Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “SNP analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
PubMed2005

Large-scale SNP analysis reveals clustered and continuous patterns of human genetic variation.

Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

Chromosomes, Human, X↗
PubMed2006

[Y-SNP analysis of Bouyei, Gelao, Mulao, Maonan, and Zhuang populations in Guizhou].

To understand the patrilineal genetic structure of Baiyue ethnic group in Guizhou province, we studied the frequencies of Y-chromosome haplotypes which consisted of 10 single nucleotide polymorphisms (SNPs) by using the PCR-RFLP method. Five haplotypes were found in Baiyue ethnic group in Guizhou, among which H8 was the most common, whereas that of Miao in Guizhou tended to be H8, H11 and H12 haplotypes. Compared with Miao in Guizhou, the Guizhou Baiyue (excluding Sui and Dong) was significantly different and could be regarded as an independent ethnic group. Differences were also found in the same ethnic group among different areas.

Asian People↗
PubMed2005

Y chromosome SNP analysis using the single-base extension: a hierarchical multiplex design.

Single nucleotide polymorphisms (SNPs) are the most frequent polymorphisms described in the human genome, and their analysis is becoming an extensive routine in molecular biology, not only in the forensic field, but also in population and clinical genetics. In particular, SNPs located on the Y chromosome have a specific utility as forensic tools, and based on this fact, we have designed a strategy that allows us to identify the most frequent haplogroups in European populations. We selected 29 markers among the 245 binary polymorphisms described in the Y-Chromosome Consortium tree. The whole set was grouped into four multiplexes in a hierarchical way, allowing us to determine the final haplogroup using only one or two multiplexes. In this way, we only type in the best-case nine SNPs, and in the worst possible combination 17 SNPs, to define the haplogroup. The selected strategy to type the SNPs was a single-base extension method using the SNaPshot multiplex kit from Applied Biosystems, and detailed practical procedures are described here. With this hierarchical strategy adapted for European populations the massive typing of SNPs was avoided, and therefore the time and money involved in the study was also reduced.

Base Sequence↗
PubMed2005

A new HLA-DRB1 genotyping method using single nucleotide polymorphism (SNP) analysis with multiplex primer extension reactions and its application to mixed samples.

We have improved on conventional methods for HLA-DRB1 genotyping and devised a new method that is simple, cost-effective, and adequately applicable to routine forensic practice. This method consists of group-specific polymerase chain reaction (PCR) of the exon 2 region of the HLA-DRB1 gene and simultaneous detection of single nucleotide polymorphisms (SNPs) at multiple sites using multiplex primer extension reactions. With this method, we successfully detected HLA-DRB1 genotypes from the following materials: the peripheral blood of 142 donors, 6 aged saliva stains of known DRB1 genotype stored for 5-10 years at room temperature, 10 aged bloodstains of unknown DRB1 genotype stored for 29 years at room temperature, and minimal bloodstains and saliva stains from 3 donors of known DRB1 genotypes. Furthermore, we were able to type DRB1 alleles of the minor component in mixed samples at a proportion of 1/1,000 or 1/10,000. In a criminal case, DRB1 alleles detected from mixed bloodstains on a sword found at the scene enabled us to explain the case. This method is expected to be useful for forensic medicine.

Alleles↗
PubMed2003

SNP analysis and presentation in the Pharmacogenetics of Membrane Transporters Project.

The multidisciplinary UCSF Pharmacogenetics of Membrane Transporters project seeks to systematically identify sequence variants in transporters and to determine the functional significance of these variants through evaluation of relevant cellular and clinical phenotypes. The project is structured around four interacting cores: genomics, cellular phenotyping, clinical phenotyping, and bioinformatics. The bioinformatics core is responsible for collecting, storing, and analyzing the information obtained by the other cores and for presenting the results, in particular, for the genomic data. Most of this process is automated using locally developed software written in Python, an open source language well suited for rapid, modular development that meets requirements that are themselves constantly evolving. Here we present the details of transforming ABI trace file data into useful information for project investigators and a description of the types of data analysis and display that we have developed.

Amino Acid Sequence↗
PubMed2004

A highly reproducible and economically competitive SNP analysis of several well characterized human mutations.

