Exploring the Genetic Characteristics of Two Recombinant Inbred Line Populations via High-Density SNP Markers in Maize

Understanding genetic characteristics can reveal the genetic diversity in maize and be used to explore evolutionary mechanisms and gene cloning. A high-density linkage map was constructed to determine recombination rates (RRs), segregation distortion regions (SDRs), and recombinant blocks (RBs) in two recombinant inbred line populations (RILs) (B73/By804 and Zong3/87-1) generated by the single seed descent method. Population B73/By804 containing 174 lines were genotyped with 198 simple sequence repeats (SSRs) markers while population Zong3/87-1 comprised of 175 lines, were genotyped with 210 SSR markers along with 1536 single nucleotide polymorphism (SNP) markers for each population, spanning 1526.7 cM and 1996.2 cM in the B73/By804 and Zong3/87-1 populations, respectively. The total variance of the RR in the whole genome was nearly 100 fold, and the maximum average was 10.43–11.50 cM/Mb while the minimum was 0.08–0.10 cM/Mb in the two populations. The average number of RB was 44 and 37 in the Zong3/87-1 and B73/By804 populations, respectively, whereas 28 SDRs were observed in both populations. We investigated 11 traits in Zong3/87-1 and 10 traits in B73/By804. Quantitative trait locus (QTLs) mapping of SNP+SSR with SNP and SSR marker sets were compared to showed the impact of different density markers on QTL mapping and resolution. The confidence interval of QTL Pa19 (FatB gene controlling palmitic acid content) was reduced from 3.5 Mb to 1.72 Mb, and the QTL Oil6 (DGAT1-2 gene controlling oil concentration) was significantly reduced from 10.8 Mb to 1.62 Mb. Thus, the use of high-density markers considerably improved QTL mapping resolution. The genetic information resulting from this study will support forthcoming efforts to understand recombination events, SDRs, and variations among different germplasm. Furthermore, this study will facilitate gene cloning and understanding of the fundamental sources of total variation and RR in maize, which is the most widely cultivated cereal crop.     

High-throughput genotyping with the GoldenGate assay in the complex genome of soybean

Large numbers of single nucleotide polymorphism (SNP) markers are now available for a number of crop species. However, the high-throughput methods for multiplexing SNP assays are untested in complex genomes, such as soybean, that have a high proportion of paralogous genes. The Illumina GoldenGate assay is capable of multiplexing from 96 to 1,536 SNPs in a single reaction over a 3-day period. We tested the GoldenGate assay in soybean to determine the success rate of converting verified SNPs into working assays. A custom 384-SNP GoldenGate assay was designed using SNPs that had been discovered through the resequencing of five diverse accessions that are the parents of three recombinant inbred line (RIL) mapping populations. The 384 SNPs that were selected for this custom assay were predicted to segregate in one or more of the RIL mapping populations. Allelic data were successfully generated for 89% of the SNP loci (342 of the 384) when it was used in the three RIL mapping populations, indicating that the complex nature of the soybean genome had little impact on conversion of the discovered SNPs into usable assays. In addition, 80% of the 342 mapped SNPs had a minor allele frequency >10% when this assay was used on a diverse sample of Asian landrace germplasm accessions. The high success rate of the GoldenGate assay makes this a useful technique for quickly creating high density genetic maps in species where SNP markers are rapidly becoming available. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Mention of a trade name, proprietary product, or specific equipment does not constitute a guarantee or warranty by the USDA and does not imply approval of a product to the exclusion of others that may be suitable.     

