Single-nucleotide polymorphism detection by denaturing high-performance liquid chromatography and direct sequencing in genes in the MHC class III region encoding novel cell surface molecules

The class III region of the human major histocompatibility complex (MHC) contains approximately 59 genes, many of which encode polypeptides with a variety of different functions. Eight of these genes are of particular interest because they encode novel surface molecules that could be involved in immune and/or inflammatory responses and are excellent candidates as disease susceptibility loci. These molecules are members of two different superfamilies, the immunoglobulin superfamily (1C7, G6B, and G6F genes) and the leucocyte antigen-6 superfamily (G6C, G6D, G6E, G5C, and G5B genes). Some level of variation was found when overlapping genomic DNAs from different haplotypes were compared. The present work describes a systematic search for single-nucleotide polymorphisms (SNPs) in these genes using direct sequencing and denaturing high-performance liquid chromatography (DHPLC) in 24 unrelated healthy individuals. We validated the DHPLC methodology by first studying the 1C7 gene. This gene was directly sequenced in all 24 samples, and DHPLC was found to resolve all the polymorphic sites present in the heterozygote samples tested. We screened the rest of the genes by DHPLC only, and only those chromatograms that revealed a polymorphic profile were sequenced. We detected one SNP every 489 bp in the 18 kb of DNA studied, corresponding to theta = 4.61x10-4. The diversity in noncoding regions is 1 SNP/560 bp, but a higher frequency was detected in coding regions with 1 SNP/423 bp corresponding to theta =5.33x10-4. Of the coding SNPs, 63.6% caused amino acid substitutions. The power of this study is emphasized by the fact that of the 37 SNPs/indels detected, only 6 can be found in the SNP database at the NCBI.     

Single-nucleotide polymorphism phylotyping of Escherichia coli

We describe a rapid and easily automated phylogenetic grouping technique based on analysis of bacterial genome single-nucleotide polymorphisms (SNPs). We selected 13 SNPs derived from a complete sequence analysis of 11 essential genes previously used for multilocus sequence typing (MLST) of 30 Escherichia coli strains representing the genetic diversity of the species. The 13 SNPs were localized in five genes, trpA, trpB, putP, icdA, and polB, and were selected to allow recovery of the main phylogenetic groups (groups A, B1, E, D, and B2) and subgroups of the species. In the first step, we validated the SNP approach in silico by extracting SNP data from the complete sequences of the five genes for a panel of 65 pathogenic strains belonging to different E. coli pathovars, which were previously analyzed by MLST. In the second step, we determined these SNPs by dideoxy single-base extension of unlabeled oligonucleotide primers for a collection of 183 commensal and extraintestinal clinical E. coli isolates and compared the SNP phylotyping method to previous well-established typing methods. This SNP phylotyping method proved to be consistent with the other methods for assigning phylogenetic groups to the different E. coli strains. In contrast to the other typing methods, such as multilocus enzyme electrophoresis, ribotyping, or PCR phylotyping using the presence/absence of three genomic DNA fragments, the SNP typing method described here is derived from a solid phylogenetic analysis, and the results obtained by this method are more meaningful. Our results indicate that similar approaches may be used for a wide variety of bacterial species.     

SNPMeta: SNP annotation and SNP metadata collection without a reference genome

The increase in availability of resequencing data is greatly accelerating SNP discovery and has facilitated the development of SNP genotyping assays. This, in turn, is increasing interest in annotation of individual SNPs. Currently, these data are only available through curation, or comparison to a reference genome. Many species lack a reference genome, but are still important genetic models or are significant species in agricultural production or natural ecosystems. For these species, it is possible to annotate SNPs through comparison with cDNA, or data from well‐annotated genes in public repositories. We present SNPMeta, a tool which gathers information about SNPs by comparison with sequences present in GenBank databases. SNPMeta is able to annotate SNPs from contextual sequence in SNP assay designs, and SNPs discovered through genotyping by sequencing (GBS) approaches. However, SNPs discovered through GBS occur throughout the genome, rather than only in gene space, and therefore do not annotate at high rates. SNPMeta can therefore be used to annotate SNPs in nonmodel species or species that lack a reference genome. Annotations generated by SNPMeta are highly concordant with annotations that would be obtained from a reference genome.     

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.     

