Tag SNP Selection Research Articles

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The tag SNPs that we selected, from the PARD3 and MAGI2 genes, were not strongly connected by linkage disequilibrium (r2 < 0.7). It is therefore unlikely that the associations that were identified resulted from a bystander effect of associations with other truly causative genes that are flanking these 2 tight junction–related genes. The haploblock structure of each of these 2 large genes was such that it not only excluded neighboring genes, but also forced us for practical reasons to direct our tag SNP selection toward exons and exon–intron junctions, and thus to ignore the contribution of intronic tag SNPs. This could have let us to underestimate the real effect of these gene variants on the disease risks. Because they focused solely on MAGI2, instead of the entire tight junction pathway like we did, McGovern et al could effectively scan this gene in more detail for associations (Inflamm Bowel Dis 2008 Aug 21 [Epub ahead of print]). It is conventional wisdom that the associated common variants are unlikely to be the causative mutations, but that other, more rare, unidentified variants located on the same haplotypes are likely responsible for the effect. To find these causative rare variants in large genes like PARD3 and MAGI2 will be a daunting task, requiring new techniques like high-throughput resequencing. It should also be kept in mind that these causative variants may contribute to the risk by exerting only subtle effects on the intestinal barrier when under stress from external sources like gluten for celiac disease or intestinal microbes in IBD. The notion of mutations leading to dysfunctional proteins should probably be abandoned for complex diseases where multiple genetic and environmental factors in concert impose enhanced disease risk on the individual. A Common Barrier Defect for Celiac Disease and Ulcerative ColitisGastroenterologyVol. 135Issue 6PreviewWapenaar MC, Monsuur AJ, van Bodegraven AA, et al. Associations with tight junction genes PARD3 and MAGI2 in Dutch patients point to a common barrier defect for celiac disease and ulcerative colitis. Gut 2008;57:463–467. Full-Text PDF

Systematic polymorphism analysis of the CYP2D6 gene in four different geographical Han populations in mainland China

In this study, we systematically screened the polymorphisms of the whole CYP2D6 gene in the populations of four different geographical locations in China, namely, Shanghai, Shantou, Shenyang, and Xi'an, using a sample of 100 subjects from each population. Forty-eight different polymorphisms were detected as well as 12 novel ones. One novel nonsynonymous SNP was detected, and one novel intronic SNP was revealed that might inactivate a cryptic donor site 392 nucleotides downstream of the exon 6 natural donor site. In addition, the frequencies of some polymorphisms and alleles demonstrated significant differences among the four populations. Linkage disequilibrium analysis and tag SNP selection were performed separately for each population. Haplotypes were analyzed within the selected tag SNPs. Tag SNP selection and haplotype distributions showed differences across the four populations. This is the first large-scale study to analyze polymorphisms systematically across the whole CYP2D6 gene in the Chinese Han population.

Using Population Mixtures to Optimize the Utility of Genomic Databases: Linkage Disequilibrium and Association Study Design in India

When performing association studies in populations that have not been the focus of large-scale investigations of haplotype variation, it is often helpful to rely on genomic databases in other populations for study design and analysis - such as in the selection of tag SNPs and in the imputation of missing genotypes. One way of improving the use of these databases is to rely on a mixture of database samples that is similar to the population of interest, rather than using the single most similar database sample. We demonstrate the effectiveness of the mixture approach in the application of African, European, and East Asian HapMap samples for tag SNP selection in populations from India, a genetically intermediate region underrepresented in genomic studies of haplotype variation.

HapMap tagSNP transferability in multiple populations: General guidelines

Linkage disequilibrium (LD) has received much attention recently because of its value in localizing disease-causing genes. Due to the extensive LD between neighboring loci in the human genome, it is believed that a subset of the single nucleotide polymorphisms in a region (tagSNPs) can be selected to capture most of the remaining SNP variants. In this study, we examined LD patterns and HapMap tagSNP transferability in more than 300 individuals. A South Indian sample and an African Mbuti Pygmy population sample were included to evaluate the performance of HapMap tagSNPs in geographically distinct and genetically isolated populations. Our results show that HapMap tagSNPs selected with r 2 >= 0.8 can capture more than 85% of the SNPs in populations that are from the same continental group. Combined tagSNPs from HapMap CEU and CHB+JPT serve as the best reference for the Indian sample. The HapMap YRI are a sufficient reference for tagSNP selection in the Pygmy sample. In addition to our findings, we reviewed over 25 recent studies of tagSNP transferability and propose a general guideline for selecting tagSNPs from HapMap populations.

