"PolyMin": software for identification of the minimum number of polymorphisms required for haplotype and genotype differentiation

Background Analysis of allelic variation for relevant genes and monitoring chromosome segment transmission during selection are important approaches in plant breeding and ecology. To minimize the number of required molecular markers for this purpose is crucial due to cost and time constraints. To date, software for identification of the minimum number of required markers has been optimized for human genetics and is only partly matching the needs of plant scientists and breeders. In addition, different software packages with insufficient interoperability need to be combined to extract this information from available allele sequence data, resulting in an error-prone multi-step process of data handling. Results PolyMin, a computer program combining the detection of a minimum set of single nucleotide polymorphisms (SNPs) and/or insertions/deletions (INDELs) necessary for allele differentiation with the subsequent genotype differentiation in plant populations has been developed. Its efficiency in finding minimum sets of polymorphisms is comparable to other available program packages. Conclusion A computer program detecting the minimum number of SNPs for haplotype discrimination and subsequent genotype differentiation has been developed, and its performance compared to other relevant software. The main advantages of PolyMin, especially for plant scientists, is the integration of procedures from sequence analysis to polymorphism selection within a single program, including both haplotype and genotype differentiation

Selection of SNP subsets for association studies in candidate genes: comparison of the power of different strategies to detect single disease susceptibility locus effects

BACKGROUND: The recent advances in genotyping and molecular techniques have greatly increased the knowledge of the human genome structure. Millions of polymorphisms are reported and freely available in public databases. As a result, there is now a need to identify among all these data, the relevant markers for genetic association studies. Recently, several methods have been published to select subsets of markers, usually Single Nucleotide Polymorphisms (SNPs), that best represent genetic polymorphisms in the studied candidate gene or region. RESULTS: In this paper, we compared four of these selection methods, two based on haplotype information and two based on pairwise linkage disequilibrium (LD). The methods were applied to the genotype data on twenty genes with different patterns of LD and different numbers of SNPs. A measure of the efficiency of the different methods to select SNPs was obtained by comparing, for each gene and under several single disease susceptibility models, the power to detect an association that will be achieved with the selected SNP subsets. CONCLUSION: None of the four selection methods stands out systematically from the others. Methods based on pairwise LD information turn out to be the most interesting methods in a context of association study in candidate gene. In a context where the number of SNPs to be tested in a given region needs to be more limited, as in large-scale studies or wide genome scans, one of the two methods based on haplotype information, would be more suitable

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.Comment: Published in at http://dx.doi.org/10.1214/07-SS026 the Statistics Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical Statistics (http://www.imstat.org

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used

Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium

We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased

A Comparison of Five Methods for Selecting Tagging Single-Nucleotide Polymorphisms

Our goal was to compare methods for tagging single-nucleotide polymorphisms (tagSNPs) withrespect to the power to detect disease association under differing haplotype-disease associationmodels. We were also interested in the effect that SNP selection samples, consisting of eithercases, controls, or a mixture, would have on power. We investigated five previously describedalgorithms for choosing tagSNPS: two that picked SNPs based on haplotype structure (Chapmanhaplotypicand Stram), two that picked SNPs based on pair-wise allelic association (Chapman-allelicand Cousin), and one control method that chose equally spaced SNPs (Zhai). In two diseaseassociatedregions from the Genetic Analysis Workshop 14 simulated data, we tested theassociation between tagSNP genotype and disease over the tagSNP sets chosen by each methodfor each sampling scheme. This was repeated for 100 replicates to estimate power. The two allelicmethods chose essentially all SNPs in the region and had nearly optimal power. The two haplotypicmethods chose about half as many SNPs. The haplotypic methods had poor performance comparedto the allelic methods in both regions. We expected an improvement in power when the selectionsample contained cases; however, there was only moderate variation in power between thesampling approaches for each method. Finally, when compared to the haplotypic methods, thereference method performed as well or worse in the region with ancestral disease haplotypestructure

