32 results on '"Besenbacher, Søren"'
Search Results
2. Expression patterns and prognostic potential of circular RNAs in mantle cell lymphoma: a study of younger patients from the MCL2 and MCL3 clinical trials
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Dahl, Mette, Husby, Simon, Eskelund, Christian W., Besenbacher, Søren, Fjelstrup, Søren, Côme, Christophe, Ek, Sara, Kolstad, Arne, Räty, Riikka, Jerkeman, Mats, Geisler, Christian H., Kjems, Jørgen, Kristensen, Lasse S., and Grønbæk, Kirsten
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- 2022
- Full Text
- View/download PDF
3. A method to build extended sequence context models of point mutations and indels
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Bethune, Jörn, Kleppe, April, and Besenbacher, Søren
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- 2022
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4. Correction: Expression patterns and prognostic potential of circular RNAs in mantle cell lymphoma: a study of younger patients from the MCL2 and MCL3 clinical trials
- Author
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Dahl, Mette, Husby, Simon, Eskelund, Christian W., Besenbacher, Søren, Fjelstrup, Søren, Côme, Christophe, Ek, Sara, Kolstad, Arne, Räty, Riikka, Jerkeman, Mats, Geisler, Christian H., Kjems, Jørgen, Kristensen, Lasse S., and Grønbæk, Kirsten
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- 2022
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- View/download PDF
5. Prognostic miRNA classifier in early-stage mycosis fungoides: development and validation in a Danish nationwide study
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Lindahl, Lise M., Besenbacher, Søren, Rittig, Anne H., Celis, Pamela, Willerslev-Olsen, Andreas, Gjerdrum, Lise M.R., Krejsgaard, Thorbjørn, Johansen, Claus, Litman, Thomas, Woetmann, Anders, Odum, Niels, and Iversen, Lars
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- 2018
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6. Direct estimation of mutations in great apes reconciles phylogenetic dating
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Besenbacher, Søren, Hvilsom, Christina, Marques-Bonet, Tomas, Mailund, Thomas, and Schierup, Mikkel Heide
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- 2019
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7. Prediction of Primary Tumors in Cancers of Unknown Primary
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Søndergaard Dan, Nielsen Svend, Pedersen Christian N.S., and Besenbacher Søren
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cancer of unknown origin ,classification ,transcriptomics ,precision medicine ,Biotechnology ,TP248.13-248.65 - Abstract
A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3–5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP). The method models the data as a mixture of normal and tumor cells and thus allows correct classification even in impure samples, where the tumor biopsy is contaminated by a large fraction of normal cells. We find that our method provides a significant increase in classification accuracy (95.8% over 90.8%) on simulated low-purity metastatic samples and shows potential on a small dataset of real metastasis samples with known origin.
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- 2017
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8. A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data
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Bertl, Johanna, Guo, Qianyun, Juul, Malene, Besenbacher, Søren, Nielsen, Morten Muhlig, Hornshøj, Henrik, Pedersen, Jakob Skou, and Hobolth, Asger
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- 2018
- Full Text
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9. Author Correction: Direct estimation of mutations in great apes reconciles phylogenetic dating
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Besenbacher, Søren, Hvilsom, Christina, Marques-Bonet, Tomas, Mailund, Thomas, and Schierup, Mikkel Heide
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- 2019
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10. The Proteome of Seed Development in the Model Legume Lotus japonicus
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Dam, Svend, Laursen, Brian S., Ørnfelt, Jane H., Jochimsen, Bjarne, Staerfeldt, Hans Henrik, Friis, Carsten, Nielsen, Kasper, Goffard, Nicolas, Besenbacher, Soren, Krusell, Lene, Sato, Shusei, Tabata, Satoshi, Thogersen, Ida B., Enghild, Jan J., and Stougaard, Jens
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- 2009
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11. Unsupervised detection of fragment length signatures of circulating tumor DNA using non-negative matrix factorization.
