Institution
University of Iceland
Education•Reykjavik, Suðurnes, Iceland•
About: University of Iceland is a education organization based out in Reykjavik, Suðurnes, Iceland. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 5423 authors who have published 16199 publications receiving 694762 citations. The organization is also known as: Háskóli Íslands.
Papers published on a yearly basis
Papers
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Washington University in St. Louis1, Icahn School of Medicine at Mount Sinai2, Yale University3, University of Oxford4, Boston University5, Lille University of Science and Technology6, Pasteur Institute7, University of Iceland8, University of Washington9, Erasmus University Rotterdam10, Cardiff University11, Columbia University12, Case Western Reserve University13, University of Miami14, National Institutes of Health15, University of Pennsylvania16, Brigham Young University17
TL;DR: Results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function, and experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells.
Abstract: A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and 14 novel loci associated with age at onset. Linkage disequilibrium score regression of 220 cell types implicated the regulation of myeloid gene expression in AD risk. The minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability was enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affected the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.
308 citations
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TL;DR: A genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals identifies 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10−8, which together explain 4–9% of the phenotypic variance per trait.
Abstract: Anaemia is a chief determinant of global ill health, contributing to cognitive impairment, growth retardation and impaired physical capacity. To understand further the genetic factors influencing red blood cells, we carried out a genome-wide association study of haemoglobin concentration and related parameters in up to 135,367 individuals. Here we identify 75 independent genetic loci associated with one or more red blood cell phenotypes at P < 10(-8), which together explain 4-9% of the phenotypic variance per trait. Using expression quantitative trait loci and bioinformatic strategies, we identify 121 candidate genes enriched in functions relevant to red blood cell biology. The candidate genes are expressed preferentially in red blood cell precursors, and 43 have haematopoietic phenotypes in Mus musculus or Drosophila melanogaster. Through open-chromatin and coding-variant analyses we identify potential causal genetic variants at 41 loci. Our findings provide extensive new insights into genetic mechanisms and biological pathways controlling red blood cell formation and function.
306 citations
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TL;DR: The study found that girls' lower enrollment in organized sport clubs fully accounts for gender differences in frequency of overall physical activity, and largely accounts for Gender differences in Frequency of strenuous activity.
306 citations
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TL;DR: A novel method for the differential analysis of RNA-Seq data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance is described.
Abstract: We describe a novel method for the differential analysis of RNA-Seq data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. The method is implemented in an interactive shiny app called sleuth that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of RNA-Seq experiments.
305 citations
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TL;DR: Considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed and demonstrates the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.
Abstract: Sparse representation has been demonstrated to be a powerful tool in classification of hyperspectral images (HSIs). The spatial context of an HSI can be exploited by first defining a local region for each test pixel and then jointly representing pixels within each region by a set of common training atoms (samples). However, the selection of the optimal region scale (size) for different HSIs with different types of structures is a nontrivial task. In this paper, considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. Experiments on several real HSI data sets demonstrate the qualitative and quantitative superiority of the proposed MASR algorithm when compared to several well-known classifiers.
304 citations
Authors
Showing all 5561 results
Name | H-index | Papers | Citations |
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Albert Hofman | 267 | 2530 | 321405 |
Kari Stefansson | 206 | 794 | 174819 |
Ronald Klein | 194 | 1305 | 149140 |
Eric Boerwinkle | 183 | 1321 | 170971 |
Unnur Thorsteinsdottir | 167 | 444 | 121009 |
Vilmundur Gudnason | 159 | 837 | 123802 |
Hakon Hakonarson | 152 | 968 | 101604 |
Bernhard O. Palsson | 147 | 831 | 85051 |
Andrew T. Hattersley | 146 | 768 | 106949 |
Fernando Rivadeneira | 146 | 628 | 86582 |
Rattan Lal | 140 | 1383 | 87691 |
Jonathan G. Seidman | 137 | 563 | 89782 |
Christine E. Seidman | 134 | 519 | 67895 |
Augustine Kong | 134 | 237 | 89818 |
Timothy M. Frayling | 133 | 500 | 100344 |