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Sökning: WFRF:(Sennblad Bengt)

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  • Arvestad, Lars, et al. (författare)
  • Bayesian gene/species tree reconciliation and orthology analysis using MCMC
  • 2003
  • Ingår i: Bioinformatics. - Oxford Journals. - 1367-4803. ; 19, s. i7-i15
  • Tidskriftsartikel (refereegranskat)abstract
    • Motivation: Comparative genomics in general and orthology analysis in particular are becoming increasingly important parts of gene function prediction. Previously, orthology analysis and reconciliation has been performed only with respect to the parsimony model. This discards many plausible solutions and sometimes precludes finding the correct one. In many other areas in bioinformatics probabilistic models have proven to be both more realistic and powerful than parsimony models. For instance, they allow for assessing solution reliability and consideration of alternative solutions in a uniform way. There is also an added benefit in making model assumptions explicit and therefore making model comparisons possible. For orthology analysis, uncertainty has recently been addressed using parsimonious reconciliation combined with bootstrap techniques. However, until now no probabilistic methods have been available. Results: We introduce a probabilistic gene evolution model based on a birth-death process in which a gene tree evolves ‘inside’ a species tree. Based on this model, we develop a tool with the capacity to perform practical orthology analysis, based on Fitch’s original definition, and more generally for reconciling pairs of gene and species trees. Our gene evolution model is biologically sound (Nei et al., 1997) and intuitively attractive. We develop a Bayesian analysis based on MCMC which facilitates approximation of an a posteriori distribution for reconciliations. That is, we can find the most probable reconciliations and estimate the probability of any reconciliation, given the observed gene tree. This also gives a way to estimate the probability that a pair of genes are orthologs. The main algorithmic contribution presented here consists of an algorithm for computing the likelihood of a given reconciliation. To the best of our knowledge, this is the first successful introduction of this type of probabilistic methods, which flourish in phylogeny analysis, into reconciliation and orthology analysis. The MCMC algorithm has been implemented and, although not yet being in its final form, tests show that it performs very well on synthetic as well as biological data. Using standard correspondences, our results carry over to allele trees as well as biogeography.
  • Arvestad, Lars, et al. (författare)
  • Gene tree reconstruction and orthology analysis based on an integrated model for duplications and sequence evolution.
  • 2004
  • Ingår i: Proceedings of the Annual International Conference on Computational Molecular Biology, RECOM. ; s. 326-335
  • Konferensbidrag (refereegranskat)abstract
    • Gene tree and species tree reconstruction, orthology analysis and reconciliation, are problems important in multigenome-based comparative genomics and biology in general. In the present paper, we advance the frontier of these areas in several respects and provide important computational tools. First, exact algorithms are given for several probabilistic reconciliation problems with respect to the probabilistic gene evolutionmodel, previously developed by the authors. Until now, those problems were solved by MCMC estimation algorithms. Second, we extend the gene evolution model to the genesequence evolution model, by including sequence evolution. Third, we develop MCMC algorithms for the gene sequence evolution model that, given gene sequence data allows: (1) orthology analysis, reconciliation analysis, and gene tree reconstruction, w.r.t. a species tree, that balances a likely/unlikely reconciliation and a likely/unlikely genetree and (2) species tree reconstruction that balance a likely /unlikely reconciliation and a likely/unlikely gene trees. These MCMC algorithms take advantage of the exact algorithms for the gene evolution model. We have successfully tested our dynamical programming algorithms on real data for a biogeography problem. The MCMC algorithms perform very well both on synthetic and biological data.
  • Arvestad, Lars, et al. (författare)
  • The Gene Evolution Model and Computing Its Associated Probabilities
  • 2009
  • Ingår i: Journal of the ACM. - 0004-5411. ; 56:2
  • Tidskriftsartikel (refereegranskat)abstract
    • Phylogeny is both a fundamental tool in biology and a rich source of fascinating modeling and algorithmic problems. Today's wealth of sequenced genomes makes it increasingly important to understand evolutionary events such as duplications, losses, transpositions, inversions, lateral transfers, and domain shuffling. We focus on the gene duplication event, that constitutes a major force in the creation of genes with new function [Ohno 1970; Lynch and Force 2000] and, thereby also, of biodiversity. We introduce the probabilistic gene evolution model, which describes how a gene tree evolves within a given species tree with respect to speciation, gene duplication, and gene loss. The actual relation between gene tree and species tree is captured by a reconciliation, a concept which we generalize for more expressiveness. The model is a canonical generalization of the classical linear birth-death process, obtained by replacing the interval where the process takes place by a tree. For the gene evolution model, we derive efficient algorithms for some associated probability distributions: the probability of a reconciled tree, the probability of a gene tree, the maximum probability reconciliation, the posterior probability of a reconciliation, and sampling reconciliations with respect to the posterior probability. These algorithms provides the basis for several applications, including species tree construction, reconciliation analysis, orthology analysis, biogeography, and host-parasite co-evolution.
