SNP subsets.     

Development and Evaluation of SoySNP50K,a High-Density Genotyping Array for Soybean

The objective of this research was to identify single nucleotide polymorphisms (SNPs) and to develop an Illumina Infinium BeadChip that contained over 50,000 SNPs from soybean (Glycine max L. Merr.). A total of 498,921,777 reads 35–45bp in length were obtained from DNA sequence analysis of reduced representation libraries from several soybean accessions which included six cultivated and two wild soybean (G. soja Sieb. et Zucc.) genotypes. These reads were mapped to the soybean whole genome sequence and 209,903 SNPs were identified. After applying several filters, a total of 146,161 of the 209,903 SNPs were determined to be ideal candidates for Illumina Infinium II BeadChip design. To equalize the distance between selected SNPs, increase assay success rate, and minimize the number of SNPs with low minor allele frequency, an iteration algorithm based on a selection index was developed and used to select 60,800 SNPs for Infinium BeadChip design. Of the 60,800 SNPs, 50,701 were targeted to euchromatic regions and 10,000 to heterochromatic regions of the 20 soybean chromosomes. In addition, 99 SNPs were targeted to unanchored sequence scaffolds. Of the 60,800 SNPs, a total of 52,041 passed Illumina’s manufacturing phase to produce the SoySNP50K iSelect BeadChip. Validation of the SoySNP50K chip with 96 landrace genotypes, 96 elite cultivars and 96 wild soybean accessions showed that 47,337 SNPs were polymorphic and generated successful SNP allele calls. In addition, 40,841 of the 47,337 SNPs (86%) had minor allele frequencies ≥10% among the landraces, elite cultivars and the wild soybean accessions. A total of 620 and 42 candidate regions which may be associated with domestication and recent selection were identified, respectively. The SoySNP50K iSelect SNP beadchip will be a powerful tool for characterizing soybean genetic diversity and linkage disequilibrium, and for constructing high resolution linkage maps to improve the soybean whole genome sequence assembly.     

Bridging the genotyping gap: using genotyping by sequencing (GBS) to add high-density SNP markers and new value to traditional bi-parental mapping and breeding populations

Genotyping by sequencing (GBS) is the latest application of next-generation sequencing protocols for the purposes of discovering and genotyping SNPs in a variety of crop species and populations. Unlike other high-density genotyping technologies which have mainly been applied to general interest “reference” genomes, the low cost of GBS makes it an attractive means of saturating mapping and breeding populations with a high density of SNP markers. One barrier to the widespread use of GBS has been the difficulty of the bioinformatics analysis as the approach is accompanied by a high number of erroneous SNP calls which are not easily diagnosed or corrected. In this study, we use a 384-plex GBS protocol to add 30,984 markers to an indica (IR64) × japonica (Azucena) mapping population consisting of 176 recombinant inbred lines of rice (Oryza sativa) and we release our imputation and error correction pipeline to address initial GBS data sparsity and error, and streamline the process of adding SNPs to RIL populations. Using the final imputed and corrected dataset of 30,984 markers, we were able to map recombination hot and cold spots and regions of segregation distortion across the genome with a high degree of accuracy, thus identifying regions of the genome containing putative sterility loci. We mapped QTL for leaf width and aluminum tolerance, and were able to identify additional QTL for both phenotypes when using the full set of 30,984 SNPs that were not identified using a subset of only 1,464 SNPs, including a previously unreported QTL for aluminum tolerance located directly within a recombination hotspot on chromosome 1. These results suggest that adding a high density of SNP markers to a mapping or breeding population through GBS has a great value for numerous applications in rice breeding and genetics research.     

High Concordance of Bovine Single Nucleotide Polymorphism Genotypes Generated Using Two Independent Genotyping Strategies

Single nucleotide polymorphisms (SNPs) represent the most common form of DNA sequence variation in mammalian livestock genomes. While the past decade has witnessed major advances in SNP genotyping technologies, genotyping errors caused, in part, by the biochemistry underlying the genotyping platform used, can occur. These errors can distort project results and conclusions and can result in incorrect decisions in animal management and breeding programs; hence, SNP genotype calls must be accurate and reliable. In this study, 263 Bos spp. samples were genotyped commercially for a total of 16 SNPs. Of the total possible 4,208 SNP genotypes, 4,179 SNP genotypes were generated, yielding a genotype call rate of 99.31% (standard deviation ± 0.93%). Between 110 and 263 samples were subsequently re-genotyped by us for all 16 markers using a custom-designed SNP genotyping platform, and of the possible 3,819 genotypes a total of 3,768 genotypes were generated (98.70% genotype call rate, SD ± 1.89%). A total of 3,744 duplicate genotypes were generated for both genotyping platforms, and comparison of the genotype calls for both methods revealed 3,741 concordant SNP genotype call rates (99.92% SNP genotype concordance rate). These data indicate that both genotyping methods used can provide livestock geneticists with reliable, reproducible SNP genotypic data for in-depth statistical analysis.     

