Shared genetic effect of kidney function on bipolar and major depressive disorders: a large-scale genome-wide cross-trait analysis.

BACKGROUND: Epidemiological studies have revealed a significant association between impaired kidney function and certain mental disorders, particularly bipolar disorder (BIP) and major depressive disorder (MDD). However, the evidence regarding shared genetics and causality is limited due to residual confounding and reverse causation. METHODS: In this study, we conducted a large-scale genome-wide cross-trait association study to investigate the genetic overlap between 5 kidney function biomarkers (eGFRcrea, eGFRcys, blood urea nitrogen (BUN), serum urate, and UACR) and 2 mental disorders (MDD, BIP). Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Psychiatric Genomics Consortium. RESULTS: Using LD score regression, we found moderate but significant genetic correlations between kidney function biomarker traits on BIP and MDD. Cross-trait meta-analysis identified 1 to 19 independent significant loci that were found shared among 10 pairs of 5 kidney function biomarkers traits and 2 mental disorders. Among them, 3 novel genes: SUFU, IBSP, and PTPRJ, were also identified in transcriptome-wide association study analysis (TWAS), most of which were observed in the nervous and digestive systems (FDR < 0.05). Pathway analysis showed the immune system could play a role between kidney function biomarkers and mental disorders. Bidirectional mendelian randomization analysis suggested a potential causal relationship of kidney function biomarkers on BIP and MDD. CONCLUSIONS: In conclusion, the study demonstrated that both BIP and MDD shared genetic architecture with kidney function biomarkers, providing new insights into their genetic architectures and suggesting that larger GWASs are warranted.

Exploring the Shared Genetic Architecture Between Obstructive Sleep Apnea and Body Mass Index.

Purpose: The reciprocal comorbidity of obstructive sleep apnea (OSA) and body mass index (BMI) has been observed, yet the shared genetic architecture between them remains unclear. This study aimed to explore the genetic overlaps between them. Methods: Summary statistics were acquired from the genome-wide association studies (GWASs) on OSA (Ncase = 41,704; Ncontrol = 335,573) and BMI (Noverall = 461,460). A comprehensive genome-wide cross-trait analysis was performed to quantify global and local genetic correlation, infer the bidirectional causal relationships, detect independent pleiotropic loci, and investigate potential comorbid genes. Results: A positive significant global genetic correlation between OSA and BMI was observed (r g = 0.52, P = 2.85e-122), which was supported by three local signal. The Mendelian randomization analysis confirmed bidirectional causal associations. In the meta-analysis of cross-traits GWAS, a total of 151 single-nucleotide polymorphisms were found to be pleiotropic between OSA and BMI. Additionally, we discovered that the genetic association between OSA and BMI is concentrated in 12 brain regions. Finally, a total 134 expression-tissue pairs were observed to have a significant impact on both OSA and BMI within the specified brain regions. Conclusion: Our comprehensive genome-wide cross-trait analysis indicates a shared genetic architecture between OSA and BMI, offering new perspectives on the possible mechanisms involved.

A multi-trait GWAS identifies novel genes influencing albuminuria.

BACKGROUND: Albuminuria is common and associated with increased risks of end-stage kidney disease and cardiovascular diseases, yet its underlying mechanism remains obscure. Previous genome-wide association studies (GWAS) for albuminuria did not consider gene pleiotropy and primarily focused on European ancestry populations. This study adopted a multi-trait analysis of GWAS (MTAG) approach to jointly analyze two vital kidney traits, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) to identify and prioritize the genes associated with UACR. METHODS: Data from the Taiwan Biobank from 2012 to 2023 were analyzed. GWAS of UACR and eGFR were performed separately and the summary statistics from these GWAS were jointly analyzed using MTAG. The polygenic risk scores (PRS) of UACR were constructed for validation. The UACR-associated loci were further fine-mapped and prioritized based on their deleteriousness, eQTL associations, and relatedness to Mendelian kidney diseases. RESULTS: MTAG analysis of the UACR revealed 15 genetic loci, including 12 novel loci. The PRS for UACR was significantly associated with urinary albumin level (P < 0.001) and microalbuminuria (P = 0.001 ∼ 0.045). A list of priority genes was generated. Twelve genes with high priority included the albumin endocytic receptor gene LRP2 and ciliary genes  IFT172. CONCLUSIONS: The findings of this multi-trait GWAS suggest that primary cilia play a role in sensing mechanical stimuli, leading to albumin endocytosis. The priority list of genes warrants further translational investigation to reduce albuminuria.

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.

Independent association of general and central adiposity with risk of gallstone disease: observational and genetic analyses.

