Identification of genome-wide single nucleotide polymorphisms in allopolyploid crop Brassica napus

Background

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation. Identification of large numbers of SNPs is helpful for genetic diversity analysis, map-based cloning, genome-wide association analyses and marker-assisted breeding. Recently, identifying genome-wide SNPs in allopolyploid Brassica napus (rapeseed, canola) by resequencing many accessions has become feasible, due to the availability of reference genomes of Brassica rapa (2n = AA) and Brassica oleracea (2n = CC), which are the progenitor species of B. napus (2n = AACC). Although many SNPs in B. napus have been released, the objective in the present study was to produce a larger, more informative set of SNPs for large-scale and efficient genotypic screening. Hence, short-read genome sequencing was conducted on ten elite B. napus accessions for SNP discovery. A subset of these SNPs was randomly selected for sequence validation and for genotyping efficiency testing using the Illumina GoldenGate assay.

Results

A total of 892,536 bi-allelic SNPs were discovered throughout the B. napus genome. A total of 36,458 putative amino acid variants were located in 13,552 protein-coding genes, which were predicted to have enriched binding and catalytic activity as a result. Using the GoldenGate genotyping platform, 94 of 96 SNPs sampled could effectively distinguish genotypes of 130 lines from two mapping populations, with an average call rate of 92%.

Conclusions

Despite the polyploid nature of B. napus, nearly 900,000 simple SNPs were identified by whole genome resequencing. These SNPs were predicted to be effective in high-throughput genotyping assays (51% polymorphic SNPs, 92% average call rate using the GoldenGate assay, leading to an estimated >450 000 useful SNPs). Hence, the development of a much larger genotyping array of informative SNPs is feasible. SNPs identified in this study to cause non-synonymous amino acid substitutions can also be utilized to directly identify causal genes in association studies.     

QTL linkage mapping of wing length in zebra finch using genome-wide single nucleotide polymorphisms markers

Avian wing length is an important trait that covaries with the ecology and migratory behaviour of a species and tends to change rapidly when the conditions are altered. Long-distance migrants typically have longer wings than short-distance migrants and sedentary species, and long-winged species also tend to be more dispersive. Although the substantial heritability of avian wing length is well established, the identification of causal genes has remained elusive. Based on large-scale genotyping of 1404 informative single nucleotide polymorphisms (SNP) in a captive population of 1067 zebra finches, we here show that the within-population variation of relative wing length (h(2) = 0.74 ± 0.05) is associated with standing genetic variation in at least six genomic regions (one genome-wide significant and five suggestive). The variance explained by these six quantitative trait loci (QTL) sums to 36.8% of the phenotypic variance (half of the additive genetic variance), although this likely is an overestimate attributable to the Beavis effect. As avian wing length is primarily determined by the length of the primary feathers, we then searched for candidate genes that are related to feather growth. Interestingly, all of the QTL signals co-locate with Wnt growth factors and closely interacting genes (Wnt3a, Wnt5a, Wnt6, Wnt7a, Wnt9a, RhoU and RhoV). Our findings therefore suggest that standing genetic variation in the Wnt genes might be linked to avian wing morphology, although there are many other genes that also fall within the confidence regions.     

Probability of detecting disease-associated single nucleotide polymorphisms in case-control genome-wide association studies

Some case-control genome-wide association studies (CCGWASs) select promising single nucleotide polymorphisms (SNPs) by ranking corresponding p-values, rather than by applying the same p-value threshold to each SNP. For such a study, we define the detection probability (DP) for a specific disease-associated SNP as the probability that the SNP will be "T-selected," namely have one of the top T largest chi-square values (or smallest p-values) for trend tests of association. The corresponding proportion positive (PP) is the fraction of selected SNPs that are true disease-associated SNPs. We study DP and PP analytically and via simulations, both for fixed and for random effects models of genetic risk, that allow for heterogeneity in genetic risk. DP increases with genetic effect size and case-control sample size and decreases with the number of nondisease-associated SNPs, mainly through the ratio of T to N, the total number of SNPs. We show that DP increases very slowly with T, and the increment in DP per unit increase in T declines rapidly with T. DP is also diminished if the number of true disease SNPs exceeds T. For a genetic odds ratio per minor disease allele of 1.2 or less, even a CCGWAS with 1000 cases and 1000 controls requires T to be impractically large to achieve an acceptable DP, leading to PP values so low as to make the study futile and misleading. We further calculate the sample size of the initial CCGWAS that is required to minimize the total cost of a research program that also includes follow-up studies to examine the T-selected SNPs. A large initial CCGWAS is desirable if genetic effects are small or if the cost of a follow-up study is large.     