Using genotyping‐by‐sequencing to predict gender in animals

Gender assignment errors are common in some animal species and lead to inaccuracies in downstream analyses. Procedures for detecting gender misassignment are available for array‐based SNP data but are still being developed for genotyping‐by‐sequencing (GBS) data. In this study, we describe a method for using GBS data to predict gender using X and Y chromosomal SNPs. From a set of 1286 X chromosomal and 23 Y chromosomal deer (Cervus sp.) SNPs discovered from GBS sequence reads, a prediction model was built using a training dataset of 422 Red deer and validated using a test dataset of 868 Red deer and Wapiti deer. Prediction was based on the proportion of heterozygous genotypes on the X chromosome and the proportion of non‐missing genotypes on the Y chromosome observed in each individual. The concordance between recorded gender and predicted gender was 98.6% in the training dataset and 99.3% in the test dataset. The model identified five individuals across both datasets with incorrect recorded gender and was unable to predict gender for another five individuals. Overall, our method predicted gender with a high degree of accuracy and could be used for quality control in gender assignment datasets or for assigning gender when unrecorded, provided a suitable reference genome is available.     

Genotyping by sequencing for genomic prediction in a soybean breeding population

Background

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations.

Results

Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller.

Conclusions

Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.     

Genome-wide identification of SNPs and copy number variation in common bean (<Emphasis Type="Italic">Phaseolus vulgaris</Emphasis> L.) using genotyping-by-sequencing (GBS)

Next-generation sequencing technologies have increased markedly the throughput of genetic studies, allowing the identification of several thousands of SNPs within a single experiment. Even though sequencing cost is rapidly decreasing, the price for whole-genome re-sequencing of a large number of individuals is still costly, especially in plants with a large and highly redundant genome. In recent years, several reduced representation library approaches have been developed for reducing the sequencing cost per individual. Among them, genotyping-by-sequencing (GBS) represents a simple, cost-effective, and highly multiplexed alternative for species with or without an available reference genome. However, this technology requires specific optimization for each species, especially for the restriction enzyme (RE) used. Here we report on the application of GBS in a test experiment with 18 genotypes of wild and domesticated Phaseolus vulgaris. After an in silico digestion with different RE of the P. vulgaris genome reference sequence, we selected CviAII as the most suitable RE for GBS in common bean based on the high frequency and even distribution of restriction sites. A total of 44,875 SNPs, 1940 deletions, and 1693 insertions were identified, with 50 % of the variants located in genic sequences and tagging 11,027 genes. SNP and InDel distributions were positively correlated with gene density across the genome. In addition, we were able to also identify putative copy number variations of genomic segments between different genotypes. In conclusion, GBS with the CviAII enzyme results in thousands of evenly spaced markers and provides a reliable, high-throughput, and cost-effective approach for genotyping both wild and domesticated common beans.     

高通量飞行时间质谱基因分型方法的研究

为了考察飞行时间质谱基因分型方法 (MALDI-TOF) 的位点分型成功率和分型结果质量的关系,分析了 96 个 SNPs 位点的近 10 000 个基因分型数据 (用 MALDI-TOF “4 重”实验方法检测 ). 结果显示,位点分型成功率和分型结果的质量显著正相关 . 分型成功率低于 82% 的 SNP 位点,其高质量结果占的比例开始逐渐降低 . 提示 82% 的分型成功率可以作为衡量分型结果质量的数据点 . 为了进一步提高通量并降低成本,在 MALDI-TOF “ 4 重”实验方法的基础上,发展了两种“准 8 重”实验方法 . 用新的实验方法检测了 95 个样本的 32 个 SNPs 位点 . 结果显示“混合准 8 重”实验方法与“ 4 重”实验方法相比无显著差异,而“复点准 8 重”的结果差于“ 4 重”分型方法 .     

Validation of Genotyping-By-Sequencing Analysis in Populations of Tetraploid Alfalfa by 454 Sequencing

Genotyping-by-sequencing (GBS) is a relatively low-cost high throughput genotyping technology based on next generation sequencing and is applicable to orphan species with no reference genome. A combination of genome complexity reduction and multiplexing with DNA barcoding provides a simple and affordable way to resolve allelic variation between plant samples or populations. GBS was performed on ApeKI libraries using DNA from 48 genotypes each of two heterogeneous populations of tetraploid alfalfa (Medicago sativa spp. sativa): the synthetic cultivar Apica (ATF0) and a derived population (ATF5) obtained after five cycles of recurrent selection for superior tolerance to freezing (TF). Nearly 400 million reads were obtained from two lanes of an Illumina HiSeq 2000 sequencer and analyzed with the Universal Network-Enabled Analysis Kit (UNEAK) pipeline designed for species with no reference genome. Following the application of whole dataset-level filters, 11,694 single nucleotide polymorphism (SNP) loci were obtained. About 60% had a significant match on the Medicago truncatula syntenic genome. The accuracy of allelic ratios and genotype calls based on GBS data was directly assessed using 454 sequencing on a subset of SNP loci scored in eight plant samples. Sequencing depth in this study was not sufficient for accurate tetraploid allelic dosage, but reliable genotype calls based on diploid allelic dosage were obtained when using additional quality filtering. Principal Component Analysis of SNP loci in plant samples revealed that a small proportion (<5%) of the genetic variability assessed by GBS is able to differentiate ATF0 and ATF5. Our results confirm that analysis of GBS data using UNEAK is a reliable approach for genome-wide discovery of SNP loci in outcrossed polyploids.     

