SNP Dataset Research Articles

Evidence of Colorectal Cancer-Associated Mutation in MCAK: A Computational Report

Computational prediction of disease-associated non-synonymous polymorphism (nsSNP) has provided a significant platform to filter out the pathological mutations from large pool of SNP datasets at a very low cost input. Several methodologies and complementary protocols have been previously implemented and has provided significant prediction results. Although the previously implicated prediction methods were capable of investigating the most likely deleterious nsSNPs, but due to the lack of genotype-phenotype association analysis, the prediction results lacked in accuracy level. In this work we implemented the computational compilation of protein conformational changes as well as the probable disease-associated phenotypic outcomes. Our result suggested E403K mutation in mitotic centromere-associated kinesin protein as highly damaging and showed strong concordance to the previously observed colorectal cancer mutations aggregation tendency and energy value changes. Moreover, the molecular dynamics simulation results showed major loss in conformation and stability of mutant N-terminal kinesin-like domain structure. The result obtained in this study will provide future prospect of computational approaches in determining the SNPs that may affect the native conformation of protein structure and lead to cancer-associated disorders.

Visualization of SNPs with t-SNE.

BackgroundSingle Nucleotide Polymorphisms (SNPs) are one of the largest sources of new data in biology. In most papers, SNPs between individuals are visualized with Principal Component Analysis (PCA), an older method for this purpose.Principal FindingsWe compare PCA, an aging method for this purpose, with a newer method, t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of large SNP datasets. We also propose a set of key figures for evaluating these visualizations; in all of these t-SNE performs better.SignificanceTo transform data PCA remains a reasonably good method, but for visualization it should be replaced by a method from the subfield of dimension reduction. To evaluate the performance of visualization, we propose key figures of cross-validation with machine learning methods, as well as indices of cluster validity.

Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching

The goal of any parentage analysis is to identify as many parent-offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but they require additional information in the form of demographic data, thousands of markers and/or estimates of genotyping error rates. For many non-model systems, it is simply not practical, cost-effective or logistically feasible to obtain this information. Here, we develop a Bayesian parentage method that only requires the sampled genotypes to account for genotyping error, missing data and false matches. Extensive testing with microsatellite and SNP datasets reveals that our Bayesian parentage method reliably controls for the number of false assignments, irrespective of the genotyping error rate. When the number of loci is limiting, our approach maximizes the number of correct assignments by accounting for the frequencies of shared alleles. Comparisons with exclusion and likelihood-based methods on an empirical salmon dataset revealed that our Bayesian method had the highest ratio of correct to incorrect assignments.

The Pathogen-annotated Tracking Resource Network (PATRN) system: A web-based resource to aid food safety, regulatory science, and investigations of foodborne pathogens and disease

Investigation of foodborne diseases requires the capture and analysis of time-sensitive information on microbial pathogens that is derived from multiple analytical methods and sources. The web-based Pathogen-annotated Tracking Resource Network (PATRN) system (www.patrn.net) was developed to address the data aggregation, analysis, and communication needs important to the global food safety community for the investigation of foodborne disease. PATRN incorporates a standard vocabulary for describing isolate metadata and provides a representational schema for a prototypic data exchange standard using a novel data loading wizard for aggregation of assay and attribution information. PATRN currently houses expert-curated, high-quality “foundational datasets” consisting of published experimental results from conventional assays and next generation analysis platforms for isolates of Escherichia coli, Listeria monocytogenes, and Salmonella, Shigella, Vibrio and Cronobacter species. A suite of computational tools for data mining, clustering, and graphical representation is available. Within PATRN, the public curated data repository is complemented by a secure private workspace for user-driven analyses, and for sharing data among collaborators. To demonstrate the data curation, loading wizard features, and analytical capabilities of PATRN, three use-case scenarios are presented. Use-case scenario one is a comparison of the distribution and prevalence of plasmid-encoded virulence factor genes among 249 Cronobacter strains with similar attributes to that of nine Cronobacter isolates from recent cases obtained between March and October, 2010–2011. To highlight PATRN's data management and trend finding tools, analysis of datasets, stored in PATRN as part of an ongoing surveillance project to identify the predominant molecular serogroups among Cronobacter sakazakii isolates observed in the USA is shown. Use-case scenario two demonstrates the secure workspace available for private users to upload and analyze sensitive data, and for collating cross-platform datasets to identify and validate congruent datapoints. SNP datasets from WGS assemblies and pan-genome microarrays are analyzed in a combinatorial fashion to determine relatedness of 33 Salmonella enterica strains to six strains collected as part of an outbreak investigation. Use-case scenario three utilizes published surveillance results that describe the incidence and sources of O157:H7 E. coli isolates associated with a produce pre-harvest surveillance study that occurred during 2002–2006. In summary, PATRN is a web-based integrated platform containing tools for the management, analysis and visualization of data about foodborne pathogens.

