Thu, 21 Aug 2008 07:01:55 BST CiteULike: hms's library [27 articles] http://www.citeulike.org/user/hms CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/hms/article/2191976 <i></i> PLoS ONE: Correction of Population Stratification in Large Multi-Ethnic Association Studies 2008-01-03T18:44:06-00:00 alternatives data hapmap population_genetics snps http://www.citeulike.org/user/hms/article/1780777 <i>Nature, Vol. 449, No. 7164. (18 October 2007), pp. 913-918.</i> Genome-wide detection and characterization of positive selection in human populations Pardis Sabeti Patrick Varilly Ben Fry Jason Lohmueller Elizabeth Hostetter Chris Cotsapas Xiaohui Xie Elizabeth Byrne Steven Mccarroll Rachelle Gaudet Stephen Schaffner Eric Lander doi:10.1038/nature06250 Nature, Vol. 449, No. 7164. (18 October 2007), pp. 913-918. 2007-10-17T17:54:19-00:00 2007 Nature 449 7164 913 918 Nature Publishing Group data hapmap population_genetics http://www.citeulike.org/user/hms/article/1609048 <i>Genome Res., Vol. 14, No. 8. (1 August 2004), pp. 1493-1500.</i><br /><br />High-density SNP screening of panels of inbred mouse strains has been proposed as a method to accelerate the identification of genes associated with complex biomedical phenotypes. To evaluate the potential of these studies, a more detailed understanding of the fine structure of sequence variation across inbred mouse strains is needed. Here, we use high-density oligonucleotide arrays to discover an extremely dense set of SNPs in 13 classical and two wild-derived inbred strains in five genomic intervals totaling 4.6 Mb of DNA sequence, and then analyze the segmental haplotype structure defined by these high-density SNPs. This analysis reveals segments ranging from 12 to 608 kb in length within which the inbred strains have a simple and distinct phylogenetic relationship with typically two or three clades accounting for the 13 classical strains examined. The phylogenetic relationships among strains change abruptly and unpredictably from segment to segment, and are distinct in each of the five genomic regions examined. The data suggest that at least 12 strains would need to be resequenced for exhaustive SNP discovery in every region of the mouse genome, that [~]97% of the variation among inbred strains is ancestral (between clades) and [~]3% private (within clades), and provides critical insights into the proposed use of panels of inbred strains to identify genes underlying quantitative trait loci. 10.1101/gr.2627804 Segmental Phylogenetic Relationships of Inbred Mouse Strains Revealed by Fine-Scale Analysis of Sequence Variation Across 4.6 Mb of Mouse Genome Kelly Frazer Claire Wade David Hinds Nila Patil David Cox Mark Daly doi:10.1101/gr.2627804 Genome Res., Vol. 14, No. 8. (1 August 2004), pp. 1493-1500. 2007-08-30T19:13:04-00:00 2004 Genome Res. 14 8 1493 1500 data mouse population_genetics snps http://www.citeulike.org/user/hms/article/1609042 <i>Nat Meth, Vol. 4, No. 9. (2007), pp. 679-680.</i> SNPs matter: impact on detection of differential expression Nicole Walter Shannon Mcweeney Sandra Peters John Belknap Robert Hitzemann Kari Buck doi:10.1038/nmeth0907-679 Nat Meth, Vol. 4, No. 9. (2007), pp. 679-680. 2007-08-30T19:06:47-00:00 2007 Nat Meth 4 9 679 680 data gene_expression snps http://www.citeulike.org/user/hms/article/1606241 <i></i> PLoS ONE: Why Men Matter: Mating Patterns Drive Evolution of Human Lifespan 2007-08-29T22:01:21-00:00 random_reading http://www.citeulike.org/user/hms/article/1583521 <i></i> A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase 2007-08-22T16:35:29-00:00 fastphase hapmap population_genetics http://www.citeulike.org/user/hms/article/1406134 <i>Nat Genet (17 June 2007)</i><br /><br />Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets. A new multipoint method for genome-wide association studies by imputation of genotypes. Jonathan Marchini Bryan Howie Simon Myers Gil McVean Peter Donnelly doi:10.1038/ng2088 Nat Genet (17 June 2007) 2007-06-23T07:42:01-00:00 2007 Nat Genet 1061-4036 data hapmap population_genetics wtccc http://www.citeulike.org/user/hms/article/1459588 <i>Mammalian Genome, Vol. 14, No. 7. (1 July 2003), pp. 464-471.