Evaluating the effects of imputation on the power, coverage, and cost efficiency of genome-wide SNP platforms

Genotype imputation is potentially a zero-cost method for bridging gaps in coverage and power between genotyping platforms. Here, we quantify these gains in power and coverage by using 1,376 population controls that are from the 1958 British Birth Cohort and were genotyped by the Wellcome Trust Case-Control Consortium with the Illumina HumanHap 550 and Affymetrix SNP Array 5.0 platforms. Approximately 50% of genotypes at single-nucleotide polymorphisms (SNPs) exclusively on the HumanHap 550 can be accurately imputed from direct genotypes on the SNP Array 5.0 or Illumina HumanHap 300. This roughly halves differences in coverage and power between the platforms. When the relative cost of currently available genome-wide SNP platforms is accounted for, and finances are limited but sample size is not, the highest-powered strategy in European populations is to genotype a larger number of individuals with the HumanHap 300 platform and carry out imputation. Platforms consisting of around 1 million SNPs offer poor cost efficiency for SNP association in European populations.     

SNP discovery in wild and domesticated populations of blue catfish,Ictalurus furcatus,using genotyping‐by‐sequencing and subsequent SNP validation

Blue catfish, Ictalurus furcatus, are valued in the United States as a trophy fishery for their capacity to reach large sizes, sometimes exceeding 45 kg. Additionally, blue catfish × channel catfish (I. punctatus) hybrid food fish production has recently increased the demand for blue catfish broodstock. However, there has been little study of the genetic impacts and interaction of farmed, introduced and stocked populations of blue catfish. We utilized genotyping‐by‐sequencing (GBS) to capture and genotype SNP markers on 190 individuals from five wild and domesticated populations (Mississippi River, Missouri, D&B, Rio Grande and Texas). Stringent filtering of SNP‐calling parameters resulted in 4275 SNP loci represented across all five populations. Population genetics and structure analyses revealed potential shared ancestry and admixture between populations. We utilized the Sequenom MassARRAY to validate two multiplex panels of SNPs selected from the GBS data. Selection criteria included SNPs shared between populations, SNPs specific to populations, number of reads per individual and number of individuals genotyped by GBS. Putative SNPs were validated in the discovery population and in two additional populations not used in the GBS analysis. A total of 64 SNPs were genotyped successfully in 191 individuals from nine populations. Our results should guide the development of highly informative, flexible genotyping multiplexes for blue catfish from the larger GBS SNP set as well as provide an example of a rapid, low‐cost approach to generate and genotype informative marker loci in aquatic species with minimal previous genetic information.     

High-Throughput SNP Discovery and Genotyping for Constructing a Saturated Linkage Map of Chickpea (Cicer arietinum L.)

The present study reports the large-scale discovery of genome-wide single-nucleotide polymorphisms (SNPs) in chickpea, identified mainly through the next generation sequencing of two genotypes, i.e. Cicer arietinum ICC4958 and its wild progenitor C. reticulatum PI489777, parents of an inter-specific reference mapping population of chickpea. Development and validation of a high-throughput SNP genotyping assay based on Illumina''s GoldenGate Genotyping Technology and its application in building a high-resolution genetic linkage map of chickpea is described for the first time. In this study, 1022 SNPs were identified, of which 768 high-confidence SNPs were selected for designing the custom Oligo Pool All (CpOPA-I) for genotyping. Of these, 697 SNPs could be successfully used for genotyping, demonstrating a high success rate of 90.75%. Genotyping data of the 697 SNPs were compiled along with those of 368 co-dominant markers mapped in an earlier study, and a saturated genetic linkage map of chickpea was constructed. One thousand and sixty-three markers were mapped onto eight linkage groups spanning 1808.7 cM (centiMorgans) with an average inter-marker distance of 1.70 cM, thereby representing one of the most advanced maps of chickpea. The map was used for the synteny analysis of chickpea, which revealed a higher degree of synteny with the phylogenetically close Medicago than with soybean. The first set of validated SNPs and map resources developed in this study will not only facilitate QTL mapping, genome-wide association analysis and comparative mapping in legumes but also help anchor scaffolds arising out of the whole-genome sequencing of chickpea.