Coding variants in human double-strand break DNA repair genes

DNA repair is essential for the maintenance of genomic integrity. Consequently, altered repair capacity may impact individual health in such areas as aging and susceptibility to certain diseases. Defects in some DNA repair genes, for example, have been shown to increase cancer risk, accelerate aging and impair neurological functions. Now that over 115 genes directly involved in human DNA repair have been characterized at the DNA sequence level, the identification of single nucleotide polymorphisms (SNPs) in DNA repair genes is becoming a reality. This information will likely lead to the identification of alleles, or combinations of alleles that affect disease predisposition. This communication summarizes SNPs identified to date in the coding region of 24 human double-strand break repair (DSBR) genes. SNP data for four of these genes were obtained by screening at least 100 individuals in our laboratory. For each SNP, the codon number, amino acid substitution, allele frequency and population information is supplied.     

Single nucleotide polymorphism (SNP) discovery in mammals: a targeted-gene approach

Single nucleotide polymorphisms (SNPs) have rarely been exploited in nonhuman and nonmodel organism genetic studies. This is due partly to difficulties in finding SNPs in species where little DNA sequence data exist, as well as to a lack of robust and inexpensive genotyping methods. We have explored one SNP discovery method for molecular ecology, evolution, and conservation studies to evaluate the method and its limitations for population genetics in mammals. We made use of 'CATS' (or 'EPIC') primers to screen for novel SNPs in mammals. Most of these primer sets were designed from primates and/or rodents, for amplifying intron regions from conserved genes. We have screened 202 loci in 16 representatives of the major mammalian clades. Polymerase chain reaction (PCR) success correlated with phylogenetic distance from the human and mouse sequences used to design most primers; for example, specific PCR products from primates and the mouse amplified the most consistently and the marsupial and armadillo amplifications were least successful. Approximately 24% (opossum) to 65% (chimpanzee) of primers produced usable PCR product(s) in the mammals tested. Products produced generally high but variable levels of readable sequence and similarity to the expected genes. In a preliminary screen of chimpanzee DNA, 12 SNPs were identified from six (of 11) sequenced regions, yielding a SNP on average every 400 base pairs (bp). Given the progress in genome sequencing, and the large numbers of CATS-like primers published to date, this approach may yield sufficient SNPs per species for population and conservation genetic studies in nonmodel mammals and other organisms.     

SNPing away at complex diseases: analysis of single-nucleotide polymorphisms around APOE in Alzheimer disease

There has been great interest in the prospects of using single-nucleotide polymorphisms (SNPs) in the search for complex disease genes, and several initiatives devoted to the identification and mapping of SNPs throughout the human genome are currently underway. However, actual data investigating the use of SNPs for identification of complex disease genes are scarce. To begin to look at issues surrounding the use of SNPs in complex disease studies, we have initiated a collaborative SNP mapping study around APOE, the well-established susceptibility gene for late-onset Alzheimer disease (AD). Sixty SNPs in a 1.5-Mb region surrounding APOE were genotyped in samples of unrelated cases of AD, in controls, and in families with AD. Standard tests were conducted to look for association of SNP alleles with AD, in cases and controls. We also used family-based association analyses, including recently developed methods to look for haplotype association. Evidence of association (P相似文献   

Single-Nucleotide Polymorphism Phylotyping of Escherichia coli

We describe a rapid and easily automated phylogenetic grouping technique based on analysis of bacterial genome single-nucleotide polymorphisms (SNPs). We selected 13 SNPs derived from a complete sequence analysis of 11 essential genes previously used for multilocus sequence typing (MLST) of 30 Escherichia coli strains representing the genetic diversity of the species. The 13 SNPs were localized in five genes, trpA, trpB, putP, icdA, and polB, and were selected to allow recovery of the main phylogenetic groups (groups A, B1, E, D, and B2) and subgroups of the species. In the first step, we validated the SNP approach in silico by extracting SNP data from the complete sequences of the five genes for a panel of 65 pathogenic strains belonging to different E. coli pathovars, which were previously analyzed by MLST. In the second step, we determined these SNPs by dideoxy single-base extension of unlabeled oligonucleotide primers for a collection of 183 commensal and extraintestinal clinical E. coli isolates and compared the SNP phylotyping method to previous well-established typing methods. This SNP phylotyping method proved to be consistent with the other methods for assigning phylogenetic groups to the different E. coli strains. In contrast to the other typing methods, such as multilocus enzyme electrophoresis, ribotyping, or PCR phylotyping using the presence/absence of three genomic DNA fragments, the SNP typing method described here is derived from a solid phylogenetic analysis, and the results obtained by this method are more meaningful. Our results indicate that similar approaches may be used for a wide variety of bacterial species.     

