Discovery of SNPs in soybean genotypes frequently used as the parents of mapping populations in the United States and Korea

Single nucleotide polymorphisms (SNPs) including insertion/deletions (indels) serve as useful and informative genetic markers. The availability of high-throughput and inexpensive SNP typing systems has increased interest in the development of SNP markers. After fragments of genes were amplified with primers derived from 110 soybean GenBank ESTs, sequencing data of PCR products from 15 soybean genotypes from Korea and the United States were analyzed by SeqScape software to find SNPs. Among 35 gene fragments with at least one SNP among the 15 genotypes, SNPs occurred at a frequency of 1 per 2,038 bp in 16,302 bp of coding sequence and 1 per 191 bp in 16,960 bp of noncoding regions. This corresponds to a nucleotide diversity (theta) of 0.00017 and 0.00186, respectively. Of the 97 SNPs discovered, 78 or 80.4% were present in the six North American soybean mapping parents. The addition of "Hwaeomputkong," which originated from Japan, increased the number to 92, or 94.8% of the total number of SNPs present among the 15 genotypes. Thus, Hwaeomputkong and the six North American mapping parents provide a diverse set of soybean genotypes that can be successfully used for SNP discovery in coding DNA and closely associated introns and untranslated regions.     

Preselection statistics and Random Forest classification identify population informative single nucleotide polymorphisms in cosmopolitan and autochthonous cattle breeds

Commercial single nucleotide polymorphism (SNP) arrays have been recently developed for several species and can be used to identify informative markers to differentiate breeds or populations for several downstream applications. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this work, we compared several methods of SNPs preselection (Delta, F_st and principal component analyses (PCA)) in addition to Random Forest classifications to analyse SNP data from six dairy cattle breeds, including cosmopolitan (Holstein, Brown and Simmental) and autochthonous Italian breeds raised in two different regions and subjected to limited or no breeding programmes (Cinisara, Modicana, raised only in Sicily and Reggiana, raised only in Emilia Romagna). From these classifications, two panels of 96 and 48 SNPs that contain the most discriminant SNPs were created for each preselection method. These panels were evaluated in terms of the ability to discriminate as a whole and breed-by-breed, as well as linkage disequilibrium within each panel. The obtained results showed that for the 48-SNP panel, the error rate increased mainly for autochthonous breeds, probably as a consequence of their admixed origin lower selection pressure and by ascertaining bias in the construction of the SNP chip. The 96-SNP panels were generally more able to discriminate all breeds. The panel derived by PCA-chrom (obtained by a preselection chromosome by chromosome) could identify informative SNPs that were particularly useful for the assignment of minor breeds that reached the lowest value of Out Of Bag error even in the Cinisara, whose value was quite high in all other panels. Moreover, this panel contained also the lowest number of SNPs in linkage disequilibrium. Several selected SNPs are located nearby genes affecting breed-specific phenotypic traits (coat colour and stature) or associated with production traits. In general, our results demonstrated the usefulness of Random Forest in combination to other reduction techniques to identify population informative SNPs.     

A highly informative SNP linkage panel for human genetic studies

We have developed a highly informative set of single-nucleotide polymorphism (SNP) assays designed for linkage mapping of the human genome. These assays were developed on a robust multiplexed assay system to provide a combination of very high accuracy and data completeness with high throughput for linkage studies. The linkage panel is comprised of approximately 4,700 SNPs with 0.39 average minor allele frequency and 624-kb average spacing. Based on almost 2 million genotypes, data quality was shown to be extremely high, with a 99.94% call rate, >99.99% reproducibility and 99.995% genotypes consistent with mendelian inheritance. We constructed a genetic map with an average 1.5-cM resolution using series of 28 CEPH pedigrees. The relative information content of this panel was higher than those of commonly used STR marker panels. The potent combination of this SNP linkage panel with the multiplexed assay system provides a previously unattainable level of performance for linkage studies.     