Development and characterisation of an expressed sequence tags (EST)-derived single nucleotide polymorphisms (SNPs) resource in rainbow trout

ABSTRACT: BACKGROUND: There is considerable interest in developing high-throughput genotyping with singlenucleotide polymorphisms (SNPs) for the identification of genes affecting importantecological or economical traits. SNPs are evenly distributed throughout the genome and arelikely to be functionally relevant. In rainbow trout, in silico screening of EST databasesrepresents an attractive approach for de novo SNP identification. Nevertheless, ESTsequencing errors and assembly of EST paralogous sequences can lead to the identification offalse positive SNPs which renders the reliability of EST-derived SNPs relatively low. Furthervalidation of EST-derived SNPs is therefore required. The objective of this work was toassess the quality of and to validate a large number of rainbow trout EST-derived SNPs. RESULTS: A panel of 1,152 EST-derived SNPs was selected from the INRA Sigenae SNP database andwas genotyped in standard and double haploid individuals from several populations using theIllumina GoldenGate BeadXpress assay. High-quality genotyping data were obtained for 958 SNPs representing a genotyping success rate of 83.2 %, out of which, 350 SNPs (36.5 %)were polymorphic in at least one population and were designated as true SNPs. They alsoproved to be a potential tool to investigate genetic diversity of the species, as the set of SNPsuccessfully sorted individuals into three main groups using STRUCTURE software.Functional annotations revealed 28 non-synonymous SNPs, out of which four substitutionswere predicted to affect protein functions. A subset of 223 true SNPs were polymorphic inthe two INRA mapping reference families and were integrated into the INRA microsatellitebasedlinkage map. CONCLUSIONS: Our results represent the first study of EST-derived SNPs validation in rainbow trout, aspecies whose genome sequences is not yet available. We designed several specific filters inorder to improve the genotyping yield. Nevertheless, our selection criteria should be furtherimproved in order to reduce the observed high rate of false positive SNPs which results fromthe occurrence of whole genome duplications.     

High-throughput SNP discovery and genotyping in durum wheat (<Emphasis Type="Italic">Triticum durum</Emphasis> Desf.)

We describe the application of complexity reduction of polymorphic sequences (CRoPS^®) technology for the discovery of SNP markers in tetraploid durum wheat (Triticum durum Desf.). A next-generation sequencing experiment was carried out on reduced representation libraries obtained from four durum cultivars. SNP validation and minor allele frequency (MAF) estimate were carried out on a panel of 12 cultivars, and the feasibility of genotyping these SNPs in segregating populations was tested using the Illumina Golden Gate (GG) technology. A total of 2,659 SNPs were identified on 1,206 consensus sequences. Among the 768 SNPs that were chosen irrespective of their genomic repetitiveness level and assayed on the Illumina BeadExpress genotyping system, 275 (35.8%) SNPs matched the expected genotypes observed in the SNP discovery phase. MAF data indicated that the overall SNP informativeness was high: a total of 196 (71.3%) SNPs had MAF >0.2, of which 76 (27.6%) showed MAF >0.4. Of these SNPs, 157 were mapped in one of two mapping populations (Meridiano × Claudio and Colosseo × Lloyd) and integrated into a common genetic map. Despite the relatively low genotyping efficiency of the GG assay, the validated CRoPS-derived SNPs showed valuable features for genomics and breeding applications such as a uniform distribution across the wheat genome, a prevailing single-locus codominant nature and a high polymorphism. Here, we report a new set of 275 highly robust genome-wide Triticum SNPs that are readily available for breeding purposes.     

SNP genotyping in melons: genetic variation, population structure, and linkage disequilibrium

Novel sequencing technologies were recently used to generate sequences from multiple melon (Cucumis melo L.) genotypes, enabling the in silico identification of large single nucleotide polymorphism (SNP) collections. In order to optimize the use of these markers, SNP validation and large-scale genotyping are necessary. In this paper, we present the first validated design for a genotyping array with 768 SNPs that are evenly distributed throughout the melon genome. This customized Illumina GoldenGate assay was used to genotype a collection of 74 accessions, representing most of the botanical groups of the species. Of the assayed loci, 91 % were successfully genotyped. The array provided a large number of polymorphic SNPs within and across accessions. This set of SNPs detected high levels of variation in accessions from this crop’s center of origin as well as from several other areas of melon diversification. Allele distribution throughout the genome revealed regions that distinguished between the two main groups of cultivated accessions (inodorus and cantalupensis). Population structure analysis showed a subdivision into five subpopulations, reflecting the history of the crop. A considerably low level of LD was detected, which decayed rapidly within a few kilobases. Our results show that the GoldenGate assay can be used successfully for high-throughput SNP genotyping in melon. Since many of the genotyped accessions are currently being used as the parents of breeding populations in various programs, this set of mapped markers could be used for future mapping and breeding efforts.     