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Renaud, Gabriel, Nørgaard, Maibritt, Lindberg, Johan, Grönberg, Henrik, Laere, Bram De, Jensen, Jørgen Bjerggaard, Borre, Michael, Andersen, Claus Lindbjerg, Sørensen, Karina Dalsgaard, Maretty, Lasse, and Besenbacher, Søren
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- 2022
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12. Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios
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Besenbacher, Søren, Liu, Siyang, Izarzugaza, José M. G., Grove, Jakob, Belling, Kirstine, Bork-Jensen, Jette, Huang, Shujia, Als, Thomas D., Li, Shengting, Yadav, Rachita, Rubio-García, Arcadio, Lescai, Francesco, Demontis, Ditte, Rao, Junhua, Ye, Weijian, Mailund, Thomas, Friborg, Rune M., Pedersen, Christian N. S., Xu, Ruiqi, Sun, Jihua, Liu, Hao, Wang, Ou, Cheng, Xiaofang, Flores, David, Rydza, Emil, Rapacki, Kristoffer, Damm Sørensen, John, Chmura, Piotr, Westergaard, David, Dworzynski, Piotr, Sørensen, Thorkild I. A., Lund, Ole, Hansen, Torben, Xu, Xun, Li, Ning, Bolund, Lars, Pedersen, Oluf, Eiberg, Hans, Krogh, Anders, Børglum, Anders D., Brunak, Søren, Kristiansen, Karsten, Schierup, Mikkel H., Wang, Jun, Gupta, Ramneek, Villesen, Palle, and Rasmussen, Simon
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- 2015
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13. A fast algorithm for genome-wide haplotype pattern mining
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Pedersen Christian NS, Besenbacher Søren, and Mailund Thomas
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The Haplotype Pattern Mining (HPM) method is a machine learning approach to do exactly this. Results We present a new, faster algorithm for the HPM method. The new approach use patterns of haplotype diversity in the genome: locally in the genome, the number of observed haplotypes is much smaller than the total number of possible haplotypes. We show that the new approach speeds up the HPM method with a factor of 2 on a genome-wide dataset with 5009 individuals typed in 491208 markers using default parameters and more if the pattern length is increased. Conclusion The new algorithm speeds up the HPM method and we show that it is feasible to apply HPM to whole genome association mapping with thousands of individuals and hundreds of thousands of markers.
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- 2009
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14. The Mutationathon highlights the importance of reaching standardization in estimates of pedigree-based germline mutation rates.
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Bergeron, Lucie A., Besenbacher, Søren, Turner, Tychele, Versoza, Cyril J., Wang, Richard J., Price, Alivia Lee, Armstrong, Ellie, Riera, Meritxell, Carlson, Jedidiah, Hwei-yen Chen, Hahn, Matthew W., Harris, Kelley, Kleppe, April Snøfrid, López-Nandam, Elora H., Moorjani, Priya, Pfeifer, Susanne P., Tiley, George P., Yoder, Anne D., Guojie Zhang, and Schierup, Mikkel H.
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GERM cells , *MACAQUES , *GENETIC mutation , *RHESUS monkeys , *STANDARDIZATION - Abstract
In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various nonhuman species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and appropriately accounting for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon,' a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a twofold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria, and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees. [ABSTRACT FROM AUTHOR]
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- 2022
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15. Studying mutation rate evolution in primates—a need for systematic comparison of computational pipelines.
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Bergeron, Lucie A, Besenbacher, Søren, Schierup, Mikkel H, and Zhang, Guojie
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GENETIC mutation , *RHESUS monkeys , *PRIMATES , *GERM cells , *BEST practices - Abstract
The lack of consensus methods to estimate germline mutation rates from pedigrees has led to substantial differences in computational pipelines in the published literature. Here, we answer Susanne Pfeifer's opinion piece discussing the pipeline choices of our recent article estimating the germline mutation rate of rhesus macaques (Macaca mulatta). We acknowledge the differences between the method that we applied and the one preferred by Pfeifer. Yet, we advocate for full transparency and justification of choices as long as rigorous comparison of pipelines remains absent because it is the only way to conclude on best practices for the field. [ABSTRACT FROM AUTHOR]
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- 2021
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16. RBT—a tool for building refined Buneman trees
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Besenbacher, Søren, Mailund, Thomas, Westh-Nielsen, Lasse, and Pedersen, Christian N. S.
