Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States

Importance Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters. Objective To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors. Design, Setting, and Participants This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC. Main Outcome and Measure Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index. Results The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4%) were American Indian or Alaska Native individuals, 11 448 (6.0%) were Asian individuals, 20 277 (10.5%) were Black individuals, 15 036 (7.8%) were Hispanic individuals, 1975 (1.0%) were Pacific Islander individuals, 138 702 (72.1%) were White individuals, and 4065 (2.1%) were individuals of another race. Additionally, 126 381 respondents (65.7%) identified as female and 65 890 (34.3%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity. Conclusions and Relevance In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed

Runs of Homozygosity Implicate Autozygosity as a Schizophrenia Risk Factor

Autozygosity occurs when two chromosomal segments that are identical from a common ancestor are inherited from each parent. This occurs at high rates in the offspring of mates who are closely related (inbreeding), but also occurs at lower levels among the offspring of distantly related mates. Here, we use runs of homozygosity in genome-wide SNP data to estimate the proportion of the autosome that exists in autozygous tracts in 9,388 cases with schizophrenia and 12,456 controls. We estimate that the odds of schizophrenia increase by ∼17% for every 1% increase in genome-wide autozygosity. This association is not due to one or a few regions, but results from many autozygous segments spread throughout the genome, and is consistent with a role for multiple recessive or partially recessive alleles in the etiology of schizophrenia. Such a bias towards recessivity suggests that alleles that increase the risk of schizophrenia have been selected against over evolutionary time

Detecting autozygosity through runs of homozygosity: A comparison of three autozygosity detection algorithms

<p>Abstract</p> <p>Background</p> <p>A central aim for studying runs of homozygosity (ROHs) in genome-wide SNP data is to detect the effects of autozygosity (stretches of the two homologous chromosomes within the same individual that are identical by descent) on phenotypes. However, it is unknown which current ROH detection program, and which set of parameters within a given program, is optimal for differentiating ROHs that are truly autozygous from ROHs that are homozygous at the marker level but vary at unmeasured variants between the markers.</p> <p>Method</p> <p>We simulated 120 Mb of sequence data in order to know the true state of autozygosity. We then extracted common variants from this sequence to mimic the properties of SNP platforms and performed ROH analyses using three popular ROH detection programs, PLINK, GERMLINE, and BEAGLE. We varied detection thresholds for each program (e.g., prior probabilities, lengths of ROHs) to understand their effects on detecting known autozygosity.</p> <p>Results</p> <p>Within the optimal thresholds for each program, PLINK outperformed GERMLINE and BEAGLE in detecting autozygosity from distant common ancestors. PLINK's sliding window algorithm worked best when using SNP data pruned for linkage disequilibrium (LD).</p> <p>Conclusion</p> <p>Our results provide both general and specific recommendations for maximizing autozygosity detection in genome-wide SNP data, and should apply equally well to research on whole-genome autozygosity burden or to research on whether specific autozygous regions are predictive using association mapping methods.</p

Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk

<p>Abstract</p> <p>Background</p> <p>Traditional genome-wide association studies are generally limited in their ability explain a large portion of genetic risk for most common diseases. We sought to use both traditional GWAS methods, as well as more recently developed polygenic genome-wide analysis techniques to identify subsets of single-nucleotide polymorphisms (SNPs) that may be involved in risk of cardiovascular disease, as well as estimate the heritability explained by common SNPs.</p> <p>Methods</p> <p>Using data from the Framingham SNP Health Association Resource (SHARe), three complimentary methods were applied to examine the genetic factors associated with the Framingham Risk Score, a widely accepted indicator of underlying cardiovascular disease risk. The first method adopted a traditional GWAS approach - independently testing each SNP for association with the Framingham Risk Score. The second two approaches involved polygenic methods with the intention of providing estimates of aggregate genetic risk and heritability.</p> <p>Results</p> <p>While no SNPs were independently associated with the Framingham Risk Score based on the results of the traditional GWAS analysis, we were able to identify cardiovascular disease-related SNPs as reported by previous studies. A predictive polygenic analysis was only able to explain approximately 1% of the genetic variance when predicting the 10-year risk of general cardiovascular disease. However, 20% to 30% of the variation in the Framingham Risk Score was explained using a recently developed method that considers the joint effect of all SNPs simultaneously.</p> <p>Conclusion</p> <p>The results of this study imply that common SNPs explain a large amount of the variation in the Framingham Risk Score and suggest that future, better-powered genome-wide association studies, possibly informed by knowledge of gene-pathways, will uncover more risk variants that will help to elucidate the genetic architecture of cardiovascular disease.</p

c-Type Cytochrome-Dependent Formation of U(IV) Nanoparticles by Shewanella oneidensis

