Lund University

About: Lund University is a education organization based out in Lund, Sweden. It is known for research contribution in the topics: Population & Cancer. The organization has 42345 authors who have published 124676 publications receiving 5016438 citations. The organization is also known as: Lunds Universitet & University of Lund.

A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium

Abstract: The number and volume of cells in the blood affect a wide range of disorders including cancer and cardiovascular, metabolic, infectious and immune conditions. We consider here the genetic variation in eight clinically relevant hematological parameters, including hemoglobin levels, red and white blood cell counts and platelet counts and volume. We describe common variants within 22 genetic loci reproducibly associated with these hematological parameters in 13,943 samples from six European population-based studies, including 6 associated with red blood cell parameters, 15 associated with platelet parameters and 1 associated with total white blood cell count. We further identified a long-range haplotype at 12q24 associated with coronary artery disease and myocardial infarction in 9,479 cases and 10,527 controls. We show that this haplotype demonstrates extensive disease pleiotropy, as it contains known risk loci for type 1 diabetes, hypertension and celiac disease and has been spread by a selective sweep specific to European and geographically nearby populations.

Why resilience is unappealing to social science: Theoretical and empirical investigations of the scientific use of resilience

Abstract: Resilience is often promoted as a boundary concept to integrate the social and natural dimensions of sustainability. However, it is a troubled dialogue from which social scientists may feel detached. To explain this, we first scrutinize the meanings, attributes, and uses of resilience in ecology and elsewhere to construct a typology of definitions. Second, we analyze core concepts and principles in resilience theory that cause disciplinary tensions between the social and natural sciences (system ontology, system boundary, equilibria and thresholds, feedback mechanisms, self-organization, and function). Third, we provide empirical evidence of the asymmetry in the use of resilience theory in ecology and environmental sciences compared to five relevant social science disciplines. Fourth, we contrast the unification ambition in resilience theory with methodological pluralism. Throughout, we develop the argument that incommensurability and unification constrain the interdisciplinary dialogue, whereas pluralism drawing on core social scientific concepts would better facilitate integrated sustainability research.

Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application

Abstract: Causality tests in the Granger's sense are increasingly applied in empirical research. Since the unit root revolution in time-series analysis, several modifications of tests for causality have been introduced in the literature. One of the recent developments is the Toda-Yamamoto modified Wald (MWALD) test, which is attractive due to its simple application, its absence of pre-testing distortions, and its basis on a standard asymptotical distribution irrespective of the number of unit roots and the cointegrating properties of the data. This study investigates the size properties of the MWALD test and finds that in small sample sizes this test performs poorly on those properties when using its asymptotical distribution, the chi-square. It is suggested that use be made of a leveraged bootstrap distribution to lower the size distortions. Monte Carlo simulation results show that an MWALD test based on a bootstrap distribution has much smaller size distortions than corresponding cases when the asymptotic distribution is used. These results hold for different sample sizes, integration orders, and error term processes (homoscedastic or ARCH). This new method is applied to the testing of the efficient market hypothesis.