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As the third generation of genetic markers SNPs (single nucleotide polymorphisms) has been used extensively in gene mapping,disease-correlativity analysis ,population genetics and drug research. Here methods for detection are reviewed. Most SNP genotyping are a combination of method for interrogating SNPs and analysis technique.It described both parts and give a outlook for detection.
We previously revealed that one of the human leukocyte antigen-linked susceptibility genes for Takayasu's arteritis (TA) was mapped between TNFA and MICB loci and that -63T allele of NFKBIL1, which is between TNFA and MICB loci, was associated with rheumatoid arthritis (RA) in the Japanese population. We have developed a novel typing method based on reference strand-mediated conformation analysis for the upstream sequence of the NFKBIL1 gene, where -422 (T)8/(T)9, -325 C/G, -263 A/G, and -63 T/A polymorphisms were found. Upon the analysis of the patients with TA (n = 84), those with RA (n = 120), and healthy control subjects (n = 217), five common haplotypes named IKBLp*01 through IKBLp*05 were found in the Japanese population. The frequency of IKBLp*03 was significantly increased in the patient with TA (57.1% vs 35.0%, giving an odds ratio of 2.47). In addition, the frequency of IKBLp*01, but not that of other -63T-bearing alleles, was increased in the patients with RA (73.3% vs 58.1%, giving an odds ratio of 1.99), suggesting that the susceptibility to RA was conferred not by -63T alone but by combination of single nucleotide polymorphisms in the NFKBIL1 promoter. A higher promoter activity associated with IKBLp*03 and a lower activity associated with IKBLp*01 may contribute to the susceptibility to TA and RA, respectively.
Single-nucleotide polymorphisms (SNPs) have great potential for use in genetic-mapping studies, which locate and characterize genes that are important in human disease and biological function. For SNPs to realize their full potential in genetic analysis, thousands of different SNP loci must be screened in a rapid, accurate and cost-effective manner. Matrix-assisted laser desorption-ionization-time-of-flight (MALDI-TOF) mass spectrometry is a promising tool for the high-throughput screening of SNPs, with future prospects for use in genetic analysis.
More than 5500 pig single nucleotide polymorphisms (SNPs) were recently identified and deposited in the public domain. To test the usefulness of these public SNPs, 109 SNPs were analysed for polymorphism within six commercial pig populations. A functional polymerase chain reaction (PCR) assay was obtained for 103 SNPs and it was possible to validate c. 59% by PCR-restriction fragment length polymorphism. Furthermore, polymorphism was found using a relatively limited number of genomic DNA samples, indicating that these polymorphisms are segregating at a useful frequency in these populations. The high percentage of validated markers demonstrates the utility of these public pig SNPs to identify loci responsible for economically important traits in commercial pig populations.
Some general likelihood and Bayesian methods for analyzing single nucleotide polymorphisms (SNPs) are presented. First, an efficient method for estimating demographic parameters from SNPs in linkage equilibrium is derived. The method is applied in the estimation of growth rates of a human population based on 37 SNP loci. It is demonstrated how ascertainment biases, due to biased sampling of loci, can be avoided, at least in some cases, by appropriate conditioning when calculating the likelihood function. Second, a Markov chain Monte Carlo (MCMC) method for analyzing linked SNPs is developed. This method can be used for Bayesian and likelihood inference on linked SNPs. The utility of the method is illustrated by estimating recombination rates in a human data set containing 17 SNPs and 60 individuals. Both methods are based on assumptions of low mutation rates.
The single nucleotide polymorphisms (SNPs) in conserved protein regions have been thought to be strong candidates that alter protein functions. Thus, we have developed SNP@Domain, a web resource, to identify SNPs within human protein domains. We annotated SNPs from dbSNP with protein structure-based as well as sequence-based domains: (i) structure-based using SCOP and (ii) sequence-based using Pfam to avoid conflicts from two domain assignment methodologies. Users can investigate SNPs within protein domains with 2D and 3D maps. We expect this visual annotation of SNPs within protein domains will help scientists select and interpret SNPs associated with diseases. A web interface for the SNP@Domain is freely available at http://snpnavigator.net/ and from http://bioportal.net/.