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- 2005
17. The germline mutational process in rhesus macaque and its implications for phylogenetic dating.
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Bergeron, Lucie A, Besenbacher, Søren, Bakker, Jaco, Zheng, Jiao, Li, Panyi, Pacheco, George, Sinding, Mikkel-Holger S, Kamilari, Maria, Gilbert, M Thomas P, Schierup, Mikkel H, and Zhang, Guojie
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RHESUS monkeys , *MOLECULAR clock , *MACAQUES , *GERM cells , *APES , *PRIMATES - Abstract
Background Understanding the rate and pattern of germline mutations is of fundamental importance for understanding evolutionary processes. Results Here we analyzed 19 parent-offspring trios of rhesus macaques (Macaca mulatta) at high sequencing coverage of ∼76× per individual and estimated a mean rate of 0.77 × 10−8 de novo mutations per site per generation (95% CI: 0.69 × 10−8 to 0.85 × 10−8). By phasing 50% of the mutations to parental origins, we found that the mutation rate is positively correlated with the paternal age. The paternal lineage contributed a mean of 81% of the de novo mutations, with a trend of an increasing male contribution for older fathers. Approximately 3.5% of de novo mutations were shared between siblings, with no parental bias, suggesting that they arose from early development (postzygotic) stages. Finally, the divergence times between closely related primates calculated on the basis of the yearly mutation rate of rhesus macaque generally reconcile with divergence estimated with molecular clock methods, except for the Cercopithecoidea/Hominoidea molecular divergence dated at 58 Mya using our new estimate of the yearly mutation rate. Conclusions When compared to the traditional molecular clock methods, new estimated rates from pedigree samples can provide insights into the evolution of well-studied groups such as primates. [ABSTRACT FROM AUTHOR]
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- 2021
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18. Whole genome association mapping by incompatibilities and local perfect phylogenies
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Besenbacher Søren, Mailund Thomas, and Schierup Mikkel H
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed. Results We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set – the ΔF508 mutation for cystic fibrosis – where the susceptibility variant is already known – and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene. Conclusion Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours.
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- 2006
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19. Pathway Analysis of Skin from Psoriasis Patients after Adalimumab Treatment Reveals New Early Events in the Anti-Inflammatory Mechanism of Anti-TNF-α.
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Langkilde, Ane, Olsen, Lene C., Sætrom, Pål, Drabløs, Finn, Besenbacher, Søren, Raaby, Line, Johansen, Claus, and Iversen, Lars
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ADALIMUMAB ,PSORIASIS treatment ,PSORIASIS ,ANTI-inflammatory agents ,DENDRITIC cells ,PATIENTS ,THERAPEUTICS - Abstract
Psoriasis is a chronic cutaneous inflammatory disease. The immunopathogenesis is a complex interplay between T cells, dendritic cells and the epidermis in which T cells and dendritic cells maintain skin inflammation. Anti-tumour necrosis factor (anti-TNF)-α agents have been approved for therapeutic use across a range of inflammatory disorders including psoriasis, but the anti-inflammatory mechanisms of anti-TNF-α in lesional psoriatic skin are not fully understood. We investigated early events in skin from psoriasis patients after treatment with anti-TNF-α antibodies by use of bioinformatics tools. We used the Human Gene 1.0 ST Array to analyse gene expression in punch biopsies taken from psoriatic patients before and also 4 and 14 days after initiation of treatment with the anti-TNF-α agent adalimumab. The gene expression was analysed by gene set enrichment analysis using the Functional Annotation Tool from DAVID Bioinformatics Resources. The most enriched pathway was visualised by the Pathview Package on Kyoto Encyclopedia of Genes and Genomes (KEGG) graphs. The analysis revealed new very early events in psoriasis after adalimumab treatment. Some of these events have been described after longer periods of anti-TNF-α treatment when clinical and histological changes appear, suggesting that effects of anti-TNF-α treatment on gene expression appear very early before clinical and histological changes. Combining microarray data on biopsies from psoriasis patients with pathway analysis allowed us to integrate in vitro findings into the identification of mechanisms that may be important in vivo. Furthermore, these results may reflect primary effect of anti-TNF-α treatment in contrast to studies of gene expression changes following clinical and histological changes, which may reflect secondary changes correlated to the healing of the skin. [ABSTRACT FROM AUTHOR]
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- 2016
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20. Multi-nucleotide de novo Mutations in Humans.