During the last years several genetic markers have appeared which were extensively studied for their clinical consequences and impact. Therefore, we developed 14 new genetic tests using the TaqMan technology. The new test systems detect the alpha1-antitrypsin, ACE, apolipoprotein B-100, apolipoprotein E, factor V Leiden, prothrombin, HFE, MTHFR, COL1A1, VDR and HLA-B27 mutations. These new kits were compared to the established endonuclease restriction digestion and flow cytometry, respectively. The results showed, that the allelic discrimination assays (TaqMan method) were in 100% concordance with the formerly used digestion method. Flow cytometry revealed a lower specificity in contrast to the TaqMan PCR system. Thus, it could be demonstrated that the new TaqMan assays are robust, rapid and automated methods for high throughput applications which avoid time consuming (and therefore expensive) and difficult post-PCR steps.

Apolipoproteins B↗
PubMed2006

Improvements in the analysis strategy make single nucleotide polymorphism analysis a powerful tool in the detection and characterization of amplified chromosomal regions in human tumors.

OBJECTIVE: Single nucleotide polymorphism analysis (SNP) has recently been proposed as an alternative technique to comparative genomic hybridization (CGH) for defining loss of heterozygosity and gene copy number changes in a single experimental setup. In order to assess the potential of SNP analysis to complement or, ultimately, substitute CGH results, we applied both techniques to five primary tumor samples and two tumor cell lines. This was complemented by dilution experiments based on normal lymphocyte DNA to decipher the lower detection limit for genetic alterations. METHODS/RESULTS: Using an in-house software tool, we demonstrated that SNP analysis permits the generation of chromosomal alteration patterns that largely resemble conventional CGH ratio profiles of a given tumor/cell line. Moreover, compared to CGH, our SNP software tool allows a much more detailed definition of amplicon sizes and involved candidate genes. This advantage even persisted when the technique was applied to DNA with more than 60% of nontumor content. However, the detection of chromosomal losses is severely hampered by the presence of nontumor DNA so that the use of the SNP technique should be limited to tumor samples with more than 80% tumor DNA. CONCLUSION: SNP analysis is a very valuable tool for the detection and characterization of high-level chromosomal amplifications in the vast majority of primary tumor samples. Our software tool improves the analysis of SNP data and the presentation of the results, bridging the gap to existing CGH knowledge (http://bioinformatics.uni-muenster.de, 'Publications and Supplements').

Algorithms↗
PubMed2006

Integration of HapMap-based SNP pattern analysis and gene expression profiling reveals common SNP profiles for cancer therapy outcome predictor genes.

Recent completion of the initial phase of a haplotype map of human genome (www.hapmap.org) provides opportunity for integrative analysis on a genome-wide scale of microarray-based gene expression profiling and SNP variation patterns for discovery of cancer-causing genes and genetic markers of therapy outcome. Here we applied this approach for analysis of SNPs of cancer-associated genes, expression profiles of which predicts the likelihood of treatment failure and death after therapy in patients diagnosed with multiple types of cancer. Unexpectedly, this analysis reveals a common SNP pattern for a majority (60 of 74; 81%) of analyzed cancer treatment outcome predictor (CTOP) genes. Our analysis suggests that heritable germ-line genetic variations driven by geographically localized form of natural selection determining population differentiations may have a significant impact on cancer treatment outcome by influencing the individual's gene expression profile. We demonstrate a translational utility of this approach by building a highly informative CTOP algorithm combining prognostic power of multiple gene expression-based CTOP models derived from signatures of oncogenic pathways associated with activation of BMI1; Myc; Her2/neu; Ras; beta-catenin; Suz12; E2F; and CCND1 oncogenes. Application of a CTOP algorithm to large databases of early-stage breast and prostate tumors identifies cancer patients with 100% probability of a cure with existing cancer therapies as well as patients with nearly 100% likelihood of treatment failure, thus providing a clinically feasible framework essential for introduction of rational evidence-based individualized therapy selection and prescription protocols. Our analysis indicates that genetic determinants of human disease susceptibility and severity are encoded by population differentiation SNP variants. Evolution of these SNPs is driven by geographically-localized form of natural selection causing population differentiation. Recent analysis identifies a class of SNPs regulating gene expression in normal individuals and likely determining unique genome-wide expression profiles of each individual. We propose that critical disease-causing combinations of SNP variants arise from SNPs regulating mRNA levels and determining genome-wide haplotype patterns of individual's disease susceptibility.

Biomarkers, Tumor↗
PubMed2004

Assessing hematopoietic chimerism after allogeneic stem cell transplantation by multiplexed SNP genotyping using microarrays and quantitative analysis of SNP alleles.