A new framework for the selection of tag SNPs by multimarker haplotypes

This paper proposes a new framework for the selection of tag SNPs based on haplotypes instead of on a single SNP. The tag SNPs found by this framework form a set of haplotypes completely predictive of the alleles of all untyped SNPs. We refer to this problem as MTMH, which is defined as follows: given a set of SNPs, find a minimum subset of SNPs (called tag SNPs) which defines a set of haplotypes completely predictive of the alleles of all untyped SNPs. The MTMH problem is solved by dividing into three subproblems, two of which are shown to be NP-hard. Several exact and approximation algorithms are proposed to solve these subproblems. We describe a framework which integrates these algorithms and develop a program called HapTagger for finding tag SNPs. HapTagger is compared with existing methods as well as the official tagging tool (called Haploview) of the International HapMap project using a variety of real data sets. Our theoretical analysis and experimental results indicate that HapTagger consistently identifies a smaller set of tag SNPs and runs much faster than existing methods. HapTagger avoids the need of incorporating a linkage disequilibrium statistic and thus significantly improves the computational efficiency. We also present an algorithm (specific to HapTagger) for reconstructing alleles of untyped SNPs. It is worth mentioning that these predictive haplotypes selected by HapTagger can be used as signatures of recent positive selection or co-evolution. HapTagger is available at http://www.csie.ntu.edu.tw/~kmchao/tools/HapTagger/.

Evaluation of HapMap data in six populations of European descent

We studied how well the European CEU samples used in the Haplotype Mapping Project (HapMap) represent five European populations by analyzing nuclear family samples from the Swedish, Finnish, Dutch, British and Australian (European ancestry) populations. The number of samples from each population (about 30 parent-offspring trios) was similar to that in the HapMap sample sets. A panel of 186 single nucleotide polymorphisms (SNPs) distributed over the 1.5 Mb region of the GRID2 gene on chromosome 4 was genotyped. The genotype data were compared pair-wise between the HapMap sample and the other population samples. Principal component analysis (PCA) was used to cluster the data from different populations with respect to allele frequencies and to define the markers responsible for observed variance. The only sample with detectable differences in allele frequencies was that from Kuusamo, Finland. This sample also separated from the others, including the other Finnish sample, in the PCA analysis. A set of tagSNPs was defined based on the HapMap data and applied to the samples. The tagSNPs were found to capture the genetic variation in the analyzed region at r(2)>0.8 at levels ranging from 95% in the Kuusamo sample to 87% in the Australian sample. To capture the maximal genetic variation in the region, the Kuusamo, HapMap and Australian samples required 58, 63 and 73 native tagSNPs, respectively. The HapMap CEU sample represents the European samples well for tagSNP selection, with some caution regarding estimation of allele frequencies in the Finnish Kuusamo sample, and a slight reduction in tagging efficiency in the Australian sample.

Snagger: A user-friendly program for incorporating additional information for tagSNP selection

BackgroundThere has been considerable effort focused on developing efficient programs for tagging single-nucleotide polymorphisms (SNPs). Many of these programs do not account for potential reduced genomic coverage resulting from genotyping failures nor do they preferentially select SNPs based on functionality, which may be more likely to be biologically important.ResultsWe have developed a user-friendly and efficient software program, Snagger, as an extension to the existing open-source software, Haploview, which uses pairwise r2 linkage disequilibrium between single nucleotide polymorphisms (SNPs) to select tagSNPs. Snagger distinguishes itself from existing SNP selection algorithms, including Tagger, by providing user options that allow for: (1) prioritization of tagSNPs based on certain characteristics, including platform-specific design scores, functionality (i.e., coding status), and chromosomal position, (2) efficient selection of SNPs across multiple populations, (3) selection of tagSNPs outside defined genomic regions to improve coverage and genotyping success, and (4) picking of surrogate tagSNPs that serve as backups for tagSNPs whose failure would result in a significant loss of data. Using HapMap genotype data from ten ENCODE regions and design scores for the Illumina platform, we show similar coverage and design score distribution and fewer total tagSNPs selected by Snagger compared to the web server Tagger.ConclusionSnagger improves upon current available tagSNP software packages by providing a means for researchers to select tagSNPs that reliably capture genetic variation across multiple populations while accounting for significant genotyping failure risk and prioritizing on SNP-specific characteristics.

SNP selection for genes of iron metabolism in a study of genetic modifiers of hemochromatosis.