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Besenbacher, Søren, Sulem, Patrick, Helgason, Agnar, Helgason, Hannes, Kristjansson, Helgi, Jonasdottir, Aslaug, Jonasdottir, Adalbjorg, Magnusson, Olafur Th., Thorsteinsdottir, Unnur, Masson, Gisli, Kong, Augustine, Gudbjartsson, Daniel F., and Stefansson, Kari
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GENETIC mutation , *DNA , *SINGLE nucleotide polymorphisms , *GENETIC recombination , *HUMAN beings - Abstract
Mutation of the DNA molecule is one of the most fundamental processes in biology. In this study, we use 283 parent-offspring trios to estimate the rate of mutation for both single nucleotide variants (SNVs) and short length variants (indels) in humans and examine the mutation process. We found 17812 SNVs, corresponding to a mutation rate of 1.29 × 10−8 per position per generation (PPPG) and 1282 indels corresponding to a rate of 9.29 × 10−10 PPPG. We estimate that around 3% of human de novo SNVs are part of a multi-nucleotide mutation (MNM), with 558 (3.1%) of mutations positioned less than 20kb from another mutation in the same individual (median distance of 525bp). The rate of de novo mutations is greater in late replicating regions (p = 8.29 × 10−19) and nearer recombination events (p = 0.0038) than elsewhere in the genome. [ABSTRACT FROM AUTHOR]
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- 2016
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21. Identifying disease associated genes by network propagation.
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Yu Qian, Besenbacher, Søren, Mailund, Thomas, and Schierup, Mikkel Heide
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Background: Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases. Results: In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn’s disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations. Conclusions: Our network propagation method applied to a genome-wide association study increases association findings over other approaches. [ABSTRACT FROM AUTHOR]
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- 2014
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22. Local Phylogeny Mapping of Quantitative Traits: Higher Accuracy and Better Ranking Than Single-Marker Association in Genomewide Scans.
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Besenbacher, Søren, Mailund, Thomas, and Schierup, Mikkel H.
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PLANT phylogeny , *PHYLOGENY , *GENE mapping , *BIOLOGICAL variation , *PLANT genetics , *GENETIC markers , *BIOMARKERS , *SIMULATION methods & models - Abstract
We present a new method, termed QBlossoc, for linkage disequilibrium (LD) mapping of genetic variants underlying a quantitative trait. The method uses principles similar to a previously published method, Blossoc, for LD mapping of case/control studies. The method builds local genealogies along the genome and looks for a significant clustering of quantitative trait values in these trees. We analyze its efficiency in terms of localization and ranking of true positives among a large number of negatives and compare the results with single-marker approaches. Simulation results of markers at densities comparable to contemporary genotype chips show that QBlossoc is more accurate in localization of true positives as expected since it uses the additional information of LD between markers simultaneously. More importantly, however, for genomewide surveys, QBlossoc places regions with true positives higher on a ranked list than single-marker approaches, again suggesting that a true signal displays itself more strongly in a set of adjacent markers than a spurious (false) signal. The method is both memory and central processing unit (CPU) efficient. It has been tested on a real data set of height data for 5000 individuals measured at ~317,000 markers and completed analysis within 5 CPU days. [ABSTRACT FROM AUTHOR]
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- 2009
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23. A fast algorithm for genome-wide haplotype pattern mining.