A genomewide single-nucleotide-polymorphism panel for Mexican American admixture mapping

For admixture mapping studies in Mexican Americans (MAM), we define a genomewide single-nucleotide-polymorphism (SNP) panel that can distinguish between chromosomal segments of Amerindian (AMI) or European (EUR) ancestry. These studies used genotypes for >400,000 SNPs, defined in EUR and both Pima and Mayan AMI, to define a set of ancestry-informative markers (AIMs). The use of two AMI populations was necessary to remove a subset of SNPs that distinguished genotypes of only one AMI subgroup from EUR genotypes. The AIMs set contained 8,144 SNPs separated by a minimum of 50 kb with only three intermarker intervals >1 Mb and had EUR/AMI FST values >0.30 (mean FST = 0.48) and Mayan/Pima FST values <0.05 (mean FST < 0.01). Analysis of a subset of these SNP AIMs suggested that this panel may also distinguish ancestry between EUR and other disparate AMI groups, including Quechuan from South America. We show, using realistic simulation parameters that are based on our analyses of MAM genotyping results, that this panel of SNP AIMs provides good power for detecting disease-associated chromosomal segments for genes with modest ethnicity risk ratios. A reduced set of 5,287 SNP AIMs captured almost the same admixture mapping information, but smaller SNP sets showed substantial drop-off in admixture mapping information and power. The results will enable studies of type 2 diabetes, rheumatoid arthritis, and other diseases among which epidemiological studies suggest differences in the distribution of ancestry-associated susceptibility.     

A 3.9-centimorgan-resolution human single-nucleotide polymorphism linkage map and screening set

Recent advances in technologies for high-throughout single-nucleotide polymorphism (SNP)-based genotyping have improved efficiency and cost so that it is now becoming reasonable to consider the use of SNPs for genomewide linkage analysis. However, a suitable screening set of SNPs and a corresponding linkage map have yet to be described. The SNP maps described here fill this void and provide a resource for fast genome scanning for disease genes. We have evaluated 6,297 SNPs in a diversity panel composed of European Americans, African Americans, and Asians. The markers were assessed for assay robustness, suitable allele frequencies, and informativeness of multi-SNP clusters. Individuals from 56 Centre d'Etude du Polymorphisme Humain pedigrees, with >770 potentially informative meioses altogether, were genotyped with a subset of 2,988 SNPs, for map construction. Extensive genotyping-error analysis was performed, and the resulting SNP linkage map has an average map resolution of 3.9 cM, with map positions containing either a single SNP or several tightly linked SNPs. The order of markers on this map compares favorably with several other linkage and physical maps. We compared map distances between the SNP linkage map and the interpolated SNP linkage map constructed by the deCode Genetics group. We also evaluated cM/Mb distance ratios in females and males, along each chromosome, showing broadly defined regions of increased and decreased rates of recombination. Evaluations indicate that this SNP screening set is more informative than the Marshfield Clinic's commonly used microsatellite-based screening set.     

Identification of Pummelo Cultivars by Using a Panel of 25 Selected SNPs and 12 DNA Segments

Pummelo cultivars are usually difficult to identify morphologically, especially when fruits are unavailable. The problem was addressed in this study with the use of two methods: high resolution melting analysis of SNPs and sequencing of DNA segments. In the first method, a set of 25 SNPs with high polymorphic information content were selected from SNPs predicted by analyzing ESTs and sequenced DNA segments. High resolution melting analysis was then used to genotype 260 accessions including 55 from Myanmar, and 178 different genotypes were thus identified. A total of 99 cultivars were assigned to 86 different genotypes since the known somatic mutants were identical to their original genotypes at the analyzed SNP loci. The Myanmar samples were genotypically different from each other and from all other samples, indicating they were derived from sexual propagation. Statistical analysis showed that the set of SNPs was powerful enough for identifying at least 1000 pummelo genotypes, though the discrimination power varied in different pummelo groups and populations. In the second method, 12 genomic DNA segments of 24 representative pummelo accessions were sequenced. Analysis of the sequences revealed the existence of a high haplotype polymorphism in pummelo, and statistical analysis showed that the segments could be used as genetic barcodes that should be informative enough to allow reliable identification of 1200 pummelo cultivars. The high level of haplotype diversity and an apparent population structure shown by DNA segments and by SNP genotypes, respectively, were discussed in relation to the origin and domestication of the pummelo species.     