Single-nucleotide polymorphisms (SNPs) have the potential to be particularly useful as markers for monitoring of chimerism after stem cell transplantation (SCT) because they can be analyzed by accurate and robust methods. We used a two-phased minisequencing strategy for monitoring chimerism after SCT. First, informative SNPs with alleles differing between donor and recipient were identified using a multiplex microarray-based minisequencing system screening 51 SNPs to ensure that multiple informative SNPs were detected in each donor-recipient pair. Secondly, the development of chimerism was followed up after SCT by sensitive, quantitative analysis of individual informative SNPs by applying the minisequencing method in a microtiter plate format. Using this panel of SNPs, we identified multiple informative SNPs in nine unrelated and in 16 related donor-recipient pairs. Samples from nine of the donor-recipient pairs taken at time points ranging from 1 month to 8 years after transplantation were available for analysis. In these samples, we monitored the allelic ratios of two or three informative SNPs in individual minisequencing reactions. The results agreed well with the data obtained by microsatellite analysis. Thus, we conclude that the two-phased minisequencing strategy is a useful approach in the following up of patients after SCT.

Alleles↗
PubMed2005

Digital single-nucleotide polymorphism analysis for allelic imbalance.

Digital single-nucleotide polymorphism (SNP) analysis is developed to amplify a single template from a pool of DNA samples, thereby generating the amplicons that are homogeneous in sequence. Different fluorophores are then applied as probes to detect and discriminate different alleles (paternal vs maternal alleles or wild-type vs mutant), which can be readily counted. In this way, digital SNP analysis transforms the exponential and analog signals from conventional polymerase chain reaction (PCR) to linear and digital ones. Digital SNP analysis has the following advantages. First, statistical analysis of the PCR products becomes available as the alleles can be directly counted. Second, this technology is designed to generate PCR products of the same size; therefore, DNA degradation would not be a problem as it commonly occurs when microsatellite markers are used to assess allelic status in clinical samples. Last, digital SNP analysis is designed to amplify a relatively small amount of DNA samples, which is available in some clinical samples. Digital SNP analysis has been applied in quantification of mutant alleles and detection of allelic imbalance in clinical specimens and it represents another example of the power of PCR and provides unprecedented opportunities for molecular genetic analysis.

Allelic Imbalance↗
PubMed2006

Interactive visual analysis of SNP data for rapid autozygosity mapping in consanguineous families.

Autozygosity mapping of recessive disorders using small numbers of highly inbred families is a powerful tool for disease gene identification. With the advent of cheap rapid methods for whole-genome SNP genotyping, data analysis, rather than laboratory work, has become rate-limiting for this approach. Here, we describe AutoSNPa, a computer program used for handling and visually presenting large amounts of SNP data, in such a way as to facilitate the rapid identification and subsequent scrutiny of autozygous regions.

Algorithms↗
PubMed2007

Genetic intra-tumour heterogeneity in epithelial ovarian cancer and its implications for molecular diagnosis of tumours.

Genetic analysis of solid tumours using DNA or cDNA expression microarrays may enable individualized treatment based on the profiles of genetic changes that are identified from each patient. This could result in better response to adjuvant chemotherapy and, consequently, improved clinical outcome. So far, most research studies that have tested the efficacy of such an approach have sampled only single areas of neoplastic tissue from tumours; this assumes that the genetic profile within solid tumours is homogeneous throughout. The aim of this study was to evaluate the extent of genetic intra-tumour heterogeneity (ITH) within a series of epithelial ovarian cancers. Several different regions (five to eight regions) of tumour tissue from 16 grade 3, serous epithelial ovarian cancers were analysed for genetic alterations using a combination of microsatellite analysis and single nucleotide polymorphism (SNP) analysis, in order to establish the extent of ITH. Maximum parsimony tree analysis was applied to the genetic data from each tumour to evaluate the clonal relationship between different regions within tumours. Extensive ITH was identified within all ovarian cancers using both microsatellite and SNP analysis. Evolutionary analysis of microsatellite data suggested that the origin of all tumours was monoclonal, but that subsequent clonal divergence created mixed populations of genetically distinct cells within the tumour. SNP analysis suggested that ITH was not restricted to random genetic changes, but affected genes that have an important functional role in ovarian cancer development. The frequent occurrence of ITH within epithelial ovarian cancers may have implications for the interpretation of genetic data generated from emerging technologies such as DNA and mRNA expression microarrays, and their use in the clinical management of patients with ovarian cancer. The basis of genetic ITH and the possible implications for molecular approaches to clinical diagnosis of ovarian cancers may apply to other tumour types.

Adenocarcinoma, Papillary↗