</i><br /><br />Genetic factors independent of those affecting plasma lipid levels are a major contributor to risk for atherosclerosis in humans, yet the basis for these is poorly understood. This study examined plasma lipids and diet-induced atherosclerosis in 16-month-old female mice of strains C56BL/6J and DBA/2J. Mice of the parental strains, from recombinant inbred strains derived from these (BXD RI), and F 2 progeny were fed an atherogenic diet for 16 weeks, beginning at 1 year of age. This induced atherosclerotic lesion formation in both parental strains, accompanied by increased plasma LDL levels. However, individual BXD RI strains and the BXD F 2 mice demonstrated a range of atherosclerotic lesion formation that was not or at best weakly correlated with plasma lipid levels. Quantitative trait locus (QTL) analysis of the BXD F 2 mice identified a locus with significant linkage (lod 4.5) for aortic lesion size on Chromosome (Chr) 10 that was independent of plasma lipids. Other loci with suggestive or significant linkage for various plasma lipid measures were identified on Chr 2, 3, 4, 5, 6, 7, 11, and 17. In this intercross, the genes primarily influencing atherosclerosis are distinct from those controlling plasma lipid levels. Genetic loci for diet-induced atherosclerotic lesions and plasma lipids in mice Veronica Colinayo Jian-Hua Qiao Xuping Wang Kelly Krass Eric Schadt Aldons Lusis Thomas Drake doi:10.1007/s00335-002-2187-2 Mammalian Genome, Vol. 14, No. 7. (1 July 2003), pp. 464-471. 2007-07-16T16:29:19-00:00 2003 Mammalian Genome 14 7 464 471 data http://www.citeulike.org/user/hms/article/833067 <i>Nature, Vol. 422, No. 6929. (20 March 2003), pp. 297-302.</i><br /><br />Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci. Genetics of gene expression surveyed in maize, mouse and man. EE Schadt SA Monks TA Drake AJ Lusis N Che V Colinayo TG Ruff SB Milligan JR Lamb G Cavet PS Linsley M Mao RB Stoughton SH Friend doi:10.1038/nature01434 Nature, Vol. 422, No. 6929. (20 March 2003), pp. 297-302. 2006-09-06T22:53:14-00:00 2003 Nature 0028-0836 422 6929 297 302 data gene_expression population_genetics http://www.citeulike.org/user/hms/article/481078 <i>Human Molecular Genetics, Vol. 15, No. 3. (1 February 2006), pp. 481-492.</i> Integrative genetic analysis of transcription modules: towards filling the gap between genetic loci and inherited traits Hongqiang Li Hao Chen Kenneth Manly Elissa Chesler Lu Lu Jintao Wang Mi Zhou Robert Williams Yan Cui doi:10.1093/hmg/ddi462 Human Molecular Genetics, Vol. 15, No. 3. (1 February 2006), pp. 481-492. 2006-01-25T22:25:45-00:00 2006 Human Molecular Genetics 0964-6906 15 3 481 492 Oxford University Press data gene_expression population_genetics http://www.citeulike.org/user/hms/article/1449803 <i>PLoS Computational Biology, Vol. 3, No. 4. (1 April 2007), e69.</i><br /><br />To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models. Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations Jun Zhu Matthew Wiener Chunsheng Zhang Arthur Fridman Eric Minch Pek Lum Jeffrey Sachs Eric Schadt doi:10.1371/journal.pcbi.0030069 PLoS Computational Biology, Vol. 3, No. 4. (1 April 2007), e69. 2007-07-11T18:54:22-00:00 2007 PLoS Computational Biology 3 4 e69 data gene_expression population_genetics http://www.citeulike.org/user/hms/article/1119761 <i>BMC Bioinformatics, Vol. 8 (23 February 2007), 61.</i> Factor analysis for gene regulatory networks and transcription factor activity profiles Iosifina Pournara Lorenz Wernisch doi:10.1186/1471-2105-8-61 BMC Bioinformatics, Vol. 8 (23 February 2007), 61. 2007-02-24T09:49:22-00:00 2007 BMC Bioinformatics 1471-2105 8 61 gene_expression http://www.citeulike.org/user/hms/article/1418697 <i></i><br /><br />Background An alternative to standard approaches to uncover biologically meaningful structures in micro array data is to treat the data as a blind source separation (BSS) problem. BSS attempts to separate a mixture of signals into their different sources and refers to the problem of recovering signals from several observed linear mixtures. In the context of micro array data, "sources" may correspond to specific cellular responses or to co-regulated genes. Results We applied independent component analysis (ICA) to three different microarray data sets; two tumor data sets and one time series experiment. To obtain reliable components we used iterated ICA to estimate component centrotypes. We found that many of the low ranking components indeed may show a strong biological coherence and hence be of biological significance. Generally ICA achieved a higher resolution when compared with results based on correlated expression and a larger number of gene clusters with significantly enriched for gene ontology (GO) categories. In addition, components characteristic for molecular subtypes and for tumors with specific chromosomal translocations were identified. ICA also identified more than one gene clusters significant for the same GO categories and hence disclosed a higher level of biological heterogeneity, even within coherent groups of genes. Conclusion Although the ICA approach primarily detects hidden variables, these surfaced as highly correlated genes in time series data and in one instance in the tumor data. This further strengthens the biological relevance of latent variables detected by ICA. Independent component analysis reveals new and biologically significant structures in micro array data Attila Frigyesi Srinivas Veerla David Lindgren Mattias Höglund 2007-06-28T09:26:04-00:00 gene_expression http://www.citeulike.org/user/hms/article/554013 <i>Bioinformatics, Vol. 22, No. 6. (15 March 2006), pp. 739-746.