Characterisation of single nucleotide polymorphisms in sugarcane ESTs

Commercial sugarcane cultivars (Saccharum spp. hybrids) are both polyploid and aneuploid with chromosome numbers in excess of 100; these chromosomes can be assigned to 8 homology groups. To determine the utility of single nucleotide polymorphisms (SNPs) as a means of improving our understanding of the complex sugarcane genome, we developed markers to a suite of SNPs identified in a list of sugarcane ESTs. Analysis of 69 EST contigs showed a median of 9 SNPs per EST and an average of 1 SNP per 50 bp of coding sequence. The quantitative presence of each base at 58 SNP loci within 19 contiguous sequence sets was accurately and reliably determined for 9 sugarcane genotypes, including both commercial cultivars and ancestral species, through the use of quantitative light emission technology in pyrophosphate sequencing. Across the 9 genotypes tested, 47 SNP loci were polymorphic and 11 monomorphic. Base frequency at individual SNP loci was found to vary approximately twofold between Australian sugarcane cultivars and more widely between cultivars and wild species. Base quantity was shown to segregate as expected in the IJ76-514 × Q165 sugarcane mapping population, indicating that SNPs that occur on one or two sugarcane chromosomes have the potential to be mapped. The use of SNP base frequencies from five of the developed markers was able to clearly distinguish all genotypes in the population. The use of SNP base frequencies from a further six markers within an EST contig was able to help establish the likely copy number of the locus in two genotypes tested. This is the first instance of a technology that has been able to provide an insight into the copy number of a specific gene locus in hybrid sugarcane. The identification of specific and numerous haplotypes/alleles present in a genotype by pyrophosphate sequencing or alternative techniques ultimately will provide the basis for identifying associations between specific alleles and phenotype and between allele dosage and phenotype in sugarcane.Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

The effect of linkage disequilibrium on linkage analysis of incomplete pedigrees

Dense SNP maps can be highly informative for linkage studies. But when parental genotypes are missing, multipoint linkage scores can be inflated in regions with substantial marker-marker linkage disequilibrium (LD). Such regions were observed in the Affymetrix SNP genotypes for the Genetic Analysis Workshop 14 (GAW14) Collaborative Study on the Genetics of Alcoholism (COGA) dataset, providing an opportunity to test a novel simulation strategy for studying this problem. First, an inheritance vector (with or without linkage present) is simulated for each replicate, i.e., locations of recombinations and transmission of parental chromosomes are determined for each meiosis. Then, two sets of founder haplotypes are superimposed onto the inheritance vector: one set that is inferred from the actual data and which contains the pattern of LD; and one set created by randomly selecting parental alleles based on the known allele frequencies, with no correlation (LD) between markers. Applying this strategy to a map of 176 SNPs (66 Mb of chromosome 7) for 100 replicates of 116 sibling pairs, significant inflation of multipoint linkage scores was observed in regions of high LD when parental genotypes were set to missing, with no linkage present. Similar inflation was observed in analyses of the COGA data for these affected sib pairs with parental genotypes set to missing, but not after reducing the marker map until r2 between any pair of markers was 相似文献   

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.     

High-Throughput RAD-SNP Genotyping for Characterization of Sugar Beet Genotypes

High-throughput single-nucleotide polymorphism (SNP) genotyping provides a rapid way of developing resourceful sets of markers for delineating genetic structure and for understanding the basis of the taxonomic discrimination. In this paper, we present a panel of 192 SNPs for effective genotyping in sugar beet using a high-throughput marker array technology, QuantStudio 12K Flex system, coupled with Taqman OpenArray technology. The selected SNPs were evaluated for genetic diversity among a set of 150 individuals representing 15 genotypes (10 individuals each) from five cytoplasmic male steriles (CMSs), five pollinators, and five commercial varieties. We demonstrated that the proposed panel of 192 SNPs effectively differentiated the studied genotypes. A higher degree of polymorphism was observed among the CMSs as compared to pollinators and commercial varieties. PCoA and STRUCTURE analysis revealed that CMSs, pollinators, and varieties clustered into three distinct subpopulations. Our results demonstrate the utility of the identified panel of 192 SNPs coupled with TaqMan OpenArray technology as a wide set of markers for high-throughput SNP genotyping in sugar beet.     

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.     