Linkage analysis using single nucleotide polymorphisms

We performed multipoint linkage analysis using 83 markers from the SNP Consortium (TSC) SNP linkage map in 3 regions covering 190 cM previously scanned with microsatellite markers and found to be linked to type 2 diabetes. Since the average linkage disequilibrium present in the TSC SNP marker clusters is relatively low, we assumed the intracluster genetic distances were a reasonable small nonzero distance (0.03 cM) and performed linkage analysis using GENEHUNTER PLUS and ASM linkage analysis software. We found that for the pedigree structures and missing data patterns in our samples the average information content in all three regions and the LOD score curves in two regions obtained from the TSC SNP markers were similar to results obtained from microsatellite marker maps with 10 cM average spacing. We also give an algorithm which extends the Lander-Green algorithm to permit multipoint linkage analysis of clusters of tightly linked markers with arbitrarily high levels of intracluster linkage disequilibrium.     

SNPsFinder--a web-based application for genome-wide discovery of single nucleotide polymorphisms in microbial genomes

SUMMARY: Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations in closely related microbial species, strains or isolates. Some SNPs confer selective advantages for microbial pathogens during infection and many others are powerful genetic markers for distinguishing closely related strains or isolates that could not be distinguished otherwise. To facilitate SNP discovery in microbial genomes, we have developed a web-based application, SNPsFinder, for genome-wide identification of SNPs. SNPsFinder takes multiple genome sequences as input to identify SNPs within homologous regions. It can also take contig sequences and sequence quality scores from ongoing sequencing projects for SNP prediction. SNPsFinder will use genome sequence annotation if available and map the predicted SNP regions to known genes or regions to assist further evaluation of the predicted SNPs for their functional significance. SNPsFinder can generate PCR primers for all predicted SNP regions according to user's input parameters to facilitate experimental validation. The results from SNPsFinder analysis are accessible through the World Wide Web. AVAILABILITY: The SNPsFinder program is available at http://snpsfinder.lanl.gov/. SUPPLEMENTARY INFORMATION: The user's manual is available at http://snpsfinder.lanl.gov/UsersManual/     

Performance of single nucleotide polymorphisms versus haplotypes for genome-wide association analysis in barley

Genome-wide association studies (GWAS) may benefit from utilizing haplotype information for making marker-phenotype associations. Several rationales for grouping single nucleotide polymorphisms (SNPs) into haplotype blocks exist, but any advantage may depend on such factors as genetic architecture of traits, patterns of linkage disequilibrium in the study population, and marker density. The objective of this study was to explore the utility of haplotypes for GWAS in barley (Hordeum vulgare) to offer a first detailed look at this approach for identifying agronomically important genes in crops. To accomplish this, we used genotype and phenotype data from the Barley Coordinated Agricultural Project and constructed haplotypes using three different methods. Marker-trait associations were tested by the efficient mixed-model association algorithm (EMMA). When QTL were simulated using single SNPs dropped from the marker dataset, a simple sliding window performed as well or better than single SNPs or the more sophisticated methods of blocking SNPs into haplotypes. Moreover, the haplotype analyses performed better 1) when QTL were simulated as polymorphisms that arose subsequent to marker variants, and 2) in analysis of empirical heading date data. These results demonstrate that the information content of haplotypes is dependent on the particular mutational and recombinational history of the QTL and nearby markers. Analysis of the empirical data also confirmed our intuition that the distribution of QTL alleles in nature is often unlike the distribution of marker variants, and hence utilizing haplotype information could capture associations that would elude single SNPs. We recommend routine use of both single SNP and haplotype markers for GWAS to take advantage of the full information content of the genotype data.     

Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms

Characterization of genetic diversity is of great value to assist breeders in parental line selection and breeding system design. We screened 770 maize inbred lines with 1,034 single nucleotide polymorphism (SNP) markers and identified 449 high-quality markers with no germplasm-specific biasing effects. Pairwise comparisons across three distinct sets of germplasm, CIMMYT (394), China (282), and Brazil (94), showed that the elite lines from these diverse breeding pools have been developed with only limited utilization of genetic diversity existing in the center of origin. Temperate and tropical/subtropical germplasm clearly clustered into two separate groups. The temperate germplasm could be further divided into six groups consistent with known heterotic patterns. The greatest genetic divergence was observed between temperate and tropical/subtropical lines, followed by the divergence between yellow and white kernel lines, whereas the least divergence was observed between dent and flint lines. Long-term selection for hybrid performance has contributed to significant allele differentiation between heterotic groups at 20% of the SNP loci. There appeared to be substantial levels of genetic variation between different breeding pools as revealed by missing and unique alleles. Two SNPs developed from the same candidate gene were associated with the divergence between two opposite Chinese heterotic groups. Associated allele frequency change at two SNPs and their allele missing in Brazilian germplasm indicated a linkage disequilibrium block of 142 kb. These results confirm the power of SNP markers for diversity analysis and provide a feasible approach to unique allele discovery and use in maize breeding programs.     

Genetic diversity of two native sheep breeds by genome-wide analysis of single nucleotide polymorphisms

Wallachian and Sumava sheep are autochthonous breeds that have undergone a significant bottleneck effect and subsequent restoration efforts. The first objective of this study was to evaluate the degree of genetic variability of both breeds and, therefore, the current management of the breeding. The second was to determine whether these two breeds still retain their genetic uniqueness in relation to each other and other breeds, despite regenerative interventions. Our data consisted of 48 individuals of Sumava and 37 individuals of Wallachian sheep. The comparison data contained 25 other breeds (primarily European) from the HapMap dataset generated by the International Sheep Genomics Consortium. When comparing all 27 breeds, the Czech breeds clustered with 15 other breeds and formed a single branch with them according to Nei's distances. At the same time, however, the clusters of both breeds were integral and easily distinguishable from the others when displayed with principal component analysis (PCA). Population substructure analysis did not show any common genetic ancestry of the Czech national breeds and breeds used for regeneration or, eventually, breeds whose ancestral population was used for regeneration. The average values of F_ST were higher in Wallachian sheep (F_ST = 0.14) than in Sumava sheep (F_ST = 0.08). The linkage disequilibrium (LD) extension per autosome was higher in Wallachian than in Sumava sheep. Consequently, the N_e estimates five generations ago were 68 for Sumava versus 34 for Wallachian sheep. Both native Czech breeds exhibit a wide range of inbreeding based on the excess of homozygosity (F_HOM) among individuals, from ?0.04 to 0.16 in Sumava and from ?0.13 to 0.12 in Wallachian. Average inbreeding based on runs of homozygosity was 0.21 in Sumava and 0.27 in Wallachian. Most detected runs of homozygosity (ROH) were less than 5 Mb long for both breeds. ROH segments longer than 15 Mb were absent in Wallachian sheep. Concerning putative selection signatures, a total of 471 candidate genes in Wallachian sheep within 11 hotspots and 653 genes within 13 hotspots in Sumava sheep were identified. Czech breeds appear to be well differentiated from each other and other European breeds. Their genetic diversity is low, especially in the case of the Wallachian breed. Sumava is not so threatened by low diversity but has a larger share of the non-native gene pool.     

Eye color prediction using single nucleotide polymorphisms in Saudi population

BackgroundDNA prediction of eye color represent one application of the externally visible characteristics (EVC), which attained growing interest in the field of DNA forensic phenotyping. This is mainly due to its ability to narrow the pool of suspects without the need to compare any retrieved DNA material from the crime scene to a reference DNA. Several methods and multiplex genetic panel were proposed with variable prediction accuracy between different populations. However, such panel was not previously tested in the Saudi population, nor any populations of the Middle East and North Africa origin.MethodA panel of eleven single nucleotide polymorphisms (SNPs) was tested for their association with three eye colors (brown, hazel, and intermediate) in 80 volunteer Saudi individuals. SNPs and haplotype association test with eye colors were performed to identify the top significant SNPs with the three eye colors. Also, multinomial logistic regression was used to construct the prediction model using a training set of 60 subjects, and a validation set of 20 subjects. The goodness of fit parameter of the model to correctly predicts each eye color as compared to the other was performed.ResultsEye color was significantly associated with rs12913832, rs7170852, and rs916977 that are located within HERC2. SNP rs12913832 was the top significant SNP (p-value = 1.78E?15) that accounted for the association in this region, as the other SNPs were not significant after adjusting for rs12913832. A prediction model containing five SNPs showed high prediction accuracy with Area Under the receiver operating characteristic Curves (AUC) equals to 0.95 and 0.83 for brown and intermediate eye colors, respectively. However, the model’s performance was very low for predicting the hazel eye color with AUC equals 0.75.DiscussionDespite the small sample size of our study, we reported very significant SNP associations with eye color. Our model to predict eye colors based on DNA material showed high accuracy for brown and intermediate eye colors. The eye color prediction-model underperformed for the hazel eye colors, suggesting that larger sample size, as well as more comprehensive set of SNPs, could improve the model-prediction accuracy.     