</i> Bayesian sparse hidden components analysis for transcription regulation networks Chiara Sabatti Gareth James doi:10.1093/bioinformatics/btk017 Bioinformatics, Vol. 22, No. 6. (15 March 2006), pp. 739-746. 2006-03-16T11:16:14-00:00 2006 Bioinformatics 1367-4803 22 6 739 746 Oxford University Press gene_expression http://www.citeulike.org/user/hms/article/1410577 <i>Am J Hum Genet, Vol. 70, No. 1. (January 2002), pp. 157-169.</i><br /><br />Haplotypes have gained increasing attention in the mapping of complex-disease genes, because of the abundance of single-nucleotide polymorphisms (SNPs) and the limited power of conventional single-locus analyses. It has been shown that haplotype-inference methods such as Clark's algorithm, the expectation-maximization algorithm, and a coalescence-based iterative-sampling algorithm are fairly effective and economical alternatives to molecular-haplotyping methods. To contend with some weaknesses of the existing algorithms, we propose a new Monte Carlo approach. In particular, we first partition the whole haplotype into smaller segments. Then, we use the Gibbs sampler both to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can accurately and rapidly infer haplotypes for a large number of linked SNPs. By using a wide variety of real and simulated data sets, we demonstrate the advantages of our Bayesian algorithm, and we show that it is robust to the violation of Hardy-Weinberg equilibrium, to the presence of missing data, and to occurrences of recombination hotspots. Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. T Niu ZS Qin X Xu JS Liu Am J Hum Genet, Vol. 70, No. 1. (January 2002), pp. 157-169. 2007-06-25T12:27:43-00:00 2002 Am J Hum Genet 0002-9297 70 1 157 169 hapmap population_genetics http://www.citeulike.org/user/hms/article/1410569 <i>Genome Res., Vol. 11, No. 10. (1 October 2001), pp. 1716-1724.</i><br /><br />Haplotype analysis of disease chromosomes can help identify probable historical recombination events and localize disease mutations. Most available analyses use only marginal and pairwise allele frequency information. We have developed a Bayesian framework that utilizes full haplotype information to overcome various complications such as multiple founders, unphased chromosomes, data contamination, and incomplete marker data. A stochastic model is used to describe the dependence structure among several variables characterizing the observed haplotypes, for example, the ancestral haplotypes and their ages, mutation rate, recombination events, and the location of the disease mutation. An efficient Markov chain Monte Carlo algorithm was developed for computing the estimates of the quantities of interest. The method is shown to perform well in both real data sets (cystic fibrosis data and Friedreich ataxia data) and simulated data sets. The program that implements the proposed method, BLADE, as well as the two real datasets, can be obtained from http://www.fas.harvard.edu/~junliu/TechRept/01folder/diseq_prog.tar.gz. 10.1101/gr.194801 Bayesian Analysis of Haplotypes for Linkage Disequilibrium Mapping Jun Liu Chiara Sabatti Jun Teng Bronya Keats Neil Risch doi:10.1101/gr.194801 Genome Res., Vol. 11, No. 10. (1 October 2001), pp. 1716-1724. 2007-06-25T12:21:10-00:00 2001 Genome Res. 11 10 1716 1724 hapmap population_genetics http://www.citeulike.org/user/hms/article/1399026 <i></i> Assessing Population Differentiation and Isolation from Single-Nucleotide Polymorphism Data George Nicholson Albert Smith Frosti Jónsson Ómar Gústafsson Kári Stefánsson Peter Donnelly 2007-06-19T17:48:35-00:00 hapmap population_genetics http://www.citeulike.org/user/hms/article/1269669 <i>Genetics, Vol. 164, No. 4. (August 2003), pp. 1567-1587.</i><br /><br />We describe extensions to the method of Pritchard et al. for inferring population structure from multilocus genotype data. Most importantly, we develop methods that allow for linkage between loci. The new model accounts for the correlations between linked loci that arise in admixed populations ("admixture linkage disequilibium"). This modification has several advantages, allowing (1) detection of admixture events farther back into the past, (2) inference of the population of origin of chromosomal regions, and (3) more accurate estimates of statistical uncertainty when linked loci are used. It is also of potential use for admixture mapping. In addition, we describe a new prior model for the allele frequencies within each population, which allows identification of subtle population subdivisions that were not detectable using the existing method. We present results applying the new methods to study admixture in African-Americans, recombination in Helicobacter pylori, and drift in populations of Drosophila melanogaster. The methods are implemented in a program, structure, version 2.0, which is available at http://pritch.bsd.uchicago.edu. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. D Falush M Stephens JK Pritchard Genetics, Vol. 164, No. 4. (August 2003), pp. 1567-1587. 2007-05-01T04:54:23-00:00 2003 Genetics 0016-6731 164 4 1567 1587 hapmap population_genetics structure http://www.citeulike.org/user/hms/article/1374645 <i>Proc Natl Acad Sci U S A, Vol. 99, No. 14. (9 July 2002), pp. 9121-9126.</i><br /><br />This article presents a Bayesian method for model-based clustering of gene expression dynamics. The method represents gene-expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters given the available data. The main contributions of this approach are the ability to take into account the dynamic nature of gene expression time series during clustering and a principled way to identify the number of distinct clusters. As the number of possible clustering models grows exponentially with the number of observed time series, we have devised a distance-based heuristic search procedure able to render the search process feasible. In this way, the method retains the important visualization capability of traditional distance-based clustering and acquires an independent, principled measure to decide when two series are different enough to belong to different clusters. The reliance of this method on an explicit statistical representation of gene expression dynamics makes it possible to use standard statistical techniques to assess the goodness of fit of the resulting model and validate the underlying assumptions. A set of gene-expression time series, collected to study the response of human fibroblasts to serum, is used to identify the properties of the method. Cluster analysis of gene expression dynamics. MF Ramoni P Sebastiani IS Kohane doi:10.1073/pnas.132656399 Proc Natl Acad Sci U S A, Vol. 99, No. 14. (9 July 2002), pp. 9121-9126. 2007-06-09T18:13:00-00:00 2002 Proc Natl Acad Sci U S A 0027-8424 99 14 9121 9126 time_series http://www.citeulike.org/user/hms/article/143442 <i>Nature Genetics, Vol. 37, No. 4. (20 March 2005), pp. 435-440.</i> Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia Paola Sebastiani Marco Ramoni Vikki Nolan Clinton Baldwin Martin Steinberg doi:10.1038/ng1533 Nature Genetics, Vol. 37, No. 4. (20 March 2005), pp. 435-440. 2005-03-31T03:04:29-00:00 2005 Nature Genetics 1061-4036 37 4 435 440 Nature Publishing Group data hapmap http://www.citeulike.org/user/hms/article/1369386 <i>Nature, Vol. 447, No. 7145. (7 June 2007), pp. 661-678.</i> Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls doi:10.1038/nature05911 Nature, Vol. 447, No. 7145. (7 June 2007), pp. 661-678. 2007-06-07T05:46:18-00:00 2007 Nature 447 7145 661 678 data wtccc http://www.citeulike.org/user/hms/article/903926 <i>Nature, Vol. 426, No. 6968. (18 December 2003), pp. 789-796.</i><br /><br />The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention. The International HapMap Project. doi:10.1038/nature02168 Nature, Vol. 426, No. 6968. (18 December 2003), pp. 789-796. 2006-10-18T18:45:51-00:00 2003 Nature 1476-4687 426 6968 789 796 hapmap http://www.citeulike.org/user/hms/article/366486 <i>Nature, Vol. 437, No. 7063., pp. 1299-1320.</i> A haplotype map of the human genome The doi:10.1038/nature04226 Nature, Vol. 437, No. 7063., pp. 1299-1320. 2005-10-27T05:55:39-00:00 Nature 0028-0836 437 7063 1299 1320 Nature Publishing Group hapmap http://www.citeulike.org/user/hms/article/420458 <i>Science, Vol. 310, No. 5746. (14 October 2005), pp. 321-324.</i><br /><br />Genetic maps, which document the way in which recombination rates vary over a genome, are an essential tool for many genetic analyses. We present a high-resolution genetic map of the human genome, based on statistical analyses of genetic variation data, and identify more than 25,000 recombination hotspots, together with motifs and sequence contexts that play a role in hotspot activity. Differences between the behavior of recombination rates over large (megabase) and small (kilobase) scales lead us to suggest a two-stage model for recombination in which hotspots are stochastic features, within a framework in which large-scale rates are constrained. A fine-scale map of recombination rates and hotspots across the human genome. S Myers L Bottolo C Freeman G McVean P Donnelly doi:10.1126/science.1117196 Science, Vol. 310, No. 5746. (14 October 2005), pp. 321-324. 2005-12-02T22:07:33-00:00 2005 Science 1095-9203 310 5746 321 324 hapmap population_genetics