- Author
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Besenbacher, Søren, Pedersen, Christian N. S., and Mailund, Thomas
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GENETIC research , *NUCLEOTIDES , *GENOMES , *ALGORITHMS , *MEDICAL genetics - Abstract
Background: Identifying the genetic components of common diseases has long been an important area of research. Recently, genotyping technology has reached the level where it is cost effective to genotype single nucleotide polymorphism (SNP) markers covering the entire genome, in thousands of individuals, and analyse such data for markers associated with a diseases. The statistical power to detect association, however, is limited when markers are analysed one at a time. This can be alleviated by considering multiple markers simultaneously. The Haplotype Pattern Mining (HPM) method is a machine learning approach to do exactly this. Results: We present a new, faster algorithm for the HPM method. The new approach use patterns of haplotype diversity in the genome: locally in the genome, the number of observed haplotypes is much smaller than the total number of possible haplotypes. We show that the new approach speeds up the HPM method with a factor of 2 on a genome-wide dataset with 5009 individuals typed in 491208 markers using default parameters and more if the pattern length is increased. Conclusion: The new algorithm speeds up the HPM method and we show that it is feasible to apply HPM to whole genome association mapping with thousands of individuals and hundreds of thousands of markers. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
24. Common variants at 19p13 are associated with susceptibility to ovarian cancer
- Author
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Bolton, Kelly L, Tyrer, Jonathan, Song, Honglin, Ramus, Susan J, Notaridou, Maria, Jones, Chris, Sher, Tanya, Gentry-Maharaj, Aleksandra, Wozniak, Eva, Tsai, Ya-Yu, Weidhaas, Joanne, Paik, Daniel, Van Den Berg, David J, Stram, Daniel O, Pearce, Celeste Leigh, Wu, Anna H, Brewster, Wendy, Anton-Culver, Hoda, Ziogas, Argyrios, Narod, Steven A, Levine, Douglas A, Kaye, Stanley B, Brown, Robert, Paul, Jim, Flanagan, James, Sieh, Weiva, McGuire, Valerie, Whittemore, Alice S, Campbell, Ian, Gore, Martin E, Lissowska, Jolanta, Yang, Hanna P, Medrek, Krzysztof, Gronwald, Jacek, Lubinski, Jan, Jakubowska, Anna, Le, Nhu D, Cook, Linda S, Kelemen, Linda E, Brook-Wilson, Angela, Massuger, Leon F A G, Kiemeney, Lambertus A, Aben, Katja K H, van Altena, Anne M, Houlston, Richard, Tomlinson, Ian, Palmieri, Rachel T, Moorman, Patricia G, Schildkraut, Joellen, Iversen, Edwin S, Phelan, Catherine, Vierkant, Robert A, Cunningham, Julie M, Goode, Ellen L, Fridley, Brooke L, Kruger-Kjaer, Susan, Blaeker, Jan, Hogdall, Estrid, Hogdall, Claus, Gross, Jenny, Karlan, Beth Y, Ness, Roberta B, Edwards, Robert P, Odunsi, Kunle, Moyisch, Kirsten B, Baker, Julie A, Modugno, Francesmary, Heikkinenen, Tuomas, Butzow, Ralf, Nevanlinna, Heli, Leminen, Arto, Bogdanova, Natalia, Antonenkova, Natalia, Doerk, Thilo, Hillemanns, Peter, Dürst, Matthias, Runnebaum, Ingo, Thompson, Pamela J, Carney, Michael E, Goodman, Marc T, Lurie, Galina, Wang-Gohrke, Shan, Hein, Rebecca, Chang-Claude, Jenny, Rossing, Mary Anne, Cushing-Haugen, Kara L, Doherty, Jennifer, Chen, Chu, Rafnar, Thorunn, Besenbacher, Soren, Sulem, Patrick, Stefansson, Kari, Birrer, Michael James, Terry, Kathryn Lynne, Hernandez, Dena, Cramer, Daniel William, Vergote, Ignace, Amant, Frederic, Lambrechts, Diether, Despierre, Evelyn, Fasching, Peter A, Beckmann, Matthias W, Thiel, Falk C, Ekici, Arif B, Chen, Xiaoqing, Johnatty, Sharon E, Webb, Penelope M, Beesley, Jonathan, Chanock, Stephen, Garcia-Closas, Montserrat, Sellers, Tom, Easton, Douglas F, Berchuck, Andrew, Chenevix-Trench, Georgia, Pharoah, Paul D P, and Gayther, Simon A
- Abstract
Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world accounting for 4 percent of deaths from cancer in women1. We performed a three-phase genome-wide association study of EOC survival in 8,951 EOC cases with available survival time data, and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.11, rs8170 and rs2363956, showed evidence of association with survival (overall P=5×10−4 and 6×10−4), but did not replicate in phase 3. However, the same two SNPs demonstrated genome-wide significance for risk of serous EOC (P=3×10−9 and 4×10−11 respectively). Expression analysis of candidate genes at this locus in ovarian tumors supported a role for the BRCA1 interacting gene C19orf62, also known as MERIT40, which contains rs8170, in EOC development.