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.     

Highly efficient genotyping of rice biparental populations by GoldenGate assays based on parental resequencing

Key message

A new time- and cost-effective strategy was developed for medium-density SNP genotyping of rice biparental populations, using GoldenGate assays based on parental resequencing.

Abstract

Since the advent of molecular markers, crop researchers and breeders have dedicated huge amounts of effort to detecting quantitative trait loci (QTL) in biparental populations for genetic analysis and marker-assisted selection (MAS). In this study, we developed a new time- and cost-effective strategy for genotyping a population of progeny from a rice cross using medium-density single nucleotide polymorphisms (SNPs). Using this strategy, 728,362 “high quality” SNPs were identified by resequencing Teqing and Lemont, the parents of the population. We selected 384 informative SNPs that were evenly distributed across the genome for genotyping the biparental population using the Illumina GoldenGate assay. 335 (87.2 %) validated SNPs were used for further genetic analyses. After removing segregation distortion markers, 321 SNPs were used for linkage map construction and QTL mapping. This strategy generated SNP markers distributed more evenly across the genome than previous SSR assays. Taking the GW5 gene that controls grain shape as an example, our strategy provided higher accuracy (0.8 Mb) and significance (LOD 5.5 and 10.1) in QTL mapping than SSR analysis. Our study thus provides a rapid and efficient strategy for genetic studies and QTL mapping using SNP genotyping assays.     

ZmcrtRB3 Encodes a Carotenoid Hydroxylase that Affects the Accumulation of α-carotene in Maize Kernel

α-carotene is one of the important components of pro-vitamin A,which is able to be converted into vitamin A in the human body.One maize(Zea mays L.) ortholog of carotenoid hydroxylases in Arabidopsis thaliana,ZmcrtRB3,was cloned and its role in carotenoid hydrolyzations was addressed.ZmcrtRB3 was mapped in a quantitative trait locus(QTL) cluster for carotenoid-related traits on chromosome 2(bin 2.03) in a recombinant inbred line(RIL) population derived from By804 and B73.Candidate-gene association analysis identified 18 polymorphic sites in ZmcrtRB3 significantly associated with one or more carotenoid-related traits in 126 diverse yellow maize inbred lines.These results indicate that the enzyme ZmcrtRB3 plays a role in hydrolyzing both α-and β-carotenes,while polymorphisms in ZmcrtRB3 contributed more variation in α-carotene than that in β-carotene.Two single nucleotide polymorphisms(SNPs),SNP1343 in 5 untranslated region and SNP2172 in the second intron,consistently had effects on α-carotene content and composition with explained phenotypic variations ranging from 8.7% to 34.8%.There was 1.7-to 3.7-fold change between the inferior and superior haplotype for α-carotene content and composition.Thus,SNP1343 and SNP2172 are potential polymorphic sites to develop functional markers for applying marker-assisted selection in the improvement of pro-vitamin A carotenoids in maize kernels.     