Single-nucleotide polymorphisms (SNPs) were studied in 15 olfactory receptor (OR) coding regions, one control region and two noncoding sequences all residing within a 412 kb OR gene cluster on human chromosome 17p13.3, as well as in other G-protein coupled receptors (GPCRs). A total of 26 SNPs were identified in ORs, 21 of which are coding SNPs (cSNPs). The mean nucleotide diversity of OR coding regions was 0.078% (ranging from 0 to 0.16%), which is about twice higher than that of other GPCRs, and similar to the nucleotide diversity levels of noncoding regions along the human genome. The high polymorphism level in the OR coding regions might be due to a weak positive selection pressure acting on the OR genes. In two cases, OR genes have been found to share the same cSNP. This could be explained by recent gene conversion events, which might be a part of a concerted evolution mechanism acting on the OR superfamily. Using the genotype data of 85 unrelated individuals in 15 SNPs, we found linkage disequilibrium (LD) between pairs of SNPs located on the centromeric part of the cluster. On the other hand, no LD was found between SNPs located on the telomeric part of the cluster, suggesting the presence of several hot-spots for recombination within this cluster. Thus, different regions of this gene cluster may have been subject to different recombination rates.
MOTIVATION: The large volume of single nucleotide polymorphism data now available motivates the development of methods for distinguishing neutral changes from those which have real biological effects. Here, two different machine-learning methods, decision trees and support vector machines (SVMs), are applied for the first time to this problem. In common with most other methods, only non-synonymous changes in protein coding regions of the genome are considered. RESULTS: In detailed cross-validation analysis, both learning methods are shown to compete well with existing methods, and to out-perform them in some key tests. SVMs show better generalization performance, but decision trees have the advantage of generating interpretable rules with robust estimates of prediction confidence. It is shown that the inclusion of protein structure information produces more accurate methods, in agreement with other recent studies, and the effect of using predicted rather than actual structure is evaluated. AVAILABILITY: Software is available on request from the authors.
In the future, analysis of single nucleotide polymorphisms (SNPs) should become a powerful tool for many genetic applications in areas such as association studies, pharmacogenetics and traceability in the agro-alimentary sector. A number of technologies have been developed for high-throughput genotyping of SNPs. Here we present the simplified GOOD assay for SNP genotyping by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI). The simplified GOOD assay is a single-tube, purification-free, three-step procedure consisting of PCR, primer extension and phosphodiesterase II digestion followed by mass spectrometric analysis. Due to the application of charge-tag technology, no sample purification is required prior to the otherwise very impurity-sensitive MALDI analysis. The use of methylphosphonate containing primers and ddNTPs or alpha-S-ddNTPs together with a novel DNA polymerase derived from Thermotoga maritima for primer extension allow the fluent preparation of negatively charge-tagged, allele-specific products. A key feature of this polymerase is its preference for ddNTPs and alpha-S-ddNTPs over dNTPs. The simplified GOOD assay was run with automatic liquid handling at the lowest manageable volumes, automatic data acquisition and interpretation. We applied this novel procedure to genotyping SNPs of candidate genes for hypertension and cardiovascular disease.
This paper reviews the theoretical basis for single nucleotide polymorphism (SNP) tagging and considers the use of current software made freely available for this task. A distinction between haplotype block-based and non-block-based approaches yields two classes of procedures. Analysis of two different sets of SNP genotype data from the HapMap is used to judge the practical aspects of using each of the programs considered, as well as to make some general observations about the performance of the programs in finding optimal sets of tagging SNPs. Pairwise R2 methods, while the simplest of those considered, do tend to pick more tagging SNPs than are strictly needed to predict unmeasured (non-tagging) SNPs, since a combination of two or more tagging SNPs can form a prediction of SNPs that have no direct (pairwise) surrogate. Block-based methods that exploit the linkage disequilibrium structure within haplotype blocks exploit this sort of redundancy, but run a risk of over-fitting if used without some care. A compromise approach which eliminates the need first to analyse block structure, but which still exploits simple relationships between SNPs, appears promising.