Effects of introgression on the genetic population structure of two ecologically and economically important conifer species: lodgepole pine (Pinus contortavar.latifolia) and jack pine (Pinus banksiana)

Forest trees exhibit a remarkable range of adaptations to their environment, but as a result of frequent and long-distance gene flow, populations are often only weakly differentiated. Lodgepole and jack pine hybridize in western Canada, which adds the opportunity for introgression through hybridization to contribute to population structure and (or) adaptive variation. Access to large sample size, high density SNP datasets for these species would improve our ability to resolve population structure, parameterize introgression, and separate the influence of demography from adaptation. To accomplish this, 454 transcriptome reads for lodgepole and jack pine were assembled using Newbler and MIRA, the assemblies mined for SNPs, and 1536 SNPs were selected for typing on lodgepole pine, jack pine, and their hybrids (N = 536). We identified population structure using both Bayesian clustering and discriminate analysis of principle components. Introgressed SNP loci were identified and their influence on observed population structure was assessed. We found that introgressed loci resulted in increased differentiation both within lodgepole and jack pine populations. These findings are timely given the recent mountain pine beetle population expansion in the hybrid zone, and will facilitate future studies of adaptive traits in these ecologically important species.

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns.

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

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

Application of imputation methods to genomic selection in Chinese Holstein cattle

Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.

RAD tag sequencing as a source of SNP markers in Cynara cardunculus L.

BackgroundThe globe artichoke (Cynara cardunculus L. var. scolymus) genome is relatively poorly explored, especially compared to those of the other major Asteraceae crops sunflower and lettuce. No SNP markers are in the public domain. We have combined the recently developed restriction-site associated DNA (RAD) approach with the Illumina DNA sequencing platform to effect the rapid and mass discovery of SNP markers for C. cardunculus.ResultsRAD tags were sequenced from the genomic DNA of three C. cardunculus mapping population parents, generating 9.7 million reads, corresponding to ~1 Gbp of sequence. An assembly based on paired ends produced ~6.0 Mbp of genomic sequence, separated into ~19,000 contigs (mean length 312 bp), of which ~21% were fragments of putative coding sequence. The shared sequences allowed for the discovery of ~34,000 SNPs and nearly 800 indels, equivalent to a SNP frequency of 5.6 per 1,000 nt, and an indel frequency of 0.2 per 1,000 nt. A sample of heterozygous SNP loci was mapped by CAPS assays and this exercise provided validation of our mining criteria. The repetitive fraction of the genome had a high representation of retrotransposon sequence, followed by simple repeats, AT-low complexity regions and mobile DNA elements. The genomic k-mers distribution and CpG rate of C. cardunculus, compared with data derived from three whole genome-sequenced dicots species, provided a further evidence of the random representation of the C. cardunculus genome generated by RAD sampling.ConclusionThe RAD tag sequencing approach is a cost-effective and rapid method to develop SNP markers in a highly heterozygous species. Our approach permitted to generate a large and robust SNP datasets by the adoption of optimized filtering criteria.

