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At least 523 records · Page 29Linked to original sources
PubMed2005

Improving functional annotation of non-synonomous SNPs with information theory.

Automated functional annotation of nsSNPs requires that amino-acid residue changes are represented by a set of descriptive features, such as evolutionary conservation, side-chain volume change, effect on ligand-binding, and residue structural rigidity. Identifying the most informative combinations of features is critical to the success of a computational prediction method. We rank 32 features according to their mutual information with functional effects of amino-acid substitutions, as measured by in vivo assays. In addition, we use a greedy algorithm to identify a subset of highly informative features. The method is simple to implement and provides a quantitative measure for selecting the best predictive features given a set of features that a human expert believes to be informative. We demonstrate the usefulness of the selected highly informative features by cross-validated tests of a computational classifier, a support vector machine (SVM). The SVM's classification accuracy is highly correlated with the ranking of the input features by their mutual information. Two features describing the solvent accessibility of "wild-type" and "mutant" amino-acid residues and one evolutionary feature based on superfamily-level multiple alignments produce comparable overall accuracy and 6% fewer false positives than a 32-feature set that considers physiochemical properties of amino acids, protein electrostatics, amino-acid residue flexibility, and binding interactions.

Analysis of Variance↗
PubMed2001

Candidate genes and single nucleotide polymorphisms (SNPs) in the study of human disease.

The genomic revolution has generated an extraordinary resource, the catalog of variation within the human genome, for investigating biological, evolutionary and medical questions. Together with new, more efficient platforms for high-throughput genotyping, it is possible to begin to dissect genetic contributions to complex trait diseases, specifically examining common variants, such as the single nucleotide polymorphism (SNP). At the same time, these tools will make it possible to identify determinants of disease with the expectation of eventually, tailoring therapies based upon specific profiles. However, a number of methodological, practical and ethical issues must be addressed before the analysis of genetic variation becomes a standard of clinical medicine. The currents of variation in human biology are reviewed here, with a specific emphasis on future challenges and directions.

Base Sequence↗
PubMed2006

Promoter SNPs in G1/S checkpoint regulators and their impact on the susceptibility to childhood leukemia.

Mutations leading to the alteration of cell-cycle checkpoint functions are a common feature of most cancers. Because of the highly regulated nature of the cell cycle, it seems likely that variation in gene dosage of key components due to functional regulatory polymorphisms could play an important role in cancer development. Here we provide evidence of the involvement of promoter single-nucleotide polymorphisms (pSNPs) in the cyclin-dependent-kinase inhibitor genes CDKN2A, CDKN2B, CDKN1A, and CDKN1B in the etiology of childhood pre-B acute lymphoblastic leukemia (ALL). A case-control study, conducted in 240 patients with pre-B ALL and 277 healthy controls, combined with a family-based analysis using 135 parental trios, all of French-Canadian origin, were used to evaluate single-site genotypic as well as multilocus haplotypic associations for a total of 10 pSNPs. Using both study designs, we showed evidence of association between variants CDKN2A -222A, CDKN2B -593A, and CDKN1B -1608A, and an increased risk of ALL. These findings suggest that variable expression levels of cell-cycle inhibitor genes CDKN2A, CDKN2B, and CDKN1B due to regulatory polymorphisms could indeed influence the risk of childhood pre-B ALL and contribute to carcinogenesis.

Adolescent↗
PubMed2006

V-MitoSNP: visualization of human mitochondrial SNPs.