Identification of single nucleotide polymorphisms (SNPs) by DNA sequence comparison across breeds is a strategy for developing genetic markers that are useful for many breeds. However, the heterozygosity of SNPs identified in this way might be severely reduced within breeds by inbreeding or genetic drift in the small effective population size of a breed (population subdivision). The effect of inbreeding and population subdivision on heterozygosity of SNPs in dog breeds has never been investigated in a systematic way. We determined the genotypes of dogs from three divergent breeds for SNPs in four canine genes (ACTC, LMNA, SCGB, and TYMS) identified by across-breed DNA sequence comparison, and compared the genotype frequencies to those expected under Hardy-Weinberg equilibrium (HWE). Although population subdivision significantly skewed allele frequencies across breeds for two of the SNPs, the deviations of observed heterozygosities compared with those expected within breeds were minimal. These results indicate that across-breed DNA sequence comparison is a reasonable strategy for identifying SNPs that are useful within many canine breeds.
Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.
We assessed the disease-causing potential of single nucleotide polymorphisms (SNPs) based on a simple set of sequence-based features. We focused on SNPs from the dbSNP database in G-protein-coupled receptors (GPCRs), a large class of important transmembrane (TM) proteins. Apart from the location of the SNP in the protein, we evaluated the predictive power of three major classes of features to differentiate between disease-causing mutations and neutral changes: (i) properties derived from amino-acid scales, such as volume and hydrophobicity; (ii) position-specific phylogenetic features reflecting evolutionary conservation, such as normalized site entropy, residue frequency and SIFT score; and (iii) substitution-matrix scores, such as those derived from the BLOSUM62, GRANTHAM and PHAT matrices. We validated our approach using a control dataset consisting of known disease-causing mutations and neutral variations. Logistic regression analyses indicated that position-specific phylogenetic features that describe the conservation of an amino acid at a specific site are the best discriminators of disease mutations versus neutral variations, and integration of all our features improves discrimination power. Overall, we identify 115 SNPs in GPCRs from dbSNP that are likely to be associated with disease and thus are good candidates for genotyping in association studies.
Single nucleotide polymorphisms (SNPs) in genes encoding drug-metabolizing enzymes, transporters, receptors, and other drug targets have been widely implicated as contributors to differences among individuals as regards the efficacy and toxicity of many medications, as well as the susceptibility to complex diseases. By combining the polymerase chain reaction (PCR) technique with direct sequencing, we screened genomic DNAs from 48 Japanese volunteers for SNPs in genes encoding three quinone oxidoreductases (NQO1, NQO2, and PIG3) and 17 sulfotransferases (SULT1A1, SULT1A2, SULT1A3, SULT1C1, SULT1C2, SULT2A1, SULT2B1, ST1B2, TPST1, TPST2, SULTX3, STE, CST, HNK-1 ST, CHST2, CHST4, and CHST5). In all, we identified 320 SNPs from these 20 loci: 22 within coding elements, 21 in 5' flanking regions, 10 in 5' untranslated regions, 223 in introns, 19 in 3' untranslated regions, and 25 in 3' flanking regions. The ratio of transitions to transversions was approximately 2.3 to 1. Of the 22 coding SNPs, 6 were nonsynonymous substitutions that resulted in amino-acid substitutions. The high-density SNP maps we constructed from this data for each of the quinone oxidoreductases and sulfotransferases examined here should provide useful information for investigations designed to detect association(s) between genetic variations and common diseases or responsiveness to drug therapy.
Single nucleotide polymorphisms (SNPs) are considered as very promising genetic markers for complex disease gene hunting. However, it has been demonstrated that there are significant ethnic differences in genetic variations. In order to investigate the genetic variations in the Korean population and their ethnic differences, a large number of SNPs of 161 disease candidate genes were collected from a publicly available SNP database and then tested for the distribution of allele frequency in the Korean population. Of all 458 SNPs tested, approximately 43.9% were polymorphic in the Korean population, whereas 44.5% were monomorphic. The remaining 11.6% were failed in the test. Significant differences have been observed when SNP allele frequency pattern of Koreans was compared with those of Caucasians and Africans, whereas this pattern was highly similar between Korean and Japanese populations. Our data indicate that although many of the SNPs available in publicly available database, especially coding-region SNPs (cSNPs), can be used as informative genetic markers for disease association studies, an extensive verification of public SNPs in a particular population studied should be undertaken prior to their association studies.
