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.

Sequence Variant on 8q24 Confers Susceptibility to Urinary Bladder Cancer

Abstract: We conducted a genome-wide SNP association study on 1,803 urinary bladder cancer (UBC) cases and 34,336 controls from Iceland and The Netherlands and follow up studies in seven additional case-control groups (2,165 cases and 3,800 controls). The strongest association was observed with allele T of rs9642880 on chromosome 8q24, 30 kb upstream of MYC (allele-specific odds ratio (OR) = 1.22; P = 9.34 x 10(-12)). Approximately 20% of individuals of European ancestry are homozygous for rs9642880[T], and their estimated risk of developing UBC is 1.49 times that of noncarriers. No association was observed between UBC and the four 8q24 variants previously associated with prostate, colorectal and breast cancers, nor did rs9642880 associate with any of these three cancers. A weaker signal, but nonetheless of genome-wide significance, was captured by rs710521[A] located near TP63 on chromosome 3q28 (allele-specific OR = 1.19; P = 1. 15 x 10(-7)).

Parental influence on human germline de novo mutations in 1,548 trios from Iceland

Abstract: The characterization of mutational processes that generate sequence diversity in the human genome is of paramount importance both to medical genetics and to evolutionary studies. To understand how the age and sex of transmitting parents affect de novo mutations, here we sequence 1,548 Icelanders, their parents, and, for a subset of 225, at least one child, to 35× genome-wide coverage. We find 108,778 de novo mutations, both single nucleotide polymorphisms and indels, and determine the parent of origin of 42,961. The number of de novo mutations from mothers increases by 0.37 per year of age (95% CI 0.32-0.43), a quarter of the 1.51 per year from fathers (95% CI 1.45-1.57). The number of clustered mutations increases faster with the mother's age than with the father's, and the genomic span of maternal de novo mutation clusters is greater than that of paternal ones. The types of de novo mutation from mothers change substantially with age, with a 0.26% (95% CI 0.19-0.33%) decrease in cytosine-phosphate-guanine to thymine-phosphate-guanine (CpG>TpG) de novo mutations and a 0.33% (95% CI 0.28-0.38%) increase in C>G de novo mutations per year, respectively. Remarkably, these age-related changes are not distributed uniformly across the genome. A striking example is a 20 megabase region on chromosome 8p, with a maternal C>G mutation rate that is up to 50-fold greater than the rest of the genome. The age-related accumulation of maternal non-crossover gene conversions also mostly occurs within these regions. Increased sequence diversity and linkage disequilibrium of C>G variants within regions affected by excess maternal mutations indicate that the underlying mutational process has persisted in humans for thousands of years. Moreover, the regional excess of C>G variation in humans is largely shared by chimpanzees, less by gorillas, and is almost absent from orangutans. This demonstrates that sequence diversity in humans results from evolving interactions between age, sex, mutation type, and genomic location.

Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis

Abstract: In this letter, a technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images. The ICA maps the data into a subspace in which the components are as independent as possible. APs, which are extracted by using several attributes, are applied to each image associated with an extracted independent component, leading to a set of extended EAPs. Two approaches are presented for including the computed profiles in the analysis. The features extracted by the morphological processing are then classified with an SVM. The experiments carried out on two hyperspectral images proved the effectiveness of the proposed technique.