BackgroundWe report our experience of selecting tag SNPs in 35 genes involved in iron metabolism in a cohort study seeking to discover genetic modifiers of hereditary hemochromatosis.MethodsWe combined our own and publicly available resequencing data with HapMap to maximise our coverage to select 384 SNPs in candidate genes suitable for typing on the Illumina platform.ResultsValidation/design scores above 0.6 were not strongly correlated with SNP performance as estimated by Gentrain score. We contrasted results from two tag SNP selection algorithms, LDselect and Tagger. Varying r2 from 0.5 to 1.0 produced a near linear correlation with the number of tag SNPs required. We examined the pattern of linkage disequilibrium of three levels of resequencing coverage for the transferrin gene and found HapMap phase 1 tag SNPs capture 45% of the ≥ 3% MAF SNPs found in SeattleSNPs where there is nearly complete resequencing. Resequencing can reveal adjacent SNPs (within 60 bp) which may affect assay performance. We report the number of SNPs present within the region of six of our larger candidate genes, for different versions of stock genotyping assays.ConclusionA candidate gene approach should seek to maximise coverage, and this can be improved by adding to HapMap data any available sequencing data. Tag SNP software must be fast and flexible to data changes, since tag SNP selection involves iteration as investigators seek to satisfy the competing demands of coverage within and between populations, and typability on the technology platform chosen.

Supervised learning-based tagSNP selection for genome-wide disease classifications

BackgroundComprehensive evaluation of common genetic variations through association of single nucleotide polymorphisms (SNPs) with complex human diseases on the genome-wide scale is an active area in human genome research. One of the fundamental questions in a SNP-disease association study is to find an optimal subset of SNPs with predicting power for disease status. To find that subset while reducing study burden in terms of time and costs, one can potentially reconcile information redundancy from associations between SNP markers.ResultsWe have developed a feature selection method named Supervised Recursive Feature Addition (SRFA). This method combines supervised learning and statistical measures for the chosen candidate features/SNPs to reconcile the redundancy information and, in doing so, improve the classification performance in association studies. Additionally, we have proposed a Support Vector based Recursive Feature Addition (SVRFA) scheme in SNP-disease association analysis.ConclusionsWe have proposed using SRFA with different statistical learning classifiers and SVRFA for both SNP selection and disease classification and then applying them to two complex disease data sets. In general, our approaches outperform the well-known feature selection method of Support Vector Machine Recursive Feature Elimination and logic regression-based SNP selection for disease classification in genetic association studies. Our study further indicates that both genetic and environmental variables should be taken into account when doing disease predictions and classifications for the most complex human diseases that have gene-environment interactions.

Generating samples for association studies based on HapMap data

BackgroundWith the completion of the HapMap project, a variety of computational algorithms and tools have been proposed for haplotype inference, tag SNP selection and genome-wide association studies. Simulated data are commonly used in evaluating these new developed approaches. In addition to simulations based on population models, empirical data generated by perturbing real data, has also been used because it may inherit specific properties from real data. However, there is no tool that is publicly available to generate large scale simulated variation data by taking into account knowledge from the HapMap project.ResultsA computer program (gs) was developed to quickly generate a large number of samples based on real data that are useful for a variety of purposes, including evaluating methods for haplotype inference, tag SNP selection and association studies. Two approaches have been implemented to generate dense SNP haplotype/genotype data that share similar local linkage disequilibrium (LD) patterns as those in human populations. The first approach takes haplotype pairs from samples as inputs, and the second approach takes patterns of haplotype block structures as inputs. Both quantitative and qualitative traits have been incorporated in the program. Phenotypes are generated based on a disease model, or based on the effect of a quantitative trait nucleotide, both of which can be specified by users. In addition to single-locus disease models, two-locus disease models have also been implemented that can incorporate any degree of epistasis. Users are allowed to specify all nine parameters in a 3 × 3 penetrance table. For several commonly used two-locus disease models, the program can automatically calculate penetrances based on the population prevalence and marginal effects of a disease that users can conveniently specify.ConclusionThe program gs can effectively generate large scale genetic and phenotypic variation data that can be used for evaluating new developed approaches. It is freely available from the authors' web site at .

A new model of multi-marker correlation for genome-wide tag SNP selection.

Tag SNP selection is an important problem in computational biology and genetics because a small set of tag SNP markers may help reduce the cost of genotyping and thus genome-wide association studies. Several methods for selecting a smallest possible set of tag SNPs based on different formulations of tag SNP selection (block-based or genome-wide) and mathematical models of marker correlation have been investigated in the literature. In this paper, we propose a new model of multi-marker correlation for genome-wide tag SNP selection, and a simple greedy algorithm to select a smallest possible set of tag SNPs according to the model. Our experimental results on several real datasets from the HapMap project demonstrate that the new model yields more succinct tag SNP sets than the previous methods.

Statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic study for complex diseases

Recent advances of information technology in biomedical sciences and other applied areas have created numerous large diverse data sets with a high dimensional feature space, which provide us a tremendous amount of information and new opportunities for improving the quality of human life. Meanwhile, great challenges are also created driven by the continuous arrival of new data that requires researchers to convert these raw data into scientific knowledge in order to benefit from it. Association studies of complex diseases using SNP data have become more and more popular in biomedical research in recent years. In this paper, we present a review of recent statistical advances and challenges for analyzing correlated high dimensional SNP data in genomic association studies for complex diseases. The review includes both general feature reduction approaches for high dimensional correlated data and more specific approaches for SNPs data, which include unsupervised haplotype mapping, tag SNP selection, and supervised SNPs selection using statistical testing/scoring, statistical modeling and machine learning methods with an emphasis on how to identify interacting loci.

Genome-wide selection of tag SNPs using multiple-marker correlation

The tag SNP approach is a valuable tool in whole genome association studies, and a variety of algorithms have been proposed to identify the optimal tag SNP set. Currently, most tag SNP selection is based on two-marker (pairwise) linkage disequilibrium (LD). Recent literature has shown that multiple-marker LD also contains useful information that can further increase the genetic coverage of the tag SNP set. Thus, tag SNP selection methods that incorporate multiple-marker LD are expected to have advantages in terms of genetic coverage and statistical power. We propose a novel algorithm to select tag SNPs in an iterative procedure. In each iteration loop, the SNP that captures the most neighboring SNPs (through pair-wise and multiple-marker LD) is selected as a tag SNP. We optimize the algorithm and computer program to make our approach feasible on today's typical workstations. Benchmarked using HapMap release 21, our algorithm outperforms standard pair-wise LD approach in several aspects. (i) It improves genetic coverage (e.g. by 7.2% for 200 K tag SNPs in HapMap CEU) compared to its conventional pair-wise counterpart, when conditioning on a fixed tag SNP number. (ii) It saves genotyping costs substantially when conditioning on fixed genetic coverage (e.g. 34.1% saving in HapMap CEU at 90% coverage). (iii) Tag SNPs identified using multiple-marker LD have good portability across closely related ethnic groups and (iv) show higher statistical power in association tests than those selected using conventional methods. A computer software suite, multiTag, has been developed based on this novel algorithm. The program is freely available by written request to the author at ke_hao@merck.com

Effectiveness of in silico TagSNP Selection Methods: Virtual Analysis of the Genotypes of Pharmacogenetic Genes

SNP tagging has been recently introduced, and the use of this strategy reduces the dimension of disease association studies and eventually saves on genotyping costs. There is no single set of tagging SNPs (tagSNPs) that will satisfy every association study design; thus, many different methods have been introduced. We evaluated various tagSNP selection methods using known haplotype data of pharmacogenetic genes. We also compared the selected tagSNPs among different ethnic groups. We collected genotype data for the NAT2 and CYP2D6 genes from the previously published literature where the linkage phase was resolved directly through molecular haplotyping. Three computational tagSNP selection methods (ldSelect, Tagger and TagIT software) were evaluated with these data sets. Tagging effectiveness and efficiency were variable in all three tagSNP selection methods. No tagSNP sets were identical among the different ethnic groups. The haplotype r(2)-based method was more effective in determining genotype-phenotype correlation than the other methods employed. All of the three computational tagSNP selection methods showed acceptable efficiency and effectiveness. The selected tagSNPs were different from each other among the different ethnic groups.

TAGster: efficient selection of LD tag SNPs in single or multiple populations

Genetic association studies increasingly rely on the use of linkage disequilibrium (LD) tag SNPs to reduce genotyping costs. We developed a software package TAGster to select, evaluate and visualize LD tag SNPs both for single and multiple populations. We implement several strategies to improve the efficiency of current LD tag SNP selection algorithms: (1) we modify the tag SNP selection procedure of Carlson et al. to improve selection efficiency and further generalize it to multiple populations. (2) We propose a redundant SNP elimination step to speed up the exhaustive tag SNP search algorithm proposed by Qin et al. (3) We present an additional multiple population tag SNP selection algorithm based on the framework of Howie et al., but using our modified exhaustive search procedure. We evaluate these methods using resequenced candidate gene data from the Environmental Genome Project and show improvements in both computational and tagging efficiency. The software Package TAGster is freely available at http://www.niehs.nih.gov/research/resources/software/tagster/

Power analysis for genome-wide association studies

BackgroundGenome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required.ResultsThe power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs.ConclusionGenome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.