Automated identification of single nucleotide polymorphisms from sequencing data

The single nucleotide polymorphism (SNP) is the difference of the DNA sequence between individuals and provides abundant information about genetic variation. Large scale discovery of high frequency SNPs is being undertaken using various methods. However, the publicly available SNP data sometimes need to be verified. If only a particular gene locus is concerned, locus-specific polymerase chain reaction amplification may be useful. Problem of this method is that the secondary peak has to be measured. We have analyzed trace data from conventional sequencing equipment and found an applicable rule to discern SNPs from noise. The rule is applied to multiply aligned sequences with a trace and the peak height of the traces are compared between samples. We have developed software that integrates this function to automatically identify SNPs. The software works accurately for high quality sequences and also can detect SNPs in low quality sequences. Further, it can determine allele frequency, display this information as a bar graph and assign corresponding nucleotide combinations. It is also designed for a person to verify and edit sequences easily on the screen. It is very useful for identifying de novo SNPs in a DNA fragment of interest.     

A comparison of simple sequence repeat and single nucleotide polymorphism marker technologies for the genotypic analysis of maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

We report on the comparative utilities of simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) markers for characterizing maize germplasm in terms of their informativeness, levels of missing data, repeatability and the ability to detect expected alleles in hybrids and DNA pools. Two different SNP chemistries were compared; single-base extension detected by Sequenom MassARRAY, and invasive cleavage detected by Invader chemistry with PCR. A total of 58 maize inbreds and four hybrids were genotyped with 80 SSR markers, 69 Invader SNP markers and 118 MassARRAY SNP markers, with 64 SNP loci being common to the two SNP marker chemistries. Average expected heterozygosity values were 0.62 for SSRs, 0.43 for SNPs (pre-selected for their high level of polymorphism) and 0.63 for the underlying sequence haplotypes. All individual SNP markers within the same set of sequences had an average expected heterozygosity value of 0.26. SNP marker data had more than a fourfold lower level of missing data (2.1-3.1%) compared with SSRs (13.8%). Data repeatability was higher for SNPs (98.1% for MassARRAY SNPs and 99.3% for Invader) than for SSRs (91.7%). Parental alleles were observed in hybrid genotypes in 97.0% of the cases for MassARRAY SNPs, 95.5% for Invader SNPs and 81.9% for SSRs. In pooled samples with mixtures of alleles, SSRs, MassARRAY SNPs and Invader SNPs were equally capable of detecting alleles at mid to high frequencies. However, at low frequencies, alleles were least likely to be detected using Invader SNP markers, and this technology had the highest level of missing data. Collectively, these results showed that SNP technologies can provide increased marker data quality and quantity compared with SSRs. The relative loss in polymorphism compared with SSRs can be compensated by increasing SNP numbers and by using SNP haplotypes. Determining the most appropriate SNP chemistry will be dependent upon matching the technical features of the method within the context of application, particularly in consideration of whether genotypic samples will be pooled or assayed individually.     

Decision Forest Analysis of 61 Single Nucleotide Polymorphisms in a Case-Control Study of Esophageal Cancer; a novel method

Background

Systematic evaluation and study of single nucleotide polymorphisms (SNPs) made possible by high throughput genotyping technologies and bioinformatics promises to provide breakthroughs in the understanding of complex diseases. Understanding how the millions of SNPs in the human genome are involved in conferring susceptibility or resistance to disease, or in rendering a drug efficacious or toxic in the individual is a major goal of the relatively new fields of pharmacogenomics. Esophageal squamous cell carcinoma is a high-mortality cancer with complex etiology and progression involving both genetic and environmental factors. We examined the association between esophageal cancer risk and patterns of 61 SNPs in a case-control study for a population from Shanxi Province in North Central China that has among the highest rates of esophageal squamous cell carcinoma in the world.

Methods

High-throughput Masscode mass spectrometry genotyping was done on genomic DNA from 574 individuals (394 cases and 180 age-frequency matched controls). SNPs were chosen from among genes involving DNA repair enzymes, and Phase I and Phase II enzymes.We developed a novel adaptation of the Decision Forest pattern recognition method named Decision Forest for SNPs (DF-SNPs). The method was designated to analyze the SNP data.

Results

The classifier in separating the cases from the controls developed with DF-SNPs gave concordance, sensitivity and specificity, of 94.7%, 99.0% and 85.1%, respectively; suggesting its usefulness for hypothesizing what SNPs or combinations of SNPs could be involved in susceptibility to esophageal cancer. Importantly, the DF-SNPs algorithm incorporated a randomization test for assessing the relevance (or importance) of individual SNPs, SNP types (Homozygous common, heterozygous and homozygous variant) and patterns of SNP types (SNP patterns) that differentiate cases from controls. For example, we found that the different genotypes of SNP GADD45B E1122 are all associated with cancer risk.