  • Bremer, Kåre, et al. (författare)
  • A phylogenetic analysis of 100+ genera and 50+ families of euasterids based on morphological and molecular data with notes on possible higher level morphological synapomorphies
  • 2001
  • Ingår i: Plant Systematics and Evolution. - 0378-2697 .- 1615-6110. ; 229:3-4, s. 137-169
  • Tidskriftsartikel (refereegranskat)abstract
    • A data matrix of 143 morphological and chemical characters for 142 genera of euasterids according to the APG system was compiled and complemented with rbcL and ndhF sequences for most of the genera. The data were subjected to parsimony analysis and support was assessed by bootstrapping. Strict consensus trees from analyses of morphology alone and morphology + rbcL + ndhF are presented. The morphological data recover several groups supported by molecular data but at the level of orders and above relationships are only superficially in agreement with molecular studies. The analyses provide support for monophyly of Gentianales, Aquifoliales, Apiales, Asterales, and Dipsacales. All data indicate that Adoxaceae are closely related to Dipsacales and hence they should be included in that order. The trees were used to assess some possible morphological synapomorphies for euasterids I and II and for the orders of the APG system. Euasterids I are generally characterised by opposite leaves, entire leaf margins, hypogynous flowers, “early sympetaly” with a ring-shaped corolla primordium, fusion of stamen filaments with the corolla tube, and capsular fruits. Euasterids II often have alternate leaves, serrate-dentate leaf margins, epigynous flowers, “late sympetaly” with distinct petal primordia, free stamen filaments, and indehiscent fruits. It is unclear which of these characters represent synapomorphies and symplesiomorphies for the two groups, respectively, and there are numerous expections to be interpreted as reversals and parallelisms.
  • Folkersen, Lasse, et al. (författare)
  • Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease
  • 2017
  • Ingår i: PLoS Genetics. - PUBLIC LIBRARY SCIENCE. - 1553-7390 .- 1553-7404. ; 13:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Recent advances in highly multiplexed immunoassays have allowed systematic large-scale measurement of hundreds of plasma proteins in large cohort studies. In combination with genotyping, such studies offer the prospect to 1) identify mechanisms involved with regulation of protein expression in plasma, and 2) determine whether the plasma proteins are likely to be causally implicated in disease. We report here the results of genome-wide association (GWA) studies of 83 proteins considered relevant to cardiovascular disease (CVD), measured in 3,394 individuals with multiple CVD risk factors. We identified 79 genome-wide significant (p<5e-8) association signals, 55 of which replicated at P<0.0007 in separate validation studies (n = 2,639 individuals). Using automated text mining, manual curation, and network-based methods incorporating information on expression quantitative trait loci (eQTL), we propose plausible causal mechanisms for 25 trans-acting loci, including a potential post-translational regulation of stem cell factor by matrix metalloproteinase 9 and receptor-ligand pairs such as RANK-RANK ligand. Using public GWA study data, we further evaluate all 79 loci for their causal effect on coronary artery disease, and highlight several potentially causal associations. Overall, a majority of the plasma proteins studied showed evidence of regulation at the genetic level. Our results enable future studies of the causal architecture of human disease, which in turn should aid discovery of new drug targets.
  • Franceschini, Nora, et al. (författare)
  • GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes
  • 2018
  • Ingår i: Nature Communications. - 20411723 (ISSN). ; 9:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Carotid artery intima media thickness (cIMT) and carotid plaque are measures of subclinical atherosclerosis associated with ischemic stroke and coronary heart disease (CHD). Here, we undertake meta-analyses of genome-wide association studies (GWAS) in 71,128 individuals for cIMT, and 48,434 individuals for carotid plaque traits. We identify eight novel susceptibility loci for cIMT, one independent association at the previously-identified PINX1 locus, and one novel locus for carotid plaque. Colocalization analysis with nearby vascular expression quantitative loci (cis-eQTLs) derived from arterial wall and metabolic tissues obtained from patients with CHD identifies candidate genes at two potentially additional loci, ADAMTS9 and LOXL4. LD score regression reveals significant genetic correlations between cIMT and plaque traits, and both cIMT and plaque with CHD, any stroke subtype and ischemic stroke. Our study provides insights into genes and tissue-specific regulatory mechanisms linking atherosclerosis both to its functional genomic origins and its clinical consequences in humans.
  • Frånberg, Mattias, 1985-, et al. (författare)
  • Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests
  • 2015
  • Ingår i: PLoS Genetics. - 1553-7390. ; 11:9
  • Tidskriftsartikel (refereegranskat)abstract
    • Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PRO-CARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.
  • Frånberg, Mattias, et al. (författare)
  • Fast and general tests of genetic interaction for genome-wide association studies
  • 2017
  • Ingår i: PloS Computational Biology. - 1553-734X. ; 13:6
  • Tidskriftsartikel (refereegranskat)abstract
    • A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 . 10(-09)) as well as replicate the interaction (p = 6.97 . 10(-07)). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.
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