Single-nucleotide polymorphisms (SNPs) are commonly used to study genetics for common diseases and predict pharmacological response. The selection of likely informative SNPs in association studies depends on their allele frequencies and on the linkage disequilibrium (LD) between SNPs, both of which may show interethnic differences. Among three populations consisting of 207 Chinese, 858 French, and 395 Spanish, we compared the allele frequency distributions of 64 intragenic SNPs of 35 candidate genes for cardiovascular diseases. Twenty-eight of these SNPs from 12 genes were also examined for intragenic LD. About 20% of SNPs were restricted to Europeans, being monomorphic in Chinese, among them mostly nonsynonymous coding SNPs and noncoding SNPs. Only 1.6% of SNPs were specific in Chinese, commensurate with the detection of these SNPs almost exclusively in Caucasians. Similarly, these SNPs were more often rare (<0.1 minor allele frequency) in Chinese (44.3%) than in Europeans (31.1%). The variant allele frequencies and intermarker LDs in terms of D' and Delta(2) were highly correlated between French and Spanish populations (r = 0.98-0.99, p < 0.001). However, only moderate correlations of allele frequencies and D' were found between the Chinese and the European populations (r = 0.7 and 0.3, respectively) despite a high correlation of Delta(2) values (r = 0.8). These results suggest that ethnic considerations are important in the selection of SNPs for association studies of candidate genes, as this may affect the power of the study as well as the likelihood of asking relevant questions and getting medically meaningful answers.
We analyzed 13 single nucleotide polymorphisms (SNPs) within the apolipoprotein E (APOE) gene, to identify pairs of SNPs that interact in a non-additive manner to influence genotypic mean levels of the ApoE protein in blood. An overparameterized general linear model of two-SNP genotype means was applied to data from 456 female and 398 male unrelated European Americans from Rochester, MN, USA. We found statistically significant evidence for non-additivity between SNPs within the male sample, but not within the female sample. We observed nine pairs of SNPs with evidence of non-additivity at the alpha=0.05 level of statistical significance within the male sample, when approximately three were expected by chance. Five of the nine pairs involved three SNPs (560, 624 and 1163) that did not have a statistically significant influence when considered separately in a single-site analysis. Three of the nine pairs involving four SNPs (832, 1998, 3937 and 4951) showed significant evidence for non-additivity in at least one of two other male samples from Jackson, MS, USA and North Karelia, Finland. Although all four of these SNPs had a statistically significant influence in Rochester when considered separately, only SNP 3937 gave a significant result in the other male samples. The four SNPs are located in the promoter, intronic and exonic regions, and 3' to the polyadenylation signal in the APOE gene. Our study suggests that analyses that only consider SNPs located in exons and ignore contexts such as those indexed by gender and population, and disregard non-additivity of SNP effects, may inappropriately model the contribution of a gene to the genetic architecture of a trait that has a complex multifactorial etiology.
We describe the application of two different fluorescence-based techniques (ddNTP primer extension and single-strand conformation polymorphism (SSCP)) to the detection of single nucleotide polymorphisms (SNPs) by capillary electrophoresis. The ddNTP primer extension technique is based on the extension, in the presence of fluorescence-labeled dideoxy nucleotides (ddNTP, terminators), of an unlabeled oligonucleotide primer that binds to the complementary template immediately adjacent to the mutant nucleotide position. Given that there are no unlabeled dNTPs, a single ddNTP is added to its 3' end, resulting in a fluorescence-labeled primer extension product which is readily separated by capillary electrophoresis. On the other hand, the non-radioisotopic version of SSCP established in this study uses fluorescent dye to label the PCR products, which are also analyzed by capillary electrophoresis. These procedures were used to identify a well-defined SNP in exon 7 of the human p53 gene in DNA samples isolated from two human cell lines (CEM and THP-1 cells). The results revealed a heterozygous single-base transition (G to A) at nucleotide position 14071 in CEM cells, proving that both fluorescence-based ddNTP primer extension and SSCP are rapid, simple, robust, specific and with no ambiguity in interpretation for the detection of well-defined SNPs.