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- 2010
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25. Hierarchical Classification of Cancers of Unknown Primary Using Multi-Omics Data.
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Bavafaye Haghighi, Elham, Knudsen, Michael, Elmedal Laursen, Britt, and Besenbacher, Søren
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CANCER of unknown primary origin ,TUMOR classification ,HIERARCHICAL clustering (Cluster analysis) ,MISSING data (Statistics) ,SOMATIC mutation ,METASTASIS - Abstract
A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to locate the primary cancer. As standard treatments are based on the cancer type, such cases are hard to treat and have very poor prognosis. Using molecular data from the metastatic cancer to predict the primary site can make treatment choice easier and enable targeted therapy. In this article, we first examine the ability to predict cancer type using different types of omics data. Methylation data lead to slightly better prediction than gene expression and both these are superior to classification using somatic mutations. After using 3 data types independently, we notice some differences between the classes that tend to be misclassified, suggesting that integrating the data might improve accuracy. In light of the different levels of information provided by different omics types and to be able to handle missing data, we perform multi-omics classification by hierarchically combining the classifiers. The proposed hierarchical method first classifies based on the most informative type of omics data and then uses the other types of omics data to classify samples that did not get a high confidence classification in the first step. The resulting hierarchical classifier has higher accuracy than any of the single omics classifiers and thus proves that the combination of different data types is beneficial. Our results show that using multi-omics data can improve the classification of cancer types. We confirm this by testing our method on metastatic cancers from the MET500 dataset. [ABSTRACT FROM AUTHOR]
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- 2019
- Full Text
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26. Association Mapping and Disease: Evolutionary Perspectives.
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Besenbacher S, Mailund T, Vilhjálmsson BJ, and Schierup MH
- Subjects
- Alleles, Computational Biology methods, Confounding Factors, Epidemiologic, Evolution, Molecular, Gene Frequency, Humans, Models, Genetic, Models, Statistical, Chromosome Mapping, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study
- Abstract
In this chapter, we give a short introduction to the genetics of complex diseases emphasizing evolutionary models for disease genes and the effect of different models on the genetic architecture, and we give a survey of the state-of-the-art of genome-wide association studies (GWASs).
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- 2019
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27. Proteomic profiling identifies outcome-predictive markers in patients with peripheral T-cell lymphoma, not otherwise specified.
- Author
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Ludvigsen M, Bjerregård Pedersen M, Lystlund Lauridsen K, Svenstrup Poulsen T, Hamilton-Dutoit SJ, Besenbacher S, Bendix K, Møller MB, Nørgaard P, d'Amore F, and Honoré B
- Subjects
- Aldehyde Dehydrogenase, Mitochondrial metabolism, Biomarkers, Tumor metabolism, Chromatography, Liquid, Computational Biology, DNA-Binding Proteins metabolism, Female, Humans, Lymphoid Tissue metabolism, Lymphoma, T-Cell, Peripheral genetics, Male, Phosphopyruvate Hydratase metabolism, Prognosis, Tandem Mass Spectrometry, Tumor Suppressor Proteins metabolism, Lymphoma, T-Cell, Peripheral metabolism, Lymphoma, T-Cell, Peripheral mortality, Proteome, Proteomics methods
- Abstract
Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) constitutes a heterogeneous category of lymphomas, which do not fit into any of the specifically defined T-cell lymphoma entities. Both the pathogenesis and tumor biology in PTCL-NOS are poorly understood. Protein expression in pretherapeutic PTCL-NOS tumors was analyzed by proteomics. Differentially expressed proteins were compared in 3 distinct scenarios: (A) PTCL-NOS tumor tissue (n = 18) vs benign lymphoid tissue (n = 8), (B) clusters defined by principal component analysis (PCA), and (C) tumors from patients with chemosensitive vs refractory PTCL-NOS. Selected differentially expressed proteins identified by proteomics were correlated with clinico-pathological features and outcome in a larger cohort of patients with PTCL-NOS (n = 87) by immunohistochemistry (IHC). Most proteins with altered expression were identified comparing PTCL-NOS vs benign lymphoid tissue. PCA of the protein profile defined 3 distinct clusters. All benign samples clustered together, whereas PTCL-NOS tumors separated into 2 clusters with different patient overall survival rates ( P = .001). Differentially expressed proteins reflected large biological diversity among PTCL-NOS, particularly associated with alterations of "immunological" pathways. The 2 PTCL-NOS subclusters defined by PCA showed disturbance of "stress-related" and "protein metabolic" pathways. α-Enolase 1 (ENO1) was found differentially expressed in all 3 analyses, and high intratumoral ENO1 expression evaluated by IHC correlated with poor outcome (hazard ratio, 2.09; 95% confidence interval, 1.17-3.73; P = .013). High expression of triosephosphate isomerase (TPI1) also showed a tendency to correlate with poor survival ( P = .057). In conclusion, proteomic profiling of PTCL-NOS provided evidence of markedly altered protein expression and identified ENO1 as a novel potential prognostic marker., (© 2018 by The American Society of Hematology.)