High-throughput single nucleotide polymorphism (SNP) identification and mapping in the sesame (<Emphasis Type="Italic">Sesamum indicum</Emphasis> L.) genome with genotyping by sequencing (GBS) analysis

Sesame (Sesamum indicum L. syn. Sesamum orientale L.) is considered to be the first oil seed crop known to man. Despite its versatile use as an oil seed and a leafy vegetable, sesame is a neglected crop and has not been a subject of molecular genetic research until the last decade. There is thus limited knowledge regarding genome-specific molecular markers that are indispensible for germplasm enhancement, gene identification, and marker-assisted breeding in sesame. In this study, we employed a genotyping by sequencing (GBS) approach to a sesame recombinant inbred line (RIL) population for high-throughput single nucleotide polymorphism (SNP) identification and genotyping. A total of 15,521 SNPs were identified with 14,786 SNPs (95.26 %) located along sesame genome assembly pseudomolecules. By incorporating sesame-specific simple sequence repeat (SSR) markers developed in our previous work, 230.73 megabases (99 %) of sequence from the genome assembly were saturated with markers. This large number of markers will be available for sesame geneticists as a resource for candidate polymorphisms located along the physical chromosomes of sesame. Defining SNP loci in genome assembly sequences provides the flexibility to utilize any genotyping strategy to survey any sesame population. SNPs selected through a high stringency filtering protocol (770 SNPs) for improved map accuracy were used in conjunction with SSR markers (50 SSRs) in linkage analysis, resulting in 13 linkage groups that encompass a total genetic distance of 914 cM with 432 markers (420 SNPs, 12 SSRs). The genetic linkage map constitutes the basis for future work that will involve quantitative trait locus (QTL) analyses of metabolic and agronomic traits in the segregating RIL population.     

Comparison of SSRs and SNPs in assessment of genetic relatedness in maize

Advances in high-throughput SNP genotyping and genome sequencing technologies have enabled genome-wide association mapping in dissecting the genetic basis of complex quantitative traits. In this study, 82 SSRs and 884 SNPs with minor allele frequencies (MAF) over 0.20 were used to compare their ability to assess population structure, principal component analysis (PCA) and relative kinship in a maize association panel consisting of 154 inbred lines. Compared to SNPs, SSRs provided more information on genetic diversity. The expected heterozygosity (He) of SSRs and SNPs averaged 0.65 and 0.44, and the polymorphic information content of these two markers was 0.61 and 0.34 in this panel, respectively. Additionally, SSRs performed better at clustering all lines into groups using STRUCTURE and PCA approaches, and estimating relative kinship. For both marker systems, the same clusters were observed based on PCA and the first two eigenvectors accounted for similar percentage of genetic variations in this panel. The correlation coefficients of each eigenvector from SSRs and SNPs decreased sharply when the eigenvector varied from 1 to 3, but kept around 0 when the eigenvector were over 3. The kinship estimates based on SSRs and SNPs were moderately correlated (r (2)?=?0.69). All these results suggest that SSR markers with moderate density are more informative than SNPs for assessing genetic relatedness in maize association mapping panels.     

Single nucleotide polymorphisms in Cryptomeria japonica: their discovery and validation for genome mapping and diversity studies

In order to develop a large set of single-nucleotide polymorphisms (SNPs) in Cryptomeria japonica, for a wide range of applications, we adopted a systematic EST (expressed sequence tags) re-sequencing approach. We examined a group of four genotypes comprising parents of a mapping population as well as representatives of two main lines from natural populations. We sequenced 5,170 gene fragments, representing analysis of over 1.3?Mb of DNA sequences in C. japonica. This analysis leads to the discovery of 13,413 SNPs in 3,744 amplicons, with an average of one SNP for every 101.0?bp (one SNP for every 78.3?bp in introns and for every 106.7?bp in exon regions). Nucleotide diversity in C. japonica (???=?0.0045) was found to be similar to values recorded in highly polymorphic forest tree species such as pine. We also validated the use of the SNPs as molecular markers for genetic diversity studies using the high throughput SNP genotyping platform GoldenGate. From 1,536 candidate SNP sites tested, 1,164 (75.8?%) were confirmed to be polymorphic. We anticipate that the genome-wide SNP markers reported here will be useful for evaluating the species?? range-wide genetic structure and in marker-assisted selection used as part of the C. japonica tree improvement program.     