A genomewide single-nucleotide-polymorphism panel with high ancestry information for African American admixture mapping

Admixture mapping requires a genomewide panel of relatively evenly spaced markers that can distinguish the ancestral origins of chromosomal segments in admixed individuals. Through use of the results of the International HapMap Project and specific selection criteria, the current study has examined the ability of selected single-nucleotide polymorphisms (SNPs) to extract continental ancestry information in African American subjects and to explore parameters for admixture mapping. Genotyping of two linguistically diverse West African populations (Bini and Kanuri Nigerians, who are Niger-Congo [Bantu] and Nilo-Saharan speakers, respectively), European Americans, and African Americans validated a genomewide set of >4,000 SNP ancestry-informative markers with mean and median F(ST) values >0.59 and mean and median Fisher's information content >2.5. This set of SNPs extracted a larger amount of ancestry information in African Americans than previously reported SNP panels and provides nearly uniform coverage of the genome. Moreover, in the current study, simulations show that this more informative panel improves power for admixture mapping in African Americans when ethnicity risk ratios are modest. This is particularly important in the application of admixture mapping in complex genetic diseases for which only modest ethnicity risk ratios of relevant susceptibility genes are expected.     

SNPs for Parentage Testing and Traceability in Globally Diverse Breeds of Sheep

DNA-based parentage determination accelerates genetic improvement in sheep by increasing pedigree accuracy. Single nucleotide polymorphism (SNP) markers can be used for determining parentage and to provide unique molecular identifiers for tracing sheep products to their source. However, the utility of a particular “parentage SNP” varies by breed depending on its minor allele frequency (MAF) and its sequence context. Our aims were to identify parentage SNPs with exceptional qualities for use in globally diverse breeds and to develop a subset for use in North American sheep. Starting with genotypes from 2,915 sheep and 74 breed groups provided by the International Sheep Genomics Consortium (ISGC), we analyzed 47,693 autosomal SNPs by multiple criteria and selected 163 with desirable properties for parentage testing. On average, each of the 163 SNPs was highly informative (MAF≥0.3) in 48±5 breed groups. Nearby polymorphisms that could otherwise confound genetic testing were identified by whole genome and Sanger sequencing of 166 sheep from 54 breed groups. A genetic test with 109 of the 163 parentage SNPs was developed for matrix-assisted laser desorption/ionization–time-of-flight mass spectrometry. The scoring rates and accuracies for these 109 SNPs were greater than 99% in a panel of North American sheep. In a blinded set of 96 families (sire, dam, and non-identical twin lambs), each parent of every lamb was identified without using the other parent’s genotype. In 74 ISGC breed groups, the median estimates for probability of a coincidental match between two animals (P_I), and the fraction of potential adults excluded from parentage (P_E) were 1.1×10(−39) and 0.999987, respectively, for the 109 SNPs combined. The availability of a well-characterized set of 163 parentage SNPs facilitates the development of high-throughput genetic technologies for implementing accurate and economical parentage testing and traceability in many of the world’s sheep breeds.     

The impact of missing and erroneous genotypes on tagging SNP selection and power of subsequent association tests

OBJECTIVE: Single nucleotide polymorphisms (SNPs) serve as effective markers for localizing disease susceptibility genes, but current genotyping technologies are inadequate for genotyping all available SNP markers in a typical linkage/association study. Much attention has recently been paid to methods for selecting the minimal informative subset of SNPs in identifying haplotypes, but there has been little investigation of the effect of missing or erroneous genotypes on the performance of these SNP selection algorithms and subsequent association tests using the selected tagging SNPs. The purpose of this study is to explore the effect of missing genotype or genotyping error on tagging SNP selection and subsequent single marker and haplotype association tests using the selected tagging SNPs. METHODS: Through two sets of simulations, we evaluated the performance of three tagging SNP selection programs in the presence of missing or erroneous genotypes: Clayton's diversity based program htstep, Carlson's linkage disequilibrium (LD) based program ldSelect, and Stram's coefficient of determination based program tagsnp.exe. RESULTS: When randomly selected known loci were relabeled as 'missing', we found that the average number of tagging SNPs selected by all three algorithms changed very little and the power of subsequent single marker and haplotype association tests using the selected tagging SNPs remained close to the power of these tests in the absence of missing genotype. When random genotyping errors were introduced, we found that the average number of tagging SNPs selected by all three algorithms increased. In data sets simulated according to the haplotype frequecies in the CYP19 region, Stram's program had larger increase than Carlson's and Clayton's programs. In data sets simulated under the coalescent model, Carlson's program had the largest increase and Clayton's program had the smallest increase. In both sets of simulations, with the presence of genotyping errors, the power of the haplotype tests from all three programs decreased quickly, but there was not much reduction in power of the single marker tests. CONCLUSIONS: Missing genotypes do not seem to have much impact on tagging SNP selection and subsequent single marker and haplotype association tests. In contrast, genotyping errors could have severe impact on tagging SNP selection and haplotype tests, but not on single marker tests.     