Recombination and linkage disequilibrium in Arabidopsis thaliana

Linkage disequilibrium (LD) is a major aspect of the organization of genetic variation in natural populations. Here we describe the genome-wide pattern of LD in a sample of 19 Arabidopsis thaliana accessions using 341,602 non-singleton SNPs. LD decays within 10 kb on average, considerably faster than previously estimated. Tag SNP selection algorithms and 'hide-the-SNP' simulations suggest that genome-wide association mapping will require only 40%-50% of the observed SNPs, a reduction similar to estimates in a sample of African Americans. An Affymetrix genotyping array containing 250,000 SNPs has been designed based on these results; we demonstrate that it should have more than adequate coverage for genome-wide association mapping. The extent of LD is highly variable, and we find clear evidence of recombination hotspots, which seem to occur preferentially in intergenic regions. LD also reflects the action of selection, and it is more extensive between nonsynonymous polymorphisms than between synonymous polymorphisms.

Tag SNP selection for candidate gene association studies using HapMap and gene resequencing data

HapMap provides linkage disequilibrium (LD) information on a sample of 3.7 million SNPs that can be used for tag SNP selection in whole-genome association studies. HapMap can also be used for tag SNP selection in candidate genes, although its performance has yet to be evaluated against gene resequencing data, where there is near-complete SNP ascertainment. The Environmental Genome Project (EGP) is the largest gene resequencing effort to date with over 500 resequenced genes. We used HapMap data to select tag SNPs and calculated the proportions of common SNPs (MAF>or=0.05) tagged (rho2>or=0.8) for each of 127 EGP Panel 2 genes where individual ethnic information was available. Median gene-tagging proportions are 50, 80 and 74% for African, Asian, and European groups, respectively. These low gene-tagging proportions may be problematic for some candidate gene studies. In addition, although HapMap targeted nonsynonymous SNPs (nsSNPs), we estimate only approximately 30% of nonsynonymous SNPs in EGP are in high LD with any HapMap SNP. We show that gene-tagging proportions can be improved by adding a relatively small number of tag SNPs that were selected based on resequencing data. We also demonstrate that ethnic-mixed data can be used to improve HapMap gene-tagging proportions, but are not as efficient as ethnic-specific data. Finally, we generalized the greedy algorithm proposed by Carlson et al (2004) to select tag SNPs for multiple populations and implemented the algorithm into a freely available software package mPopTag.

QuickSNP: an automated web server for selection of tagSNPs

Although large-scale genetic association studies involving hundreds to thousands of SNPs have become feasible, the associated cost is substantial. Even with the increased efficiency introduced by the use of tagSNPs, researchers are often seeking ways to maximize resource utilization given a set of SNP-based gene-mapping goals. We have developed a web server named QuickSNP in order to provide cost-effective selection of SNPs, and to fill in some of the gaps in existing SNP selection tools. One useful feature of QuickSNP is the option to select only gene-centric SNPs from a chromosomal region in an automated fashion. Other useful features include automated selection of coding non-synonymous SNPs, SNP filtering based on inter-SNP distances and information regarding the availability of genotyping assays for SNPs and whether they are present on whole genome chips. The program produces user-friendly summary tables and results, and a link to a UCSC Genome Browser track illustrating the position of the selected tagSNPs in relation to genes and other genomic features. We hope the unique combination of features of this server will be useful for researchers aiming to select markers for their genotyping studies. The server is freely available and can be accessed at the URL http://bioinformoodics.jhmi.edu/quickSNP.pl.

GEVALT: An integrated software tool for genotype analysis

BackgroundGenotype information generated by individual and international efforts carries the promise of revolutionizing disease studies and the association of phenotypes with alleles and haplotypes. Given the enormous amounts of public genotype data, tools for analyzing, interpreting and visualizing these data sets are of critical importance to researchers. In past works we have developed algorithms for genotypes phasing and tag SNP selection, which were shown to be quick and accurate. Both algorithms were available until now only as batch executables.ResultsHere we present GEVALT (GEnotype Visualization and ALgorithmic Tool), a software package designed to simplify and expedite the process of genotype analysis, by providing a common interface to several tasks relating to such analysis. GEVALT combines the strong visual abilities of Haploview with our quick and powerful algorithms for genotypes phasing (GERBIL), tag SNP selection (STAMPA) and permutation testing for evaluating significance of association. All of the above are provided in a visually appealing and interactive interface.ConclusionGEVALT is an integrated viewer that uses state of the art phasing and tag SNP selection algorithms. By streamlining the application of GERBIL and STAMPA together with strong visualization for assessment of the results, GEVALT makes the algorithms accessible to the broad community of researchers in genetics.