Modern approaches for bioremediation of radionuclide contaminated environments are based on the ability of microorganisms to effectively catalyze changes in the oxidation states of metals that in turn influence their solubility. Although microbial metal reduction has been identified as an effective means for immobilizing highly-soluble uranium(VI) complexes in situ, the biomolecular mechanisms of U(VI) reduction are not well understood. Here, we show that c-type cytochromes of a dissimilatory metal-reducing bacterium, Shewanella oneidensis MR-1, are essential for the reduction of U(VI) and formation of extracelluar UO (2) nanoparticles. In particular, the outer membrane (OM) decaheme cytochrome MtrC (metal reduction), previously implicated in Mn(IV) and Fe(III) reduction, directly transferred electrons to U(VI). Additionally, deletions of mtrC and/or omcA significantly affected the in vivo U(VI) reduction rate relative to wild-type MR-1. Similar to the wild-type, the mutants accumulated UO (2) nanoparticles extracellularly to high densities in association with an extracellular polymeric substance (EPS). In wild-type cells, this UO (2)-EPS matrix exhibited glycocalyx-like properties and contained multiple elements of the OM, polysaccharide, and heme-containing proteins. Using a novel combination of methods including synchrotron-based X-ray fluorescence microscopy and high-resolution immune-electron microscopy, we demonstrate a close association of the extracellular UO (2) nanoparticles with MtrC and OmcA (outer membrane cytochrome). This is the first study to our knowledge to directly localize the OM-associated cytochromes with EPS, which contains biogenic UO (2) nanoparticles. In the environment, such association of UO (2) nanoparticles with biopolymers may exert a strong influence on subsequent behavior including susceptibility to oxidation by O (2) or transport in soils and sediments

The effects of warming on the ecophysiology of two co-existing kelp species with contrasting distributions

The northeast Atlantic has warmed significantly since the early 1980s, leading to shifts in species distributions and changes in the structure and functioning of communities and ecosystems. This study investigated the effects of increased temperature on two co-existing habitat-forming kelps: Laminaria digitata, a northern boreal species, and Laminaria ochroleuca, a southern Lusitanian species, to shed light on mechanisms underpinning responses of trailing and leading edge populations to warming. Kelp sporophytes collected from southwest United Kingdom were maintained under 3 treatments: ambient temperature (12 °C), +3 °C (15 °C) and +6 °C (18 °C) for 16 days. At higher temperatures, L. digitata showed a decline in growth rates and Fv/Fm, an increase in chemical defence production and a decrease in palatability. In contrast, L. ochroleuca demonstrated superior growth and photosynthesis at temperatures higher than current ambient levels, and was more heavily grazed. Whilst the observed decreased palatability of L. digitata held at higher temperatures could reduce top-down pressure on marginal populations, field observations of grazer densities suggest that this may be unimportant within the study system. Overall, our study suggests that shifts in trailing edge populations will be primarily driven by ecophysiological responses to high temperatures experienced during current and predicted thermal maxima, and although compensatory mechanisms may reduce top-down pressure on marginal populations, this is unlikely to be important within the current biogeographical context. Better understanding of the mechanisms underpinning climate-driven range shifts is important for habitat-forming species like kelps, which provide organic matter, create biogenic structure and alter environmental conditions for associated communities

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Polygenic Analysis of Genome‐Wide SNP Data

One of the central motivators behind genetic research is to understand how genetic variation relates to human health and disease. Recently, there has been a large-scale effort to find common genetic variants associated with many forms of disease and disorder using single nucleotide polymorphisms (SNPs). Several genome-wide association (GWAS) studies have successfully identified SNPs associated with phenotypes. However, the effect sizes attributed to individual variants is generally small, explaining only a very small amount of the genetic risk and heritability expected based on the estimates of family and twin studies. Several explanations exist for the inability of GWAS to find the \u22missing heritability.\u22 The results of recent research appear to confirm the prediction made by population genetics theory that most complex phenotypes are highly polygenic, occasionally influenced by a few alleles of relatively large effect, and usually by several of small effect. Studies have also confirmed that common variants are only part of what contributes to the total genetic variance for most traits, indicating rare-variants may play a significant role. This research addresses some of the most glaring weaknesses of the traditional GWAS approach through the application of methods of polygenic analysis. We apply several methods, including those that investigate the net effects of large sets of SNPs, more sophisticated approaches informed by biology rather than the purely statistical approach of GWAS, as well as methods that infer the effects of recessive rare variants. Our results indicate that traditional GWAS is well complemented and improved upon by methods of polygenic analysis. We demonstrate that polygenic approaches can be used to significantly predict individual risk for disease, provide an unbiased estimate of a substantial proportion of the heritability for multiple phenotypes, identify sets of genes grouped into biological pathways that are enriched for associations, and finally, detect the significant influence of recessive rare variants

Whole-genome pathway analysis on 132,497 individuals identifies novel gene-sets associated with body mass index.

Whole genome pathway analysis is a powerful tool for the exploration of the combined effects of gene-sets within biological pathways. This study applied Interval Based Enrichment Analysis (INRICH) to perform whole-genome pathway analysis of body-mass index (BMI). We used a discovery set composed of summary statistics from a meta-analysis of 123,865 subjects performed by the GIANT Consortium, and an independent sample of 8,632 subjects to assess replication of significant pathways. We examined SNPs within nominally significant pathways using linear mixed models to estimate their contribution to overall BMI heritability. Six pathways replicated as having significant enrichment for association after correcting for multiple testing, including the previously unknown relationships between BMI and the Reactome regulation of ornithine decarboxylase pathway, the KEGG lysosome pathway, and the Reactome stabilization of P53 pathway. Two non-overlapping sets of genes emerged from the six significant pathways. The clustering of shared genes based on previously identified protein-protein interactions listed in PubMed and OMIM supported the relatively independent biological effects of these two gene-sets. We estimate that the SNPs located in examined pathways explain ∼20% of the heritability for BMI that is tagged by common SNPs (3.35% of the 16.93% total)

Pathways with significant enrichment for associations in the replication set after correcting for multiple testing.

<p>Pathways with novel detected enrichment have a * next to the name and contain no genes listed in the far right column.</p