Haplo2Ped: a tool using haplotypes as markers for linkage analysis

BackgroundGenerally, SNPs are abundant in the genome; however, they display low power in linkage analysis because of their limited heterozygosity. Haplotype markers, on the other hand, which are composed of many SNPs, greatly increase heterozygosity and have superiority in linkage statistics.ResultsHere we developed Haplo2Ped to automatically transform SNP data into haplotype markers and then to compute the logarithm (base 10) of odds (LOD) scores of regional haplotypes that are homozygous within the disease co-segregation haploid group. The results are reported as a hypertext file and a 3D figure to help users to obtain the candidate linkage regions. The hypertext file contains parameters of the disease linked regions, candidate genes, and their links to public databases. The 3D figure clearly displays the linkage signals in each chromosome. We tested Haplo2Ped in a simulated SNP dataset and also applied it to data from a real study. It successfully and accurately located the causative genomic regions. Comparison of Haplo2Ped with other existing software for linkage analysis further indicated the high effectiveness of this software.ConclusionsHaplo2Ped uses haplotype fragments as mapping markers in whole genome linkage analysis. The advantages of Haplo2Ped over other existing software include straightforward output files, increased accuracy and superior ability to deal with pedigrees showing incomplete penetrance. Haplo2Ped is freely available at: http://bighapmap.big.ac.cn/software.html.

SNP and gene networks construction and analysis from classification of copy number variations data

BackgroundDetection of genomic DNA copy number variations (CNVs) can provide a complete and more comprehensive view of human disease. It is interesting to identify and represent relevant CNVs from a genome-wide data due to high data volume and the complexity of interactions.ResultsIn this paper, we incorporate the DNA copy number variation data derived from SNP arrays into a computational shrunken model and formalize the detection of copy number variations as a case-control classification problem. More than 80% accuracy can be obtained using our classification model and by shrinkage, the number of relevant CNVs to disease can be determined. In order to understand relevant CNVs, we study their corresponding SNPs in the genome and a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and identify SNP networks based on their P-values. Our selected SNP networks are statistically significant compared with random SNP networks and play a role in the biological process. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that have similarity values being greater than a threshold are selected to construct gene networks. A gene enrichment analysis show that our gene networks are functionally important.Experimental results demonstrate that our selected SNP and gene networks based on the selected CNVs contain some functional relationships directly or indirectly to disease study.ConclusionsTwo datasets are given to demonstrate the effectiveness of the introduced method. Some statistical and biological analysis show that this shrunken classification model is effective in identifying CNVs from genome-wide data and our proposed framework has a potential to become a useful analysis tool for SNP data sets.

The effects of linkage disequilibrium in large scale SNP datasets for MDR

BackgroundIn the analysis of large-scale genomic datasets, an important consideration is the power of analytical methods to identify accurate predictive models of disease. When trying to assess sensitivity from such analytical methods, a confounding factor up to this point has been the presence of linkage disequilibrium (LD). In this study, we examined the effect of LD on the sensitivity of the Multifactor Dimensionality Reduction (MDR) software package.ResultsFour relative amounts of LD were simulated in multiple one- and two-locus scenarios for which the position of the functional SNP(s) within LD blocks varied. Simulated data was analyzed with MDR to determine the sensitivity of the method in different contexts, where the sensitivity of the method was gauged as the number of times out of 100 that the method identifies the correct one- or two-locus model as the best overall model. As the amount of LD increases, the sensitivity of MDR to detect the correct functional SNP drops but the sensitivity to detect the disease signal and find an indirect association increases.ConclusionsHigher levels of LD begin to confound the MDR algorithm and lead to a drop in sensitivity with respect to the identification of a direct association; it does not, however, affect the ability to detect indirect association. Careful examination of the solution models generated by MDR reveals that MDR can identify loci in the correct LD block; though it is not always the functional SNP. As such, the results of MDR analysis in datasets with LD should be carefully examined to consider the underlying LD structure of the dataset.

Genetic Association between Eotaxin Genes and Asthma and Its Relationship to Birth Season in Korean Children

Abstract Asthma is a chronic disease associated with airway constriction due to inflammation caused by eosinophils, mast cells, and T lymphocytes, leading to serious chronic illness in children. The eotaxin gene family has been shown to play an important role in the patho-genesis of asthma. We hypothesized that the distinctive variations among the four seasons in Korea may affect the expression of eotaxin polymorphisms, especially in children. We examined the possible effects of birth sea-son (spring, March-May; summer, June-August; fall, September-November; and winter, December-February) on the phenotype of asthma in children. All SNP data sets of the eotaxin-2 and eotaxin-3 genes were col-lected from 78 asthma patients and 101 controls. Here, we investigated the effects of birth season on the ex-pression of eotaxin-2 and eotaxin-3 in Korean children. Using the HAPLOTYPE procedure with the HTR method in SAS/Genetics, we showed that children born in spring and summer show significant haplotypes in both the eo-taxin-2 and eotaxin-3 genes. Thus, the expression of polymorphisms in eotaxin-2 and eotaxin-3 may vary by season.Keywords: asthma, haplotype, eotaxin, children, Korean