A model-based approach to selection of tag SNPs

BACKGROUND: Single Nucleotide Polymorphisms (SNPs) are the most common type of polymorphisms found in the human genome. Effective genetic association studies require the identification of sets of tag SNPs that capture as much haplotype information as possible. Tag SNP selection is analogous to the problem of data compression in information theory. According to Shannon's framework, the optimal tag set maximizes the entropy of the tag SNPs subject to constraints on the number of SNPs. This approach requires an appropriate probabilistic model. Compared to simple measures of Linkage Disequilibrium (LD), a good model of haplotype sequences can more accurately account for LD structure. It also provides a machinery for the prediction of tagged SNPs and thereby to assess the performances of tag sets through their ability to predict larger SNP sets. RESULTS: Here, we compute the description code-lengths of SNP data for an array of models and we develop tag SNP selection methods based on these models and the strategy of entropy maximization. Using data sets from the HapMap and ENCODE projects, we show that the hidden Markov model introduced by Li and Stephens outperforms the other models in several aspects: description code-length of SNP data, information content of tag sets, and prediction of tagged SNPs. This is the first use of this model in the context of tag SNP selection. CONCLUSION: Our study provides strong evidence that the tag sets selected by our best method, based on Li and Stephens model, outperform those chosen by several existing methods. The results also suggest that information content evaluated with a good model is more sensitive for assessing the quality of a tagging set than the correct prediction rate of tagged SNPs. Besides, we show that haplotype phase uncertainty has an almost negligible impact on the ability of good tag sets to predict tagged SNPs. This justifies the selection of tag SNPs on the basis of haplotype informativeness, although genotyping studies do not directly assess haplotypes. A software that implements our approach is available

Inferring Genomic Sequences

Recent advances in next generation sequencing have provided unprecedented opportunities for high-throughput genomic research, inexpensively producing millions of genomic sequences in a single run. Analysis of massive volumes of data results in a more accurate picture of the genome complexity and requires adequate bioinformatics support. We explore computational challenges of applying next generation sequencing to particular applications, focusing on the problem of reconstructing viral quasispecies spectrum from pyrosequencing shotgun reads and problem of inferring informative single nucleotide polymorphisms (SNPs), statistically covering genetic variation of a genome region in genome-wide association studies. The genomic diversity of viral quasispecies is a subject of a great interest, particularly for chronic infections, since it can lead to resistance to existing therapies. High-throughput sequencing is a promising approach to characterizing viral diversity, but unfortunately standard assembly software cannot be used to simultaneously assemble and estimate the abundance of multiple closely related (but non-identical) quasispecies sequences. Here, we introduce a new Viral Spectrum Assembler (ViSpA) for inferring quasispecies spectrum and compare it with the state-of-the-art ShoRAH tool on both synthetic and real 454 pyrosequencing shotgun reads from HCV and HIV quasispecies. While ShoRAH has an advanced error correction algorithm, ViSpA is better at quasispecies assembling, producing more accurate reconstruction of a viral population. We also foresee ViSpA application to the analysis of high-throughput sequencing data from bacterial metagenomic samples and ecological samples of eukaryote populations. Due to the large data volume in genome-wide association studies, it is desirable to find a small subset of SNPs (tags) that covers the genetic variation of the entire set. We explore the trade-off between the number of tags used per non-tagged SNP and possible overfitting and propose an efficient 2LR-Tagging heuristic

Genetic Characterisation of Neurodegenerative disorders

Our global population is ageing and an ever increasing number of elderly are affected with neurodegenerative diseases, including the subjects of the studies in this work, Alzheimer's disease (AD), Parkinson's disease (PD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). On strong evidence that several genes may influence the development of sporadic neurodegenerative diseases, the genetic association approach was used in the work of this thesis to identify the multiple variants of small effect that may modulate susceptibility to common, complex neurodegenerative diseases. It has been shown that the common genetic variation of one of these susceptibility genes, MAPT, that of the microtubule associated protein, tau, is an important genetic risk factor for neurodegenerative diseases. There are two major MAPT haplotypes at 17q21.31 designated as H1 and H2. In order to dissect the relationship between MAPT variants and the pathogenesis of neurodegenerative diseases, the architecture and distribution the major haplotypes of MAPT have been assessed. The distribution of H2 haplotype is almost exclusively in the Caucasian population, with other populations having H2 allele frequencies of essentially zero. A series of association studies of common variation of MAPT in PSP, CBD, AD and PD in different populations were performed in this work with the hypothesis that common molecular pathways are involved in these disorders. Multiple common variants of the H1 haplotypes were identified and one common haplotype, H1c, showed preferential association with PSP and AD. A whole-genome association study of PD was also undertaken in this study in order to detect if common genetic variability exerts a large effect in risk for disease in idiopathic PD. Twenty six candidate loci have been found in this whole-genome association study and they provide the basis for our investigation of disease causing genetic variants in idiopathic PD