An EST-derived SNP and SSR genetic linkage map of cassava (Manihot esculenta Crantz)

Cassava (Manihot esculenta Crantz) is one of the most important food security crops in the tropics and increasingly being adopted for agro-industrial processing. Genetic improvement of cassava can be enhanced through marker-assisted breeding. For this, appropriate genomic tools are required to dissect the genetic architecture of economically important traits. Here, a genome-wide SNP-based genetic map of cassava anchored in SSRs is presented. An outbreeder full-sib (F1) family was genotyped on two independent SNP assay platforms: an array of 1,536 SNPs on Illumina's GoldenGate platform was used to genotype a first batch of 60 F1. Of the 1,358 successfully converted SNPs, 600 which were polymorphic in at least one of the parents and was subsequently converted to KBiosciences' KASPar assay platform for genotyping 70 additional F1. High-precision genotyping of 163 informative SSRs using capillary electrophoresis was also carried out. Linkage analysis resulted in a final linkage map of 1,837 centi-Morgans (cM) containing 568 markers (434 SNPs and 134 SSRs) distributed across 19 linkage groups. The average distance between adjacent markers was 3.4?cM. About 94.2% of the mapped SNPs and SSRs have also been localized on scaffolds of version 4.1 assembly of the cassava draft genome sequence. This more saturated genetic linkage map of cassava that combines SSR and SNP markers should find several applications in the improvement of cassava including aligning scaffolds of the cassava genome sequence, genetic analyses of important agro-morphological traits, studying the linkage disequilibrium landscape and comparative genomics.     

The genetic architecture of amino acids dissection by association and linkage analysis in maize

Amino acids are both constituents of proteins, providing the essential nutrition for humans and animals, and signalling molecules regulating the growth and development of plants. Most cultivars of maize are deficient in essential amino acids such as lysine and tryptophan. Here, we measured the levels of 17 different total amino acids, and created 48 derived traits in mature kernels from a maize diversity inbred collection and three recombinant inbred line (RIL) populations. By GWAS, 247 and 281 significant loci were identified in two different environments, 5.1 and 4.4 loci for each trait, explaining 7.44% and 7.90% phenotypic variation for each locus in average, respectively. By linkage mapping, 89, 150 and 165 QTLs were identified in B73/By804, Kui3/B77 and Zong3/Yu87‐1 RIL populations, 2.0, 2.7 and 2.8 QTLs for each trait, explaining 13.6%, 16.4% and 21.4% phenotypic variation for each QTL in average, respectively. It implies that the genetic architecture of amino acids is relative simple and controlled by limited loci. About 43.2% of the loci identified by GWAS were verified by expression QTL, and 17 loci overlapped with mapped QTLs in the three RIL populations. GRMZM2G015534, GRMZM2G143008 and one QTL were further validated using molecular approaches. The amino acid biosynthetic and catabolic pathways were reconstructed on the basis of candidate genes proposed in this study. Our results provide insights into the genetic basis of amino acid biosynthesis in maize kernels and may facilitate marker‐based breeding for quality protein maize.     