A Robust Statistical Method to Detect Null Alleles in Microsatellite and SNP Datasets in Both Panmictic and Inbred Populations

Null alleles are common technical artifacts in genetic-based analysis. Powerful methods enabling their detection in either panmictic or inbred populations have been proposed. However, none of these methods appears unbiased in both types of mating systems, necessitating a priori knowledge of the inbreeding level of the population under study. To counter this problem, I propose to use the software FDist2 to detect the atypical fixation indices that characterize markers with null alleles. The rational behind this approach and the parameter settings are explained. The power of the method for various sample sizes, degrees of inbreeding and null allele frequencies is evaluated using simulated microsatellite and SNP datasets and then compared to two other null allele detection methods. The results clearly show the robustness of the method proposed here as well as its greater accuracy in both panmictic and inbred populations for both types of marker. By allowing a proper detection of null alleles for a wide range of mating systems and markers, this new method is particularly appealing for numerous genetic studies using co-dominant loci.

An Application of the Elastic Net for an Endophenotype Analysis

We provide an illustration of an application of the elastic net to a large number of common genetic variants in the context of the search for the genetic bases of an endophenotype conceivably related to individual differences in learning. GABA concentration in the occipital cortex, a critical area for reading, was obtained in a group (n = 76) of children aged 6-10 years. Two extreme groups, high and low, were selected for genotyping with the 650Y Illumina array chip (Ilmn650Y). An elastic net approach was applied to the resulting SNP dataset; 100 SNPs were identified for each chromosome as "interesting" based on having the highest absolute value coefficients. The analyses highlighted chromosomes 15 and 20, which contained 55 candidate genes. The STRING partner analyses of the associated proteins pointed to a number of related genes, most notably, GABA and NTRK receptors.

Genetic signals of origin, spread, and introgression in a large sample of maize landraces

The last two decades have seen important advances in our knowledge of maize domestication, thanks in part to the contributions of genetic data. Genetic studies have provided firm evidence that maize was domesticated from Balsas teosinte (Zea mays subspecies parviglumis), a wild relative that is endemic to the mid- to lowland regions of southwestern Mexico. An interesting paradox remains, however: Maize cultivars that are most closely related to Balsas teosinte are found mainly in the Mexican highlands where subspecies parviglumis does not grow. Genetic data thus point to primary diffusion of domesticated maize from the highlands rather than from the region of initial domestication. Recent archeological evidence for early lowland cultivation has been consistent with the genetics of domestication, leaving the issue of the ancestral position of highland maize unresolved. We used a new SNP dataset scored in a large number of accessions of both teosinte and maize to take a second look at the geography of the earliest cultivated maize. We found that gene flow between maize and its wild relatives meaningfully impacts our inference of geographic origins. By analyzing differentiation from inferred ancestral gene frequencies, we obtained results that are fully consistent with current ecological, archeological, and genetic data concerning the geography of early maize cultivation.

Identification of Single Nucleotide Polymorphism Interactions Associated with Survival and Risk In Multiple Myeloma Using Novel Data Mining Methods