Finding Markers That Make a Difference: DNA Pooling and SNP-Arrays Identify Population Informative Markers for Genetic Stock Identification

Genetic stock identification (GSI) using molecular markers is an important tool for management of migratory species. Here, we tested a cost-effective alternative to individual genotyping, known as allelotyping, for identification of highly informative SNPs for accurate genetic stock identification. We estimated allele frequencies of 2880 SNPs from DNA pools of 23 Atlantic salmon populations using Illumina SNP-chip. We evaluated the performance of four common strategies (global F _ST, pairwise F _ST, Delta and outlier approach) for selection of the most informative set of SNPs and tested their effectiveness for GSI compared to random sets of SNP and microsatellite markers. For the majority of cases, SNPs selected using the outlier approach performed best followed by pairwise F _ST and Delta methods. Overall, the selection procedure reduced the number of SNPs required for accurate GSI by up to 53% compared with randomly chosen SNPs. However, GSI accuracy was more affected by populations in the ascertainment group rather than the ranking method itself. We demonstrated for the first time the compatibility of different large-scale SNP datasets by compiling the largest population genetic dataset for Atlantic salmon to date. Finally, we showed an excellent performance of our top SNPs on an independent set of populations covering the main European distribution range of Atlantic salmon. Taken together, we demonstrate how combination of DNA pooling and SNP arrays can be applied for conservation and management of salmonids as well as other species.     

MLR-tagging: informative SNP selection for unphased genotypes based on multiple linear regression

The search for the association between complex diseases and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. For these studies, it is essential to use a small subset of informative SNPs accurately representing the rest of the SNPs. Informative SNP selection can achieve (1) considerable budget savings by genotyping only a limited number of SNPs and computationally inferring all other SNPs or (2) necessary reduction of the huge SNP sets (obtained, e.g. from Affymetrix) for further fine haplotype analysis. A novel informative SNP selection method for unphased genotype data based on multiple linear regression (MLR) is implemented in the software package MLR-tagging. This software can be used for informative SNP (tag) selection and genotype prediction. The stepwise tag selection algorithm (STSA) selects positions of the given number of informative SNPs based on a genotype sample population. The MLR SNP prediction algorithm predicts a complete genotype based on the values of its informative SNPs, their positions among all SNPs, and a sample of complete genotypes. An extensive experimental study on various datasets including 10 regions from HapMap shows that the MLR prediction combined with stepwise tag selection uses fewer tags than the state-of-the-art method of Halperin et al. (2005). AVAILABILITY: MLR-Tagging software package is publicly available at http://alla.cs.gsu.edu/~software/tagging/tagging.html     

Evaluation of linkage disequilibrium and its effect on non-parametric multipoint linkage analysis using two high density single-nucleotide polymorphism mapping panels

Genotype data from the Illumina Linkage III SNP panel (n = 4,720 SNPs) and the Affymetrix 10 k mapping array (n = 11,120 SNPs) were used to test the effects of linkage disequilibrium (LD) between SNPs in a linkage analysis in the Collaborative Study on the Genetics of Alcoholism pedigree collection (143 pedigrees; 1,614 individuals). The average r2 between adjacent markers across the genetic map was 0.099 +/- 0.003 in the Illumina III panel and 0.17 +/- 0.003 in the Affymetrix 10 k array. In order to determine the effect of LD between marker loci in a nonparametric multipoint linkage analysis, markers in strong LD with another marker (r2 > 0.40) were removed (n = 471 loci in the Illumina panel; n = 1,804 loci in the Affymetrix panel) and the linkage analysis results were compared to the results using the entire marker sets. In all analyses using the ALDX1 phenotype, 8 linkage regions on 5 chromosomes (2, 7, 10, 11, X) were detected (peak markers p < 0.01), and the Illumina panel detected an additional region on chromosome 6. Analysis of the same pedigree set and ALDX1 phenotype using short tandem repeat markers (STRs) resulted in 3 linkage regions on 3 chromosomes (peak markers p < 0.01). These results suggest that in this pedigree set, LD between loci with spacing similar to the SNP panels tested may not significantly affect the overall detection of linkage regions in a genome scan. Moreover, since the data quality and information content are greatly improved in the SNP panels over STR genotyping methods, new linkage regions may be identified due to higher information content and data quality in a dense SNP linkage panel.