University of 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.

Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci

Abstract: Meta-analyses of association results for blood pressure using exome-centric single-variant and gene-based tests identified 31 new loci in a discovery stage among 146,562 individuals, with follow-up and meta-analysis in 180,726 additional individuals (total n = 327,288). These blood pressure-associated loci are enriched for known variants for cardiometabolic traits. Associations were also observed for the aggregation of rare and low-frequency missense variants in three genes, NPR1, DBH, and PTPMT1. In addition, blood pressure associations at 39 previously reported loci were confirmed. The identified variants implicate biological pathways related to cardiometabolic traits, vascular function, and development. Several new variants are inferred to have roles in transcription or as hubs in protein-protein interaction networks. Genetic risk scores constructed from the identified variants were strongly associated with coronary disease and myocardial infarction. This large collection of blood pressure-associated loci suggests new therapeutic strategies for hypertension, emphasizing a link with cardiometabolic risk.

Land-Cover Mapping by Markov Modeling of Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images

Abstract: Markov models represent a wide and general family of stochastic models for the temporal and spatial dependence properties associated to 1-D and multidimensional random sequences or random fields. Their applications range over a wide variety of subareas of the information and communication technology (ICT) field, including networking, automation, speech processing, genomic-sequence analysis, or image processing. Focusing on the applicative problem of land-cover mapping from very-high-resolution (VHR) remote sensing images, which is a relevant problem in many applications of environmental monitoring and natural resource exploitation, Markov models convey a great potential, thanks to their capability to effectively describe and incorporate the spatial information associated with image data into an image-classification process. In this framework, the main ideas and previous work about Markov modeling for VHR image classification will be recalled in this paper and processing results obtained through recent methods proposed by the authors will be discussed.

Brain age prediction using deep learning uncovers associated sequence variants

Abstract: Machine learning algorithms can be trained to estimate age from brain structural MRI. The difference between an individual’s predicted and chronological age, predicted age difference (PAD), is a phenotype of relevance to aging and brain disease. Here, we present a new deep learning approach to predict brain age from a T1-weighted MRI. The method was trained on a dataset of healthy Icelanders and tested on two datasets, IXI and UK Biobank, utilizing transfer learning to improve accuracy on new sites. A genome-wide association study (GWAS) of PAD in the UK Biobank data (discovery set: $$N=12378$$, replication set: $$N=4456$$) yielded two sequence variants, rs1452628-T ($$\beta =-0.08$$, $$P=1.15\times{10}^{-9}$$) and rs2435204-G ($$\beta =0.102$$, $$P=9.73\times 1{0}^{-12}$$). The former is near KCNK2 and correlates with reduced sulcal width, whereas the latter correlates with reduced white matter surface area and tags a well-known inversion at 17q21.31 (H2). Machine learning algorithms can be trained to estimate age from brain structural MRI. Here, the authors introduce a new deep-learning-based age prediction approach, and then carry out a GWAS of the difference between predicted and chronological age, revealing two associated variants.

Galaxy formation with BECDM - I. Turbulence and relaxation of idealized haloes.

Abstract: We present a theoretical analysis of some unexplored aspects of relaxed Bose–Einstein condensate dark matter (BECDM) haloes. This type of ultralight bosonic scalar field dark matter is a viable alternative to the standard cold dark matter (CDM) paradigm, as it makes the same large-scale predictions as CDM and potentially overcomes CDM's small-scale problems via a galaxy-scale de Broglie wavelength. We simulate BECDM halo formation through mergers, evolved under the Schrodinger–Poisson equations. The formed haloes consist of a soliton core supported against gravitational collapse by the quantum pressure tensor and an asymptotic r^−3 NFW-like profile. We find a fundamental relation of the core-to-halo mass with the dimensionless invariant Ξ ≡ |E|/M^3/(Gm/ℏ)^2 or M_c/M ≃ 2.6Ξ^1/3, linking the soliton to global halo properties. For r ≥ 3.5 r_c core radii, we find equipartition between potential, classical kinetic and quantum gradient energies. The haloes also exhibit a conspicuous turbulent behaviour driven by the continuous reconnection of vortex lines due to wave interference. We analyse the turbulence 1D velocity power spectrum and find a k^−1.1 power law. This suggests that the vorticity in BECDM haloes is homogeneous, similar to thermally-driven counterflow BEC systems from condensed matter physics, in contrast to a k^−5/3 Kolmogorov power law seen in mechanically-driven quantum systems. The mode where the power spectrum peaks is approximately the soliton width, implying that the soliton-sized granules carry most of the turbulent energy in BECDM haloes.

The topography of mutational processes in breast cancer genomes

Abstract: Somatic mutations in human cancers show unevenness in genomic distribution that correlate with aspects of genome structure and function. These mutations are, however, generated by multiple mutational processes operating through the cellular lineage between the fertilized egg and the cancer cell, each composed of specific DNA damage and repair components and leaving its own characteristic mutational signature on the genome. Using somatic mutation catalogues from 560 breast cancer whole-genome sequences, here we show that each of 12 base substitution, 2 insertion/deletion (indel) and 6 rearrangement mutational signatures present in breast tissue, exhibit distinct relationships with genomic features relating to transcription, DNA replication and chromatin organization. This signature-based approach permits visualization of the genomic distribution of mutational processes associated with APOBEC enzymes, mismatch repair deficiency and homologous recombinational repair deficiency, as well as mutational processes of unknown aetiology. Furthermore, it highlights mechanistic insights including a putative replication-dependent mechanism of APOBEC-related mutagenesis.