Conclusion

The DF-SNPs method can be used to differentiate esophageal squamous cell carcinoma cases from controls based on individual SNPs, SNP types and SNP patterns. The method could be useful to identify potential biomarkers from the SNP data and complement existing methods for genotype analyses.

     

Single nucleotide polymorphism discovery in barley using autoSNPdb

Molecular markers are used to provide the link between genotype and phenotype, for the production of molecular genetic maps and to assess genetic diversity within and between related species. Single nucleotide polymorphisms (SNPs) are the most abundant molecular genetic marker. SNPs can be identified in silico , but care must be taken to ensure that the identified SNPs reflect true genetic variation and are not a result of errors associated with DNA sequencing. The SNP detection method autoSNP has been developed to identify SNPs from sequence data for any species. Confidence in the predicted SNPs is based on sequence redundancy, and haplotype co-segregation scores are calculated for a further independent measure of confidence. We have extended the autoSNP method to produce autoSNPdb, which integrates SNP and gene annotation information with a graphical viewer. We have applied this software to public barley expressed sequences, and the resulting database is available over the Internet. SNPs can be viewed and searched by sequence, functional annotation or predicted synteny with a reference genome, in this case rice. The correlation between SNPs and barley cultivar, expressed tissue type and development stage has been collated for ease of exploration. An average of one SNP per 240 bp was identified, with SNPs more prevalent in the 5' regions and simple sequence repeat (SSR) flanking sequences. Overall, autoSNPdb can provide a wealth of genetic polymorphism information for any species for which sequence data are available.     

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.     

Variable SNP density in aspartyl-protease genes of the malaria parasite Plasmodium falciparum

An analysis of the diversity of the aspartyl proteases of Plasmodium falciparum, known as plasmepsins (PMs), was completed in view of their possible role as drug targets. DNA sequence polymorphisms were identified in nine pm genes including their non-coding (introns and 5' flanking) sequences. All genes contained at least one single nucleotide polymorphism (SNP). Extensive microsatellite diversity was observed predominantly in non-coding sequences. All but one non-synonymous polymorphism (a conservative substitution) were mapped to the surface of the predicted protein, contradicting a possible role in enzymatic activity. The distribution of SNPs was found to be non-random among pm genes, with pm6 and pm10 having significantly higher SNP densities, suggesting they were under selection. For pm6 the majority of the SNPs were in introns and some of these may contribute to splice site variation. SNPs were found at a high density in both the coding and non-coding sequences of pm10. Recombination was important in generating additional diversity at this locus. Although direct selection for pm10 mutations could not be ruled out, the presence of balancing selection and a high density of SNPs in non-coding sequence led us to propose that another gene under selection may be influencing the diversity in the region. By sequencing short DNA tags in a 200 kb region flanking pm10 we show that a cluster of antigen genes, known to be under diversifying selection, may contribute to the observed diversity. We discuss the importance of diversity and local selection effects when choosing drug targets for intervention strategies.     

Development of high‐density SNP genotyping arrays for white spruce (Picea glauca) and transferability to subtropical and nordic congeners

High‐density SNP genotyping arrays can be designed for any species given sufficient sequence information of high quality. Two high‐density SNP arrays relying on the Infinium iSelect technology (Illumina) were designed for use in the conifer white spruce (Picea glauca). One array contained 7338 segregating SNPs representative of 2814 genes of various molecular functional classes for main uses in genetic association and population genetics studies. The other one contained 9559 segregating SNPs representative of 9543 genes for main uses in population genetics, linkage mapping of the genome and genomic prediction. The SNPs assayed were discovered from various sources of gene resequencing data. SNPs predicted from high‐quality sequences derived from genomic DNA reached a genotyping success rate of 64.7%. Nonsingleton in silico SNPs (i.e. a sequence polymorphism present in at least two reads) predicted from expressed sequenced tags obtained with the Roche 454 technology and Illumina GAII analyser resulted in a similar genotyping success rate of 71.6% when the deepest alignment was used and the most favourable SNP probe per gene was selected. A variable proportion of these SNPs was shared by other nordic and subtropical spruce species from North America and Europe. The number of shared SNPs was inversely proportional to phylogenetic divergence and standing genetic variation in the recipient species, but positively related to allele frequency in P. glauca natural populations. These validated SNP resources should open up new avenues for population genetics and comparative genetic mapping at a genomic scale in spruce species.