BACKGROUND: Mitochondrial single nucleotide polymorphisms (mtSNPs) constitute important data when trying to shed some light on human diseases and cancers. Unfortunately, providing relevant mtSNP genotyping information in mtDNA databases in a neatly organized and transparent visual manner still remains a challenge. Amongst the many methods reported for SNP genotyping, determining the restriction fragment length polymorphisms (RFLPs) is still one of the most convenient and cost-saving methods. In this study, we prepared the visualization of the mtDNA genome in a way, which integrates the RFLP genotyping information with mitochondria related cancers and diseases in a user-friendly, intuitive and interactive manner. The inherent problem associated with mtDNA sequences in BLAST of the NCBI database was also solved. DESCRIPTION: V-MitoSNP provides complete mtSNP information for four different kinds of inputs: (1) color-coded visual input by selecting genes of interest on the genome graph, (2) keyword search by locus, disease and mtSNP rs# ID, (3) visualized input of nucleotide range by clicking the selected region of the mtDNA sequence, and (4) sequences mtBLAST. The V-MitoSNP output provides 500 bp (base pairs) flanking sequences for each SNP coupled with the RFLP enzyme and the corresponding natural or mismatched primer sets. The output format enables users to see the SNP genotype pattern of the RFLP by virtual electrophoresis of each mtSNP. The rate of successful design of enzymes and primers for RFLPs in all mtSNPs was 99.1%. The RFLP information was validated by actual agarose electrophoresis and showed successful results for all mtSNPs tested. The mtBLAST function in V-MitoSNP provides the gene information within the input sequence rather than providing the complete mitochondrial chromosome as in the NCBI BLAST database. All mtSNPs with rs number entries in NCBI are integrated in the corresponding SNP in V-MitoSNP. CONCLUSION: V-MitoSNP is a web-based software platform that provides a user-friendly and interactive interface for mtSNP information, especially with regard to RFLP genotyping. Visual input and output coupled with integrated mtSNP information from MITOMAP and NCBI make V-MitoSNP an ideal and complete visualization interface for human mtSNPs association studies.

Base Sequence↗
PubMed2006

The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases.

Genetic epidemiologists have taken the challenge to identify genetic polymorphisms involved in the development of diseases. Many have collected data on large numbers of genetic markers but are not familiar with available methods to assess their association with complex diseases. Statistical methods have been developed for analyzing the relation between large numbers of genetic and environmental predictors to disease or disease-related variables in genetic association studies. In this commentary we discuss logistic regression analysis, neural networks, including the parameter decreasing method (PDM) and genetic programming optimized neural networks (GPNN) and several non-parametric methods, which include the set association approach, combinatorial partitioning method (CPM), restricted partitioning method (RPM), multifactor dimensionality reduction (MDR) method and the random forests approach. The relative strengths and weaknesses of these methods are highlighted. Logistic regression and neural networks can handle only a limited number of predictor variables, depending on the number of observations in the dataset. Therefore, they are less useful than the non-parametric methods to approach association studies with large numbers of predictor variables. GPNN on the other hand may be a useful approach to select and model important predictors, but its performance to select the important effects in the presence of large numbers of predictors needs to be examined. Both the set association approach and random forests approach are able to handle a large number of predictors and are useful in reducing these predictors to a subset of predictors with an important contribution to disease. The combinatorial methods give more insight in combination patterns for sets of genetic and/or environmental predictor variables that may be related to the outcome variable. As the non-parametric methods have different strengths and weaknesses we conclude that to approach genetic association studies using the case-control design, the application of a combination of several methods, including the set association approach, MDR and the random forests approach, will likely be a useful strategy to find the important genes and interaction patterns involved in complex diseases.

Editorial↗
PubMed2003

AACR Special Conference: SNPs, haplotypes, and cancer - applications in molecular epidemiology, Key Biscayne, Florida, USA, 13-17 September 2003.

This American Association for Cancer Research Special Conference brought together scientists with diverse expertise to address issues related to use of appropriate epidemiological, statistical, and laboratory methods to study the genetic epidemiology of cancer. Discussions focused on experiences with association studies using single nucleotide polymorphisms and haplotypes, their limitations, and what is needed to improve on the current 'state of the art'. Various studies were presented in different contexts, ranging from candidate gene studies to whole genome scans, and conducted in prospective cohorts, case-control studies, and other study designs. Common problems such as determining the probability that observed associations are false negative or false positive, the potential effects of admixture, and determining which polymorphisms to examine in which genes and in which populations were examined. Problems specific to haplotype analysis were discussed, with emphasis on haplotype block structures and on how to use haplotypes in analysis. Questions were also posed as to determining the functional relevance of single nucleotide polymorphisms in molecular epidemiology. Finally, future directions, using specific examples, were addressed.

DNA, Neoplasm↗
PubMed2006

Linkage disequilibria and haplotype structure of four SNPs of the interleukin 1 gene cluster in seven Asian Indian populations.

Variation at four single nucleotide polymorphism (SNP) sites of the interleukin 1 (IL1) gene cluster was investigated among 280 unrelated individuals, representing 7 caste groups from the state of Karnataka, India, and one European American community of Boston, Massachusetts. Allele and haplotype frequencies, strength of linkage disequilibrium, and signatures of recombination varied considerably among populations. Variable community sizes and traditions of consanguinity may account for the observed variation.

Cardiovascular Diseases↗
PubMed2003

SNPs: why do we care?

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Genetic Diseases, Inborn↗