Detection of single nucleotide polymorphisms

Single nucleotide polymorphism (SNP) detection technologies are used to scan for new polymorphisms and to determine the allele(s) of a known polymorphism in target sequences. SNP detection technologies have evolved from labor intensive, time consuming, and expensive processes to some of the most highly automated, efficient, and relatively inexpensive methods. Driven by the Human Genome Project, these technologies are now maturing and robust strategies are found in both SNP discovery and genotyping areas. The nearly completed human genome sequence provides the reference against which all other sequencing data can be compared. Global SNP discovery is therefore only limited by the amount of funding available for the activity. Local, target, SNP discovery relies mostly on direct DNA sequencing or on denaturing high performance liquid chromatography (dHPLC). The number of SNP genotyping methods has exploded in recent years and many robust methods are currently available. The demand for SNP genotyping is great, however, and no one method is able to meet the needs of all studies using SNPs. Despite the considerable gains over the last decade, new approaches must be developed to lower the cost and increase the speed of SNP detection.     

Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

Background  

As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.     

Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

MOTIVATION: Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. RESULTS: In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. AVAILABILITY: Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software     

单核苷酸多态概述

单核苷酸多态ＳＮＰ是遍布于基因组中的一种ＤＮＡ序列变化类型,人类基因组中平均约每一千碱基中有一个。单核苷酸多态是一种双等位型多态,群体中出现的频率大于１％或２％者视为多态,低于１％或２％的则视为突变。由于其具有高信息量、高密度又便于自动化操作的特点,单核苷酸多态在遗传性疾病基因的克隆和药物的设计与开发方面具有广阔的应用前景。本文对单核苷酸的概念、特点、应用前景,及其研究应用的一些问题作一综述。     

Multiplex single nucleotide polymorphisms genotyping using solid-phase single base extension on magnetic nanoparticles

To fulfill the increasing need for large-scale genetic research, we have developed a new solid-phase single base extension (SBE) protocol on magnetic nanoparticles (MNPs) for multiplex SNP detection using adapter polymerase chain reaction (PCR) products as templates. Extension primers were covalently immobilized on the MNPs, and allele-specific extension took place along the stretch of target DNA for one-color ddNTP incorporation. The MNPs with fluorophores were spotted on a glass slide to fabricate a “bead array” to discriminate their genotypes. Eight SNP loci of three DNA samples were interrogated, and the experiment demonstrated that it is an efficient method for large-scale SNP genotyping.     

High-throughput genotyping of single nucleotide polymorphisms using new biplex invader technology

The feasibility of large-scale genome-wide association studies of complex human disorders depends on the availability of accurate and efficient genotyping methods for single nucleotide polymorphisms (SNPs). We describe a new platform of the invader assay, a biplex assay, where both alleles are interrogated in a single reaction tube. The assay was evaluated on over 50 different SNPs, with over 20 SNPs genotyped in study cohorts of over 1500 individuals. We assessed the usefulness of the new platform in high-throughput genotyping and compared its accuracy to genotyping results obtained by the traditional monoplex invader assay, TaqMan genotyping and sequencing data. We present representative data for two SNPs in different genes (CD36 and protein tyrosine phosphatase 1β) from a study cohort comprising over 1500 individuals with high or low-normal blood pressure. In this high-throughput application, the biplex invader assay is very accurate, with an error rate of <0.3% and a failure rate of 1.64%. The set-up of the assay is highly automated, facilitating the processing of large numbers of samples simultaneously. We present new analysis tools for the assignment of genotypes that further improve genotyping success. The biplex invader assay with its automated set-up and analysis offers a new efficient high-throughput genotyping platform that is suitable for association studies in large study cohorts.     