- Published
- 2018
- Full Text
- View/download PDF
28. Assembly and analysis of 100 full MHC haplotypes from the Danish population.
- Author
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Jensen JM, Villesen P, Friborg RM, Mailund T, Besenbacher S, and Schierup MH
- Subjects
- Alleles, Chromosome Mapping, Denmark, Haplotypes genetics, Humans, Polymorphism, Single Nucleotide genetics, Genetic Variation genetics, Genetics, Population, Linkage Disequilibrium genetics, Major Histocompatibility Complex genetics
- Abstract
Genes in the major histocompatibility complex (MHC, also known as HLA) play a critical role in the immune response and variation within the extended 4-Mb region shows association with major risks of many diseases. Yet, deciphering the underlying causes of these associations is difficult because the MHC is the most polymorphic region of the genome with a complex linkage disequilibrium structure. Here, we reconstruct full MHC haplotypes from de novo assembled trios without relying on a reference genome and perform evolutionary analyses. We report 100 full MHC haplotypes and call a large set of structural variants in the regions for future use in imputation with GWAS data. We also present the first complete analysis of the recombination landscape in the entire region and show how balancing selection at classical genes have linked effects on the frequency of variants throughout the region., (© 2017 Jensen et al.; Published by Cold Spring Harbor Laboratory Press.)
- Published
- 2017
- Full Text
- View/download PDF
29. Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.
- Author
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Maretty L, Jensen JM, Petersen B, Sibbesen JA, Liu S, Villesen P, Skov L, Belling K, Theil Have C, Izarzugaza JMG, Grosjean M, Bork-Jensen J, Grove J, Als TD, Huang S, Chang Y, Xu R, Ye W, Rao J, Guo X, Sun J, Cao H, Ye C, van Beusekom J, Espeseth T, Flindt E, Friborg RM, Halager AE, Le Hellard S, Hultman CM, Lescai F, Li S, Lund O, Løngren P, Mailund T, Matey-Hernandez ML, Mors O, Pedersen CNS, Sicheritz-Pontén T, Sullivan P, Syed A, Westergaard D, Yadav R, Li N, Xu X, Hansen T, Krogh A, Bolund L, Sørensen TIA, Pedersen O, Gupta R, Rasmussen S, Besenbacher S, Børglum AD, Wang J, Eiberg H, Kristiansen K, Brunak S, and Schierup MH
- Subjects
- Adult, Alleles, Child, Chromosomes, Human, Y genetics, Denmark, Female, Haplotypes genetics, Humans, Major Histocompatibility Complex genetics, Male, Maternal Age, Mutation Rate, Paternal Age, Point Mutation genetics, Reference Standards, Genetic Variation genetics, Genetics, Population standards, Genome, Human genetics, Genomics standards, Sequence Analysis, DNA standards
- Abstract
Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.