AbstractAbstract 2973Disease risk and therapeutic outcomes are impacted by both tumor heterogeneity as well as germline variations found in the population. Multiple myeloma (MM) shows significant heterogeneity in genetic aberrations in tumor cells, that together with inherited polymorphisms, affects disease risk and therapeutic response. In order to identify the impact of genetic variations (SNPs) on MM we have developed a Bank On A Cure platform for examining 3404 SNPs, selected in 983 genes associated with pathways affecting cellular functions important in cancer. Using SNP data sets we sought to identify genetic interactions, beyond single univariate association analysis. The challenge was to use data mining methods that take into account relatively small cohorts of patients, in which false discovery rates typically exceed the power of the study. We report results from using novel computational approaches that efficiently identify higher order SNP interactions associated with disease risk as well as survival outcomes, while minimizing the false discovery rate. The BOAC SNP panel was used to develop a data base on 143 patients selected for short (<1yr) versus long (>3yr) survival in ECOG 9486 and SWOG 9321; as well as 247 newly diagnosed patients and equal number of controls for disease risk analysis. One algorithm developed employs a discriminative pattern mining approach in which defined pathway sets of SNPs are used in combination testing. A second algorithm used identified SNPs that had some association with outcome (survival or disease status); but demonstrated a significant increase in associations when examined in combinations – we refer to this as a p-value jump association. Variations in genes associated with cell cycle, apoptosis, drug metabolism, stress response and immunity reached very low p-values, and survived multiple comparison testing when analyzed in combinations associated with both survival (PFS) predictions as well as analysis of case-control disease risk. Some of the key genetic variations identified in various combinations, included: PTRB, PTEN, CDK5, XRCC4, GSTA4, GPX, DYPD, PCNA, CYP4F2, VEGF, PON1, ALK, and BAG3. The data mining methods and algorithms used, and specific combinations associated with risk and survival, will be presented. These results are being further validated in new cohorts, and functional implications of identified genetic variants are being investigated in HapMap cell lines. Disclosures:No relevant conflicts of interest to declare.

Classifier Ensemble Based Analysis of a Genome-Wide SNP Dataset Concerning Late-Onset Alzheimer Disease

In this paper, the OpenBiomind toolkit is used to apply GA, GP, and local search methods to analyze a large SNP dataset concerning Late-Onset Alzheimer’s Disease (LOAD). Classification models identifying LOAD with statistically significant accuracy are identified as well as using ensemble-based important features analysis in order to identify brain genes related to LOAD, most notably the solute carrier gene SLC6A15. Ensemble analysis is used to identify potentially significant interactions between genes in the context of LOAD.

A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets

BackgroundThe recent advent of high-throughput SNP genotyping technologies has opened new avenues of research for population genetics. In particular, a growing interest in the identification of footprints of selection, based on genome scans for adaptive differentiation, has emerged.Methodology/Principal FindingsThe purpose of this study is to develop an efficient model-based approach to perform Bayesian exploratory analyses for adaptive differentiation in very large SNP data sets. The basic idea is to start with a very simple model for neutral loci that is easy to implement under a Bayesian framework and to identify selected loci as outliers via Posterior Predictive P-values (PPP-values). Applications of this strategy are considered using two different statistical models. The first one was initially interpreted in the context of populations evolving respectively under pure genetic drift from a common ancestral population while the second one relies on populations under migration-drift equilibrium. Robustness and power of the two resulting Bayesian model-based approaches to detect SNP under selection are further evaluated through extensive simulations. An application to a cattle data set is also provided.Conclusions/SignificanceThe procedure described turns out to be much faster than former Bayesian approaches and also reasonably efficient especially to detect loci under positive selection.

Evolutionary and Functional Analysis of Celiac Risk Loci Reveals SH2B3 as a Protective Factor against Bacterial Infection

Celiac disease (CD) is an intolerance to dietary proteins of wheat, barley, and rye. CD may have substantial morbidity, yet it is quite common with a prevalence of 1%-2% in Western populations. It is not clear why the CD phenotype is so prevalent despite its negative effects on human health, especially because appropriate treatment in the form of a gluten-free diet has only been available since the 1950s, when dietary gluten was discovered to be the triggering factor. The high prevalence of CD might suggest that genes underlying this disease may have been favored by the process of natural selection. We assessed signatures of selection for ten confirmed CD-associated loci in several genome-wide data sets, comprising 8154 controls from four European populations and 195 individuals from a North African population, by studying haplotype lengths via the integrated haplotype score (iHS) method. Consistent signs of positive selection for CD-associated derived alleles were observed in three loci: IL12A, IL18RAP, and SH2B3. For the SH2B3 risk allele, we also show a difference in allele frequency distribution (Fst) between HapMap phase II populations. Functional investigation of the effect of the SH2B3 genotype in response to lipopolysaccharide and muramyl dipeptide revealed that carriers of the SH2B3 rs3184504*A risk allele showed stronger activation of the NOD2 recognition pathway. This suggests that SH2B3 plays a role in protection against bacteria infection, and it provides a possible explanation for the selective sweep on SH2B3, which occurred sometime between 1200 and 1700 years ago.