Genomic Selection Using Low-Density Marker Panels

Genomic selection (GS) using high-density single-nucleotide polymorphisms (SNPs) is promising to improve response to selection in populations that are under artificial selection. High-density SNP genotyping of all selection candidates each generation, however, may not be cost effective. Smaller panels with SNPs that show strong associations with phenotype can be used, but this may require separate SNPs for each trait and each population. As an alternative, we propose to use a panel of evenly spaced low-density SNPs across the genome to estimate genome-assisted breeding values of selection candidates in pedigreed populations. The principle of this approach is to utilize cosegregation information from low-density SNPs to track effects of high-density SNP alleles within families. Simulations were used to analyze the loss of accuracy of estimated breeding values from using evenly spaced and selected SNP panels compared to using all high-density SNPs in a Bayesian analysis. Forward stepwise selection and a Bayesian approach were used to select SNPs. Loss of accuracy was nearly independent of the number of simulated quantitative trait loci (QTL) with evenly spaced SNPs, but increased with number of QTL for the selected SNP panels. Loss of accuracy with evenly spaced SNPs increased steadily over generations but was constant when the smaller number individuals that are selected for breeding each generation were also genotyped using the high-density SNP panel. With equal numbers of low-density SNPs, panels with SNPs selected on the basis of the Bayesian approach had the smallest loss in accuracy for a single trait, but a panel with evenly spaced SNPs at 10 cM was only slightly worse, whereas a panel with SNPs selected by forward stepwise selection was inferior. Panels with evenly spaced SNPs can, however, be used across traits and populations and their performance is independent of the number of QTL affecting the trait and of the methods used to estimate effects in the training data and are, therefore, preferred for broad applications in pedigreed populations under artificial selection.     

SNP discovery by amplicon sequencing and multiplex SNP genotyping in the allopolyploid species Brassica napus

Oilseed rape (Brassica napus) is an allotetraploid species consisting of two genomes, derived from B. rapa (A genome) and B. oleracea (C genome). The presence of these two genomes makes single nucleotide polymorphism (SNP) marker identification and SNP analysis more challenging than in diploid species, as for a given locus usually two versions of a DNA sequence (based on the two ancestral genomes) have to be analyzed simultaneously during SNP identification and analysis. One hundred amplicons derived from expressed sequence tag (ESTs) were analyzed to identify SNPs in a panel of oilseed rape varieties and within two sister species representing the ancestral genomes. A total of 604 SNPs were identified, averaging one SNP in every 42 bp. It was possible to clearly discriminate SNPs that are polymorphic between different plant varieties from SNPs differentiating the two ancestral genomes. To validate the identified SNPs for their use in genetic analysis, we have developed Illumina GoldenGate assays for some of the identified SNPs. Through the analysis of a number of oilseed rape varieties and mapping populations with GoldenGate assays, we were able to identify a number of different segregation patterns in allotetraploid oilseed rape. The majority of the identified SNP markers can be readily used for genetic mapping, showing that amplicon sequencing and Illumina GoldenGate assays can be used to reliably identify SNP markers in tetraploid oilseed rape and to convert them into successful SNP assays that can be used for genetic analysis.     

High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform

Multiplexed single nucleotide polymorphism (SNP) markers have the potential to increase the speed and cost-effectiveness of genotyping, provided that an optimal SNP density is used for each application. To test the efficiency of multiplexed SNP genotyping for diversity, mapping and breeding applications in rice (Oryza sativa L.), we designed seven GoldenGate VeraCode oligo pool assay (OPA) sets for the Illumina BeadXpress Reader. Validated markers from existing 1536 Illumina SNPs and 44?K Affymetrix SNP chips developed at Cornell University were used to select subsets of informative SNPs for different germplasm groups with even distribution across the genome. A 96-plex OPA was developed for quality control purposes and for assigning a sample into one of the five O. sativa population subgroups. Six 384-plex OPAs were designed for genetic diversity analysis, DNA fingerprinting, and to have evenly-spaced polymorphic markers for quantitative trait locus (QTL) mapping and background selection for crosses between different germplasm pools in rice: Indica/Indica, Indica/Japonica, Japonica/Japonica, Indica/O. rufipogon, and Japonica/O. rufipogon. After testing on a diverse set of rice varieties, two of the SNP sets were re-designed by replacing poor-performing SNPs. Pilot studies were successfully performed for diversity analysis, QTL mapping, marker-assisted backcrossing, and developing specialized genetic stocks, demonstrating that 384-plex SNP genotyping on the BeadXpress platform is a robust and efficient method for marker genotyping in rice.     

