Association between depression and sleep apnoea: a Mendelian randomisation study

BackgroundStudies have reported a close relationship between depression and sleep apnoea, yet it is unknown whether these are causally related. Thus, we aimed to determine whether depression is associated with the aetiology of sleep apnoea.MethodsWe used publicly available genetic summary data from two large consortia, the Psychiatric Genomics Consortium, with data from 36 single-nucleotide polymorphisms (SNPs) closely associated with major depressive disorder (MDD) and UK Biobank, including 456 736 patients with sleep apnoea and 766 964 controls. For Mendelian randomisation (MR) analysis, we used the inverse-variance weighted method, weighted median method, MR-Egger regression, MR pleiotropy residual sum, and outlier test to retrieve summary data. Analyses were performed using the “TwoSampleMR” package in R.ResultsOf the 36 SNPs associated with MDD, we found statistically significant evidence of a potential causal effect of MDD on the risk of sleep apnoea (odds ratio 1.004, 95% confidence interval: 1.001–1.006, p=0.001). Similar results were obtained using the MR-Egger and weighted median methods. Additionally, we found no heterogeneity or pleiotropy.ConclusionsOur findings suggest that depression slightly increases the risk of sleep apnoea. Further investigation of the potential biological mechanisms is necessary.

Нistoriographical characteristics of the aggregated data published on the official Facebook pages of the Lviv Regional State Administration

The article is devoted to the analysis of a separate class of historical sources — aggregated data and the format of their presentation in the Ukrainian segment of the Internet, in particular, on the official Facebook pages of the authorities and administration of Ukraine. The groups, types, and kinds of aggregated data that can be used in the research activities of the historian, if for some reason such information is not available on other resources, were described. The question of qualitative characteristics of different types of aggregated data is considered, namely: significance, validity, relevance. An assessment is given of the studied array of summary data of such types as departmental statistics, expert data, the content of publications, information about events available on the official Facebook pages of the Lviv Regional State Administration (LRSA), and analysis of their historiographical characteristics.

Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

Improved analyses of GWAS summary statistics by reducing data heterogeneity and errors

Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants (false-positive rate reduced from >28% to <2% in the presence of heterogeneity between GWAS and LD reference). We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis.

COVID-19 and Older Adults: Coping With Long-Term Pandemic Precautions

Studies conducted at the beginning of Covid-19 precautions suggested that older adults were stressed, but hopeful. Less is known how coping has changed for older adults after experiencing months-long pandemic precautions. We explore differences in coping between the initial pandemic declaration in March 2020, and 9 months later, via an internet survey fielded in November 2020 (n= 781). We present summary data, using chi-square tests for subgroup analyses. A majority of respondents (aged M=66 yrs, range 60-89) were women (64%) and White (94%). When asked to compare their feelings to the beginning of the pandemic, 44.8% were more frustrated, 38.7% were more stressed, and 32.7% were more anxious. However, 38.3% were more appreciative. Women were significantly more likely than men to report increases in feeling frustrated, angry, scared, stressed, sad, and hopeless. Introverts were significantly more likely than extroverts to report an increase in loneliness and stress. Since the first few weeks of the pandemic, respondents reported more communication through video calls (45.2%), texting (40.2%), and phone calls (28.8%). Additionally, 61.5% spent more time on computers/tablets, 47.2% spent more time watching TV, and 24.5% spent more time praying. Extroverts were significantly more likely than introverts to report an increase in time with TV, phones, and computers/tablets. Women were significantly more likely than men to report increased texting and praying. These data provide further understanding of the impact of long-term pandemic precautions on older adults and suggest particular subgroups of older adults may benefit from public health and mental health interventions.

Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model

With the increasing availability of large-scale GWAS summary data on various traits, Mendelian randomization (MR) has become commonly used to infer causality between a pair of traits, an exposure and an outcome. It depends on using genetic variants, typically SNPs, as instrumental variables (IVs). The inverse-variance weighted (IVW) method (with a fixed-effect meta-analysis model) is most powerful when all IVs are valid; however, when horizontal pleiotropy is present, it may lead to biased inference. On the other hand, Egger regression is one of the most widely used methods robust to (uncorrelated) pleiotropy, but it suffers from loss of power. We propose a two-component mixture of regressions to combine and thus take advantage of both IVW and Egger regression; it is often both more efficient (i.e. higher powered) and more robust to pleiotropy (i.e. controlling type I error) than either IVW or Egger regression alone by accounting for both valid and invalid IVs respectively. We propose a model averaging approach and a novel data perturbation scheme to account for uncertainties in model/IV selection, leading to more robust statistical inference for finite samples. Through extensive simulations and applications to the GWAS summary data of 48 risk factor-disease pairs and 63 genetically uncorrelated trait pairs, we showcase that our proposed methods could often control type I error better while achieving much higher power than IVW and Egger regression (and sometimes than several other new/popular MR methods). We expect that our proposed methods will be a useful addition to the toolbox of Mendelian randomization for causal inference.

Fine-mapping from summary data with the "Sum of Single Effects" model

In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To achieve this we introduce a simple strategy that could be used to extend any individual-level data method to deal with summary data. In essence, this strategy replaces the usual regression likelihood with an analogous likelihood based on summary data, exploiting the close connection between the two. Our strategy also has the benefit of dealing automatically with non-invertible LD matrices, which arise frequently in fine-mapping applications, and can complicate inference. We highlight other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Simulation studies show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.

Causality of Abdominal Obesity on Cognition: a Trans-ethnic Mendelian Randomization study

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding, reverse causality, or causal remains inconclusive. Using two-sample Mendelian randomization (MR) analyses, we investigated the causality of overall obesity, measured by BMI, and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), on cognition. Using summary data from the GIANT consortium, COGENT consortium, and UK Biobank of European ancestry, there was no causal effect of BMI on cognition performance (beta[95% CI]=-0.04[-0.12,0.04], p-value=0.35); however, a 1-SD increase in WHRadjBMI was associated with 0.07 standardized decrease in cognition performance (beta[95% CI]=-0.07[-0.12,-0.02], p=0.006). Using raw data from the Taiwan Biobank of Asian ancestry, there was no causal effect of BMI on cognitive aging (beta[95% CI]=0.00[-0.09,0.09], p-value=0.95); however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized decrease in cognitive aging (beta[95% CI]=-0.17[-0.30,-0.03], p=0.02). This trans-ethnic MR study reveals that abdominal adiposity impairs cognition.

Investigating the transparency of reporting in two-sample summary data Mendelian randomization studies

Background: Two-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear. Objectives: We aimed to assess the reporting quality of studies that used MR-Base, one of the most popular platforms for implementing 2SMR analysis. Methods: We searched Web of Science Core Collection, PsycInfo, MEDLINE, EMBASE and citations listed in Google Scholar of the MR-Base descriptor paper for any published MR study that used MR-Base during any component of the MR analysis. Studies were screened by two independent reviewers. We created a bespoke reporting checklist to evaluate reporting quality of 2SMR studies. Information was extracted by at least two independent reviewers. Results: 87 studies were included in the primary analysis, of which 14 had at least 10 phenotypes. Reporting quality was generally poor with a mean of 53% (SD = 14%) of items reported in each study. Many items required for evaluating the validity of key assumptions made in MR were poorly reported: only 44% of studies provided sufficient details for assessing if the variant associates with the exposure ('relevance' assumption), 31% for the assessing if there are any variant-outcome confounders ('independence' assumption), 89% for the assessing if the variant causes the outcome independently of the exposure ('exclusion restriction' assumption), and 32% for assumptions of falsification tests. We found no evidence of a change in reporting over time and findings were similar in a random sample of MR studies that did not use the MR-Base platform. Discussion: The quality of reporting of two-sample Mendelian randomization studies in our sample was generally poor. Journals and researchers should implement the STROBE-MR guidelines to improve reporting quality. Other: Funding: ESRC, Regression: This study pre-registered on the OSF, and the protocol can be found at DOI 10.17605/OSF.IO/NFM27