289 research outputs found
Gate control of low-temperature spin dynamics in two-dimensional hole systems
We have investigated spin and carrier dynamics of resident holes in
high-mobility two-dimensional hole systems in GaAs/AlGaAs
single quantum wells at temperatures down to 400 mK. Time-resolved Faraday and
Kerr rotation, as well as time-resolved photoluminescence spectroscopy are
utilized in our study. We observe long-lived hole spin dynamics that are
strongly temperature dependent, indicating that in-plane localization is
crucial for hole spin coherence. By applying a gate voltage, we are able to
tune the observed hole g factor by more than 50 percent. Calculations of the
hole g tensor as a function of the applied bias show excellent agreement with
our experimental findings.Comment: 8 pages, 7 figure
Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation
We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis
The genetic basis of major depression
Major depressive disorder (MDD) is a common, debilitating, phenotypically heterogeneous disorder with heritability ranges from 30% to 50%. Compared to other psychiatric disorders, its high prevalence, moderate heritability, and strong polygenicity have posed major challenges for gene-mapping in MDD. Studies of common genetic variation in MDD, driven by large international collaborations such as the Psychiatric Genomics Consortium, have confirmed the highly polygenic nature of the disorder and implicated over 100 genetic risk loci to date. Rare copy number variants associated with MDD risk were also recently identified. The goal of this review is to present a broad picture of our current understanding of the epidemiology, genetic epidemiology, molecular genetics, and gene–environment interplay in MDD. Insights into the impact of genetic factors on the aetiology of this complex disorder hold great promise for improving clinical care
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS
DeepWAS: multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/cellmapslab/DeepWAS
Spin dynamics in semiconductors
This article reviews the current status of spin dynamics in semiconductors
which has achieved a lot of progress in the past years due to the fast growing
field of semiconductor spintronics. The primary focus is the theoretical and
experimental developments of spin relaxation and dephasing in both spin
precession in time domain and spin diffusion and transport in spacial domain. A
fully microscopic many-body investigation on spin dynamics based on the kinetic
spin Bloch equation approach is reviewed comprehensively.Comment: a review article with 193 pages and 1103 references. To be published
in Physics Reports
Optical control of one and two hole spins in interacting quantum dots
A single hole spin in a semiconductor quantum dot has emerged as a quantum
bit that is potentially superior to an electron spin. A key feature of holes is
that they have a greatly reduced hyperfine interaction with nuclear spins,
which is one of the biggest difficulties in working with an electron spin. It
is now essential to show that holes are viable for quantum information
processing by demonstrating fast quantum gates and scalability. To this end we
have developed InAs/GaAs quantum dots coupled through coherent tunneling and
charged with controlled numbers of holes. We report fast, single qubit gates
using a sequence of short laser pulses. We then take the important next step
toward scalability of quantum information by optically controlling two
interacting hole spins in separate dots.Comment: 5 figure
Genetic comorbidity between major depression and cardiometabolic traits, stratified by age at onset of major depression
It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect
their overlapping genetic etiology irrespective of the age depression first presents
- …