Identification of functional genetic variations underlying drought tolerance in maize using SNP markers

Single nucleotide polymorphism (SNP) is a common form of genetic variation and popularly exists in maize genome. An Illumina GoldenGate assay with 1 536 SNP markers was used to genotype maize inbred lines and identified the functional genetic variations underlying drought tolerance by association analysis. Across 80 lines, 1 006 polymorphic SNPs (65.5% of the total) in the assay with good call quality were used to estimate the pattern of genetic diversity, population structure, and familial relatedness. The analysis showed the best number of fixed subgroups was six, which was consistent with their original sources and results using only simple sequence repeat markers. Pairwise linkage disequilibrium (LD) and association mapping with phenotypic traits investigated under water-stressed and well-watered regimes showed rapid LD decline within 100-500 kb along the physical distance of each chromosome, and that 29 SNPs were associated with at least two phenotypic traits in one or more environments, which were related to drought-tolerant or drought-responsive genes. These drought-tolerant SNPs could be converted into functional markers and then used for maize improvement by marker-assisted selection.     

Single nucleotide polymorphisms for pig identification and parentage exclusion

Single nucleotide polymorphisms (SNPs) have become an important type of marker for commercial diagnostic and parentage genotyping applications as automated genotyping systems have been developed that yield accurate genotypes. Unfortunately, allele frequencies for public SNP markers in commercial pig populations have not been available. To fulfil this need, SNP markers previously mapped in the USMARC swine reference population were tested in a panel of 155 boars that were representative of US purebred Duroc, Hampshire, Landrace and Yorkshire populations. Multiplex assay groups of 5-7 SNP assays/group were designed and genotypes were determined using Sequenom's massarray system. Of 80 SNPs that were evaluated, 60 SNPs with minor allele frequencies >0.15 were selected for the final panel of markers. Overall identity power across breeds was 4.6 x 10(-23), but within-breed values ranged from 4.3 x 10(-14) (Hampshire) to 2.6 x 10(-22) (Yorkshire). Parentage exclusion probability with only one sampled parent was 0.9974 (all data) and ranged from 0.9594 (Hampshire) to 0.9963 (Yorkshire) within breeds. Sire exclusion probability when the dam's genotype was known was 0.99998 (all data) and ranged from 0.99868 (Hampshire) to 0.99997 (Yorkshire) within breeds. Power of exclusion was compared between the 60 SNP and 10 microsatellite markers. The parental exclusion probabilities for SNP and microsatellite marker panels were similar, but the SNP panel was much more sensitive for individual identification. This panel of SNP markers is theoretically sufficient for individual identification of any pig in the world and is publicly available.     

Sequence‐based SNP genotyping in durum wheat

Marker development for marker‐assisted selection in plant breeding is increasingly based on next‐generation sequencing (NGS). However, marker development in crops with highly repetitive, complex genomes is still challenging. Here we applied sequence‐based genotyping (SBG), which couples AFLP^®‐based complexity reduction to NGS, for de novo single nucleotide polymorphisms (SNP) marker discovery in and genotyping of a biparental durum wheat population. We identified 9983 putative SNPs in 6372 contigs between the two parents and used these SNPs for genotyping 91 recombinant inbred lines (RILs). Excluding redundant information from multiple SNPs per contig, 2606 (41%) markers were used for integration in a pre‐existing framework map, resulting in the integration of 2365 markers over 2607 cM. Of the 2606 markers available for mapping, 91% were integrated in the pre‐existing map, containing 708 SSRs, DArT markers, and SNPs from CRoPS technology, with a map‐size increase of 492 cM (23%). These results demonstrate the high quality of the discovered SNP markers. With this methodology, it was possible to saturate the map at a final marker density of 0.8 cM/marker. Looking at the binned marker distribution (Figure 2), 63 of the 268 10‐cM bins contained only SBG markers, showing that these markers are filling in gaps in the framework map. As to the markers that could not be used for mapping, the main reason was the low sequencing coverage used for genotyping. We conclude that SBG is a valuable tool for efficient, high‐throughput and high‐quality marker discovery and genotyping for complex genomes such as that of durum wheat.     

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.