- Published
- 2017
- Full Text
- View/download PDF
30. Identifying disease associated genes by network propagation.
- Author
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Qian Y, Besenbacher S, Mailund T, and Schierup MH
- Subjects
- Crohn Disease pathology, Genome-Wide Association Study, Humans, Interleukin-12 metabolism, ROC Curve, Signal Transduction genetics, Computational Biology, Crohn Disease genetics, Crohn Disease metabolism, Protein Interaction Maps
- Abstract
Background: Genome-wide association studies have identified many individual genes associated with complex traits. However, pathway and network information have not been fully exploited in searches for genetic determinants, and including this information may increase our understanding of the underlying biology of common diseases., Results: In this study, we propose a framework to address this problem in a principled way, with the underlying hypothesis that complex disease operates through multiple connected genes. Associations inferred from GWAS are translated into prior scores for vertices in a protein-protein interaction network, and these scores are propagated through the network. Permutation is used to select genes that are guilty-by-association and thus consistently obtain high scores after network propagation. We apply the approach to data of Crohn's disease and call candidate genes that have been reported by other independent GWAS, but not in the analysed data set. A prediction model based on these candidate genes show good predictive power as measured by Area Under the Receiver Operating Curve (AUC) in 10 fold cross-validations., Conclusions: Our network propagation method applied to a genome-wide association study increases association findings over other approaches.
- Published
- 2014
- Full Text
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31. Association mapping and disease: evolutionary perspectives.
- Author
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Besenbacher S, Mailund T, and Schierup MH
- Subjects
- Data Interpretation, Statistical, Gene Frequency, Humans, Disease genetics, Evolution, Molecular, Genome-Wide Association Study methods
- Abstract
In this chapter, we give a short introduction to the genetics of complex disease with special emphasis on evolutionary models for disease genes and the effect of different models on the genetic architecture, and finally give a survey of the state-of-the-art of genome-wide association studies.
- Published
- 2012
- Full Text
- View/download PDF
32. Whole genome association mapping by incompatibilities and local perfect phylogenies.
- Author
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Mailund T, Besenbacher S, and Schierup MH
- Subjects
- Cystic Fibrosis diagnosis, Humans, Phylogeny, Polymorphism, Single Nucleotide genetics, Chromosome Mapping methods, Cystic Fibrosis genetics, Cytochrome P-450 CYP2D6 genetics, DNA Mutational Analysis methods, Genetic Predisposition to Disease genetics, Linkage Disequilibrium genetics
- Abstract
Background: With current technology, vast amounts of data can be cheaply and efficiently produced in association studies, and to prevent data analysis to become the bottleneck of studies, fast and efficient analysis methods that scale to such data set sizes must be developed., Results: We present a fast method for accurate localisation of disease causing variants in high density case-control association mapping experiments with large numbers of cases and controls. The method searches for significant clustering of case chromosomes in the "perfect" phylogenetic tree defined by the largest region around each marker that is compatible with a single phylogenetic tree. This perfect phylogenetic tree is treated as a decision tree for determining disease status, and scored by its accuracy as a decision tree. The rationale for this is that the perfect phylogeny near a disease affecting mutation should provide more information about the affected/unaffected classification than random trees. If regions of compatibility contain few markers, due to e.g. large marker spacing, the algorithm can allow the inclusion of incompatibility markers in order to enlarge the regions prior to estimating their phylogeny. Haplotype data and phased genotype data can be analysed. The power and efficiency of the method is investigated on 1) simulated genotype data under different models of disease determination 2) artificial data sets created from the HapMap ressource, and 3) data sets used for testing of other methods in order to compare with these. Our method has the same accuracy as single marker association (SMA) in the simplest case of a single disease causing mutation and a constant recombination rate. However, when it comes to more complex scenarios of mutation heterogeneity and more complex haplotype structure such as found in the HapMap data our method outperforms SMA as well as other fast, data mining approaches such as HapMiner and Haplotype Pattern Mining (HPM) despite being significantly faster. For unphased genotype data, an initial step of estimating the phase only slightly decreases the power of the method. The method was also found to accurately localise the known susceptibility variants in an empirical data set--the DeltaF508 mutation for cystic fibrosis--where the susceptibility variant is already known--and to find significant signals for association between the CYP2D6 gene and poor drug metabolism, although for this dataset the highest association score is about 60 kb from the CYP2D6 gene., Conclusion: Our method has been implemented in the Blossoc (BLOck aSSOCiation) software. Using Blossoc, genome wide chip-based surveys of 3 million SNPs in 1000 cases and 1000 controls can be analysed in less than two CPU hours.
- Published
- 2006
- Full Text
- View/download PDF
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