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Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.
Rationale: Potassium (K) is a major component of several silicate minerals and seawater, and, therefore, constraining past changes in the potassium cycle is a promising way of tracing large-scale geological processes on Earth. However, [K] measurement using inductively coupled plasma mass spectrometry (ICP-MS) is challenging due to an ArH+ interference, which may be of a similar magnitude to the K+ ion beam in samples with <0.1% m/m [K].
Methods: In this work, we investigated the effect of the ArH+ interference on K/Ca data quality by comparing results from laser-ablation (LA)-ICP-MS measured in medium and high mass resolution modes and validating our LA results via solution ICP-optical emission spectroscopy (OES) and solution ICP-MS measurements. To do so, we used a wide range of geological reference materials, with a particular focus on marine carbonates, which are potential archives of past changes in the K cycle but are typically characterised by [K] < 200 μg/g. In addition, we examine the degree to which trace-element data quality is driven by downhole fractionation during LA-ICP-MS measurements.
Results: Our results show that medium mass resolution (MR) mode is sufficiently capable of minimising the effect of the ArH+ interference on K+. However, the rate of downhole fractionation for Na and K varies between different samples as a result of their differing bulk composition, resulting in matrix-specific inaccuracy. We show how this can be accounted for via downhole fractionation corrections, resulting in an accuracy of better than 1% and a long-term reproducibility (intermediate precision) of <6% (relative standard deviation) in JCp-1NP using LA-ICP-MS in MR mode.
Conclusion: Our [K] measurement protocol is demonstrably precise and accurate and applicable to a wide range of materials. The measurement of K/Ca in relatively low-[K] marine carbonates is presented here as a key example of a new application opened up by these advances.
Non-matrix-matched calibration of laser ablation ICPMS (trace/major) element data is a common quantification strategy. However, LA sampling is associated with downhole elemental fractionation, potentially causing inaccuracies if the magnitude of fractionation between the sample and reference material (RM) differs. Here, we estimate fractionation factors (FFs) for different elements (El) in a range of RMs relative to NIST SRM610/612 (FFEl/Ca-NIST) and evaluate element-specific corrections for downhole fractionation using these measured FFEl/Ca-NIST. Significantly different mean El/Ca values were observed before and after correction, particularly for the alkali elements (all RMs), and B, Fe, and Zn (some RMs), notably improving accuracy, especially for the alkali elements. In cases where this methodology does not result in an accuracy improvement, this may help identify underlying issues in reported/reference values for RMs, given that this phenomenon should be accounted for. Overall, we recommend considering routine assessment of FFs and applying a FF correction to enhance data quality.
No association between Parkinson disease and autoantibodies against NMDA-type glutamate receptors
(2019)
Background: IgG-class autoantibodies to N-Methyl-D-Aspartate (NMDA)-type glutamate receptors define a novel entity of autoimmune encephalitis. Studies examining the prevalence of NMDA IgA/IgM antibodies in patients with Parkinson disease with/without dementia produced conflicting results. We measured NMDA antibodies in a large, well phenotyped sample of Parkinson patients without and with cognitive impairment (n = 296) and controls (n = 295) free of neuropsychiatric disease. Detailed phenotyping and large numbers allowed statistically meaningful correlation of antibody status with diagnostic subgroups as well as quantitative indicators of disease severity and cognitive impairment.
Methods: NMDA antibodies were analysed in the serum of patients and controls using well established validated assays. We used anti-NMDA antibody positivity as the main independent variable and correlated it with disease status and phenotypic characteristics.
Results: The frequency of NMDA IgA/IgM antibodies was lower in Parkinson patients (13%) than in controls (22%) and higher than in previous studies in both groups. NMDA IgA/IgM antibodies were neither significantly associated with diagnostic subclasses of Parkinson disease according to cognitive impairment, nor with quantitative indicators of disease severity and cognitive impairment. A positive NMDA antibody status was positively correlated with age in controls but not in Parkinson patients.
Conclusion: It is unlikely albeit not impossible that NMDA antibodies play a significant role in the pathogenesis or progression of Parkinson disease e.g. to Parkinson disease with dementia, while NMDA IgG antibodies define a separate disease of its own.
A wide variety of enzymatic pathways that produce specialized metabolites in bacteria, fungi and plants are known to be encoded in biosynthetic gene clusters. Information about these clusters, pathways and metabolites is currently dispersed throughout the literature, making it difficult to exploit. To facilitate consistent and systematic deposition and retrieval of data on biosynthetic gene clusters, we propose the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard.
This paper reports on Monte Carlo simulation results for future measurements of the moduli of time-like proton electromagnetic form factors, |GE | and |GM|, using the ¯pp → μ+μ− reaction at PANDA (FAIR). The electromagnetic form factors are fundamental quantities parameterizing the electric and magnetic structure of hadrons. This work estimates the statistical and total accuracy with which the form factors can be measured at PANDA, using an analysis of simulated data within the PandaRoot software framework. The most crucial background channel is ¯pp → π+π−,due to the very similar behavior of muons and pions in the detector. The suppression factors are evaluated for this and all other relevant background channels at different values of antiproton beam momentum. The signal/background separation is based on a multivariate analysis, using the Boosted Decision Trees method. An expected background subtraction is included in this study, based on realistic angular distribuations of the background contribution. Systematic uncertainties are considered and the relative total uncertainties of the form factor measurements are presented.