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Institution

University of Turku

EducationTurku, Finland
About: University of Turku is a education organization based out in Turku, Finland. It is known for research contribution in the topics: Population & Galaxy. The organization has 16296 authors who have published 45124 publications receiving 1505428 citations. The organization is also known as: Turun yliopisto & Åbo universitet.


Papers
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Journal ArticleDOI
Iris M. Heid1, Anne U. Jackson2, Joshua C. Randall3, Tthomas W. Winkler1  +352 moreInstitutions (90)
TL;DR: A meta-analysis of genome-wide association studies for WHR adjusted for body mass index provides evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.
Abstract: Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻¹³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

869 citations

Journal ArticleDOI
19 Jun 2013-JAMA
TL;DR: The majority of children at risk of type 1 diabetes who had multiple islet autoantibody seroconversion progressed to diabetes over the next 15 years, and future prevention studies should focus on this high-risk population.
Abstract: Results Progression to type 1 diabetes at 10-year follow-up after islet autoantibody seroconversion in 585 children with multiple islet autoantibodies was 69.7% (95% CI, 65.1%-74.3%), and in 474 children with a single islet autoantibody was 14.5% (95% CI, 10.3%-18.7%). Risk of diabetes in children who had no islet autoantibodies was 0.4% (95% CI, 0.2%-0.6%) by the age of 15 years. Progression to type 1 diabetes in the children with multiple islet autoantibodies was faster for children who had islet autoantibody seroconversion younger than age 3 years (hazard ratio [HR], 1.65 [95% CI, 1.30-2.09; P .001]; 10-year risk, 74.9% [95% CI, 69.7%-80.1%]) vs children 3 years or older (60.9% [95% CI, 51.5%-70.3%]); for children with the human leukocyte antigen (HLA) genotype DR3/DR4-DQ8 (HR, 1.35 [95% CI, 1.09-1.68; P=.007]; 10-year risk, 76.6% [95% CI, 69.2%-84%]) vs other HLA genotypes (66.2% [95% CI, 60.2%-72.2%]); and for girls (HR, 1.28 [95% CI, 1.04-1.58; P=.02];10year risk, 74.8% [95% CI, 68.0%-81.6%]) vs boys (65.7% [95% CI, 59.3%72.1%]).

867 citations

Journal ArticleDOI
01 Jul 2004-Ecology
TL;DR: This paper presents four applications of PCNM analysis to ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data, and new ecological knowledge was obtained through this analysis.
Abstract: Spatial structures may not only result from ecological interactions, they may also play an essential functional role in organizing the interactions. Modeling spatial patterns at multiple spatial and temporal scales is thus a crucial step to understand the functioning of ecological communities. PCNM (principal coordinates of neighbor matrices) analysis achieves a spectral decomposition of the spatial relationships among the sampling sites, creating variables that correspond to all the spatial scales that can be perceived in a given data set. The analysis then finds the scales to which a data table of interest responds. The significant PCNM variables can be directly interpreted in terms of spatial scales, or included in a procedure of variation decomposition with respect to spatial and environmental components. This paper presents four applications of PCNM analysis to ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data. The data sets include Amazonian ferns, tropical marine zooplankton, chlorophyll in a marine lagoon, and oribatid mites in a peat bog. In each case, new ecological knowledge was obtained through PCNM analysis.

864 citations

Journal ArticleDOI
Predrag Radivojac1, Wyatt T. Clark1, Tal Ronnen Oron2, Alexandra M. Schnoes3, Tobias Wittkop2, Artem Sokolov4, Artem Sokolov5, Kiley Graim4, Christopher S. Funk6, Karin Verspoor6, Asa Ben-Hur4, Gaurav Pandey7, Gaurav Pandey8, Jeffrey M. Yunes8, Ameet Talwalkar8, Susanna Repo8, Susanna Repo9, Michael L Souza8, Damiano Piovesan10, Rita Casadio10, Zheng Wang11, Jianlin Cheng11, Hai Fang, Julian Gough12, Patrik Koskinen13, Petri Törönen13, Jussi Nokso-Koivisto13, Liisa Holm13, Domenico Cozzetto14, Daniel W. A. Buchan14, Kevin Bryson14, David T. Jones14, Bhakti Limaye15, Harshal Inamdar15, Avik Datta15, Sunitha K Manjari15, Rajendra Joshi15, Meghana Chitale16, Daisuke Kihara16, Andreas Martin Lisewski17, Serkan Erdin17, Eric Venner17, Olivier Lichtarge17, Robert Rentzsch14, Haixuan Yang18, Alfonso E. Romero18, Prajwal Bhat18, Alberto Paccanaro18, Tobias Hamp19, Rebecca Kaßner19, Stefan Seemayer19, Esmeralda Vicedo19, Christian Schaefer19, Dominik Achten19, Florian Auer19, Ariane Boehm19, Tatjana Braun19, Maximilian Hecht19, Mark Heron19, Peter Hönigschmid19, Thomas A. Hopf19, Stefanie Kaufmann19, Michael Kiening19, Denis Krompass19, Cedric Landerer19, Yannick Mahlich19, Manfred Roos19, Jari Björne20, Tapio Salakoski20, Andrew Wong21, Hagit Shatkay21, Hagit Shatkay22, Fanny Gatzmann23, Ingolf Sommer23, Mark N. Wass24, Michael J.E. Sternberg24, Nives Škunca, Fran Supek, Matko Bošnjak, Panče Panov, Sašo Džeroski, Tomislav Šmuc, Yiannis A. I. Kourmpetis25, Yiannis A. I. Kourmpetis26, Aalt D. J. van Dijk25, Cajo J. F. ter Braak25, Yuanpeng Zhou27, Qingtian Gong27, Xinran Dong27, Weidong Tian27, Marco Falda28, Paolo Fontana, Enrico Lavezzo28, Barbara Di Camillo28, Stefano Toppo28, Liang Lan29, Nemanja Djuric29, Yuhong Guo29, Slobodan Vucetic29, Amos Marc Bairoch30, Amos Marc Bairoch31, Michal Linial32, Patricia C. Babbitt3, Steven E. Brenner8, Christine A. Orengo14, Burkhard Rost19, Sean D. Mooney2, Iddo Friedberg33 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
Abstract: Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

859 citations


Authors

Showing all 16461 results

NameH-indexPapersCitations
Kari Alitalo174817114231
Mika Kivimäki1661515141468
Jaakko Kaprio1631532126320
Veikko Salomaa162843135046
Markus W. Büchler148154593574
Eugene C. Butcher14644672849
Steven Williams144137586712
Terho Lehtimäki1421304106981
Olli T. Raitakari1421232103487
Pim Cuijpers13698269370
Jeroen J. Bax132130674992
Sten Orrenius13044757445
Aarno Palotie12971189975
Stefan W. Hell12757765937
Carlos López-Otín12649483933
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023102
2022290
20212,673
20202,688
20192,407
20182,189