Joint Analysis Of Psychiatric Disorders Increases Accuracy Of Risk Prediction For Schizophrenia, Bipolar Disorder, And Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

ANALYSING URBAN EFFECTS IN BUDAPEST USING THE WRF NUMERICAL WEATHER PREDICTION MODEL

Continuously growing cities significantly modify the entire environment through air pollution and modification of land surface, resulting altered energy budget and land-atmosphere exchange processes over built-up areas. These effects mainly appear in cities or metropolitan areas, leading to the Urban Heat Island (UHI) phenomenon, which occurs due to the temperature difference between the built-up areas and their cooler surroundings. The Weather Research and Forecasting (WRF) mesoscale model coupled to multilayer urban canopy parameterisation is used to investigate this phenomenon for Budapest and its surroundings with actual land surface properties. In this paper the basic ideas of our research and the methodology in brief are presented. The simulation is completed for one week in summer 2015 with initial meteorological fields from Global Forecasting System (GFS) outputs, under atmospheric conditions of weak wind and clear sky for the Pannonian Basin. Then, to improve the WRF model and its settings, the calculated skin temperature is compared to the remotely sensed measurements derived from satellites Aqua and Terra, and the temporal and spatial bias values are estimated

Renewable Energy Sources

Készült az ELTE Felsőoktatási Struktúraátalakítási Alapból támogatott programja keretében

Assessment of projected climate change in the Carpathian Region using the Holdridge life zone system

In this paper, expected changes in the spatial and altitudinal distribution patterns of Holdridge life zone (HLZ) types are analysed to assess the possible ecological impacts of future climate change for the Carpathian Region, by using 11 bias-corrected regional climate model simulations of temperature and precipitation. The distribution patterns of HLZ types are characterized by the relative extent, the mean centre and the altitudinal range. According to the applied projections, the following conclusions can be drawn: (a) the altitudinal ranges are likely to expand in the future, (b) the lower and upper altitudinal limits as well as the altitudinal midpoints may move to higher altitudes, (c) a northward shift is expected for most HLZ types and (d) the magnitudes of these shifts can even be multiples of those observed in the last century. Related to the northward shifts, the HLZ types warm temperate thorn steppe and subtropical dry forest can also appear in the southern segment of the target area. However, a large uncertainty in the estimated changes of precipitation patterns was indicated by the following: (a) the expected change in the coverage of the HLZ type cool temperate steppe is extremely uncertain because there is no consensus among the projections even in terms of the sign of the change (high inter-model variability) and (b) a significant trend in the westward/eastward shift is simulated just for some HLZ types (high temporal variability). Finally, it is important to emphasize that the uncertainty of our results is further enhanced by the fact that some important aspects (e.g. seasonality of climate variables, direct CO2 effect, etc.) cannot be considered in the estimating process