First direct measurements of g factors of the three superdeformed bands of 194Hg

The average g factors of the high-energy states of the three superdeformed bands in 194Hg were determined directly in a transient field experiment. The reaction 150Nd(48Ca,4n)194Hg at a beam energy of 203 MeV was used to provide recoiling reaction product nuclei with sufficient velocity to traverse a gadolinium ferromagnetic layer. The resulting g factors g(SD1)50.36(10), g(SD2)50.41(20), and g(SD3)50.71(26) are in agreement with cranked Hartree-Fock calculations as well as with the picture of a rigid rotation for which g 5Z/A

Single particle signatures in high-spin, quasi continuum states in 193,194 Hg from g-factor measurements

The average g factors of high spin, high-excitation energy, quasi continuum structures in 194,193Hg were measured by observing the precessions of the angular distributions of γ-ray transitions in several normal-deformation bands that coalesce in the decay of the entry distribution of states. The average g factors of the states leading to the three main bands in the 193,194Hg isoles were: 〈g(193Hg)〉 = +0.19(1) and 〈g(194Hg)〉 = +0.26(1), respectively. These average g factors are smaller than the average of the g factors of the high energy states in the three superdeformed bands of 194Hg, 〈g(SD;194Hg)〉 = +0.41(8). While the nucleus in the superdeformed well behaves like a rigid rotor, the present results demonstrate the important role played by multiple, quasi particle neutron configurations in the structure of normal-deformation, highly-excited nuclear states

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