Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

Long-term trends in catchment organic carbon and nitrogen exports from three acidified catchments in Nova Scotia, Canada

We sampled two streams in southwestern Nova Scotia from 1983 to 2004 and one stream from 1992 to 2004 for total organic carbon (TOC) and nitrogen (TN) in order to investigate if changes in catchment exports could be determined over the sampling periods, and if so what were the controlling factors. We first show that early TOC measurements underestimated concentrations due to analytical shortcomings and then produce a correction to adjust values to more accurate levels. Our trend results showed that TOC concentrations decreased in the two streams with the longest record, from 1980 to 1992 when acid deposition to the area decreased most rapidly, and have remained constant since then. TOC exports only decreased at one site over the total sampling period. As expected, we also measured seasonal changes in exports, with the autumn period showing TOC and TN exports as high as during spring snowmelt. We found that only 24% of deposition N is exported from the larger catchments, most of it in organic form, while the smallest catchment exported 16%. We also show a constant increase in TN from 1994 to the present at all three sites sampled. Our results do not support the hypothesis that reductions in sulfur acidification lead to increases in catchment organic carbon mobilization to streams

The Critical Loads and Levels Approach for Nitrogen

This chapter reports the findings of a Working Group on how atmospheric nitrogen (N) deposition affects both terrestrial and freshwater biodiversity. Regional and global scale impacts on biodiversity are addressed, together with potential indicators. Key conclusions are that: the rates of loss in biodiversity are greatest at the lowest and initial stages of N deposition increase; changes in species compositions are related to the relative amounts of N, carbon (C) and phosphorus (P) in the plant soil system; enhanced N inputs have implications for C cycling; N deposition is known to be having adverse effects on European and North American vegetation composition; very little is known about tropical ecosystem responses, while tropical ecosystems are major biodiversity hotspots and are increasingly recipients of very high N deposition rates; N deposition alters forest fungi and mycorrhyzal relations with plants; the rapid response of forest fungi and arthropods makes them good indicators of change; predictive tools (models) that address ecosystem scale processes are necessary to address complex drivers and responses, including the integration of N deposition, climate change and land use effects; criteria can be identified for projecting sensitivity of terrestrial and aquatic ecosystems to N deposition. Future research and policy-relevant recommendations are identified

Becoming a high-performance work organization: the role of security, employee involvement and training

We discuss an emerging employment system characterized by a high degree of employment security with flexible job assignments, employee involvement in problem solving and continuous improvement, and continuous training of employees. We call this model the SET system (for Security, Employee involvement and Training) and examine case studies of five U.S. firms that are attempting to establish or maintain a SET system. We find that SET systems are difficult to implement in a gradual and partial manner. The three elements of SET reinforce one another and firms that are successful in adopting SET have made an investment to implement all three SET elements simultaneously

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina Desenvolvimento e avaliação de modelos de redes neurais para estimação da irradiação solar diária em Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.<br>O objetivo deste trabalho foi desenvolver modelos de redes neuronais, do tipo retropropagação, para a estimação da irradiação solar, a partir de dados de irradiação solar extraterrestre, amplitude térmica, precipitação, nebulosidade e razão de insolação. O treinamento e a validação foram realizados com dados correspondentes a Córdoba, Argentina. O comportamento e ajuste entre os valores observados e os estimados pelas redes foram avaliados para diferentes combinações das variáveis de entrada, que apresentaram valores do erro quadrático médio entre 3,15 e 3,88 MJ m-2 d-1 . Este último valor corresponde ao modelo que calcula a irradiação somente utilizando precipitação e amplitude térmica diária. Os resultados exibem em todos os modelos um ajuste apropriado ao comportamento sazonal da irradiação solar e permitem concluir a pertinência e o adequado desempenho desse método para estimar fenômenos complexos como a irradiação solar