Fluorescence detection of single nucleotide polymorphisms using a universal molecular beacon

We present a simple and novel assay—employing a universal molecular beacon (MB) in the presence of Hg²⁺—for the detection of single nucleotide polymorphisms (SNPs) based on Hg²⁺–DNA complexes inducing a conformational change in the MB. The MB (T₇-MB) contains a 19-mer loop and a stem of a pair of seven thymidine (T) bases, a carboxyfluorescein (FAM) unit at the 5′-end, and a 4-([4-(dimethylamino)phenyl]azo)benzoic acid (DABCYL) unit at the 3′-end. Upon formation of Hg²⁺–T₇-MB complexes through T–Hg²⁺–T bonding, the conformation of T₇-MB changes from a random coil to a folded structure, leading to a decreased distance between the FAM and DABCYL units and, hence, increased efficiency of fluorescence resonance energy transfer (FRET) between the FAM and DABCYL units, resulting in decreased fluorescence intensity of the MB. In the presence of complementary DNA, double-stranded DNA complexes form (instead of the Hg²⁺–T₇-MB complexes), with FRET between the FAM and DABCYL units occurring to a lesser extent than in the folded structure. Under the optimal conditions (20 nM T₇-MB, 20 mM NaCl, 1.0 μM Hg²⁺, 5.0 mM phosphate buffer solution, pH 7.4), the linear plot of the fluorescence intensity against the concentration of perfectly matched DNA was linear over the range 2–30 nM (R² = 0.991), with a limit of detection of 0.5 nM at a signal-to-noise ratio of 3. This new probe provides higher selectivity toward DNA than that exhibited by conventional MBs.     

Cytokine single nucleotide polymorphisms in Iranian populations

Cytokines are important immunomodulatory molecules involved in immune responses against microorganisms; they also have an important role in the setting of immune system disorders. Cytokine single nucleotide polymorphisms have been extensively studied in different, normal populations as well as in association with disease. Cytokine gene polymorphisms are potentially important as genetic predictors of disease susceptibility, clinical outcome, and as a tool for anthropological studies. In this study, samples have been collected from 455 healthy individuals located in different regions of Iran (Tehran, Yazd, Sistan and Balochistan). Allele and genotype frequencies of cytokine SNP, including: IL-1alpha, IL-1beta, IL-1R, IL-1RA, IL-2, IL-4, IL-4RA, IL-6, IL-10, IL-12, TNF-alpha, TGF-beta and IFN-gamma were investigated, using the PCR-SSP method. Allele frequencies in Tehran and Yazd populations were similar, except for TGF-beta. Allele frequencies in Sistani & Baloch populations were similar at all positions, except for IL-1beta at position of -511 and IFN-gamma genes at position UTR5644; there were some differences in allele frequencies comparing these populations with the Yazd population, including: IL-4, IL-6, IL-10, TGF-beta and TNF-alpha. Although some significant differences were observed for some cytokines, it seems that the cytokine gene polymorphism profile of the Iranian population is similar to that of Caucasians, particularly the Italian population.     

Detecting single nucleotide polymorphisms using DNA arrays for plant pathogen diagnosis

The lack of a rapid and reliable means for routine pathogen identification has been one of the main limitations in plant disease management, and has pushed the development of culture-independent, molecular approaches. Currently, DNA array technology is the most suitable technique for high-throughput detection and identification, as well as quantification, of multiple pathogens in a single assay. Closely related pathogens that may have completely different host ranges or pathogenicity often differ in only a single to a few base pairs in genes that may be targeted for identification. Therefore, the ability to discriminate single nucleotide polymorphisms (SNPs) should be pursued in any diagnostic assay. In this paper, we demonstrate the utility of DNA array technology to detect SNPs while accounting for specific criteria such as the position of the mismatch, the sequence of the oligonucleotide, and the length and amount of labeled amplicons that are hybridized. When disregarding mismatches at the extreme ends of the oligonucleotides, cross hybridization to single mismatch oligonucleotides is rare when processing environmental samples that contain genetic material from unknown sources. In addition to plant pathology, this study is relevant for any field of research where DNA arrays are used to detect mutations or polymorphisms, ranging from human diagnostics to environmental microbiology and microbial ecology.     

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.