Background: General obesity is a well-established risk factor for gallstone disease (GSD), but whether central obesity contributes additional independent risk remains controversial. We aimed to comprehensively clarify the effect of body fat distribution on GSD. Methods: We first investigated the observational association of central adiposity, characterized by waist-to-hip ratio (WHR), with GSD risk using data from UK Biobank (N=472,050). We then explored the genetic relationship using summary statistics from the largest genome-wide association study of GSD (ncase=43,639, ncontrol=506,798) as well as WHR, with and without adjusting for body mass index (BMI) (WHR: n=697,734; WHRadjBMI: n=694,649). Results: Observational analysis demonstrated an increased risk of GSD with one unit increase in WHR (HR=1.18, 95%CI=1.14-1.21). A positive WHR-GSD genetic correlation (rg =0.41, P=1.42×10-52) was observed, driven by yet independent of BMI (WHRadjBMI: rg =0.19, P=6.89×10-16). Cross-trait meta-analysis identified four novel pleiotropic loci underlying WHR and GSD with biological mechanisms outside of BMI. Mendelian randomization confirmed a robust WHR-GSD causal relationship (OR=1.50, 95%CI=1.35-1.65) which attenuated yet remained significant after adjusting for BMI (OR=1.17, 95%CI=1.09-1.26). Furthermore, observational analysis confirmed a positive association between general obesity and GSD, corroborated by a shared genetic basis (rg =0.40, P=2.16×10-43), multiple novel pleiotropic loci (N=11) and a causal relationship (OR=1.67, 95%CI=1.56-1.78). Conclusion: Both observational and genetic analyses consistently provide evidence on an association of central obesity with an increased risk of GSD, independent of general obesity. Our work highlights the need of considering both general and central obesity in the clinical management of GSD.

A genome-wide cross-trait analysis identifies shared loci and causal relationships of obesity and lipidemic traits with psoriasis.

Background: Obesity and dyslipidemia, major global health concerns, have been linked to psoriasis, but previous studies faced methodological limitations and their shared genetic basis remains unclear. This study examines various obesity-related and lipidemic traits as potential contributors to psoriasis development, aiming to clarify their genetic associations and potential causal links. Methods: Summary statistics from genome-wide association studies (GWAS) conducted for obesity-related traits (body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-hip ratio adjusted for the body mass index (WHRadjBMI)) and lipidemic traits (high-density lipoprotein (HDL), LDL, triglyceride (TG), total Cholesterol (TC), apolipoprotein A1 (apoA1), apolipoprotein B (apoB), and apolipoprotein E (apoE)) and psoriasis, all in populations of European ancestry, were used. We quantified genetic correlations, identified shared loci and explored causal relationship across traits. Results: We found positive genetic correlation between BMI and psoriasis (rg=0.22, p=2.44×10-18), and between WHR and psoriasis (rg=0.19, p=1.41×10-12). We further found the positive genetic correlation between psoriasis and WHRadjBMI(rg=0.07, p=1.81×10-2) the genetic correlation, in while the effect of BMI was controlled for. We identified 14 shared loci underlying psoriasis and obesity-related traits and 43 shared loci between psoriasis and lipidemic traits via cross-trait meta-analysis. Mendelian randomization (MR) supported the causal roles of BMI (IVW OR=1.483, 95%CI=1.333-1.649), WHR (IVW OR=1.393, 95%CI=1.207-1.608) and WHRadjBMI (IVW OR=1.18, 95%CI=1.047-1.329) in psoriasis, but not observe any significant association between lipidemic traits and the risk of psoriasis. Genetic predisposition to psoriasis did not appear to affect the risk of obesity and lipidemic traits. Conclusions: An intrinsic link between obesity-related traits and psoriasis has been demonstrated. The genetic correlation and causal role of obesity-related traits in psoriasis highlight the significance of weight management in both the prevention and treatment of this condition.

recolorize: An R package for flexible colour segmentation of biological images.

Colour pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing colour pattern geometries across images requires colour segmentation, where pixels in an image are assigned to one of a set of colour classes shared by all images. Manual methods for colour segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human-subjective colour segmentation with functions for batch-processing low-variation image sets and additional tools for handling images from diverse (high-variation) sources. The package also includes export options for a variety of formats and colour analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output.

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models.

Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.

Multi-trait analysis of gene-by-environment interactions in large-scale genetic studies.

Identifying genotype-by-environment interaction (GEI) is challenging because the GEI analysis generally has low power. Large-scale consortium-based studies are ultimately needed to achieve adequate power for identifying GEI. We introduce Multi-Trait Analysis of Gene-Environment Interactions (MTAGEI), a powerful, robust, and computationally efficient framework to test gene-environment interactions on multiple traits in large data sets, such as the UK Biobank (UKB). To facilitate the meta-analysis of GEI studies in a consortium, MTAGEI efficiently generates summary statistics of genetic associations for multiple traits under different environmental conditions and integrates the summary statistics for GEI analysis. MTAGEI enhances the power of GEI analysis by aggregating GEI signals across multiple traits and variants that would otherwise be difficult to detect individually. MTAGEI achieves robustness by combining complementary tests under a wide spectrum of genetic architectures. We demonstrate the advantages of MTAGEI over existing single-trait-based GEI tests through extensive simulation studies and the analysis of the whole exome sequencing data from the UKB.

Environmental heterogeneity of cold seep by biological trait analysis of marine nematodes at Site F cold seep in South China Sea.

Cold seeps provide high environmental heterogeneity for marine benthos. Site F is one of the active cold seeps in the South China Sea. In this study, free-living marine nematode communities were investigated at Site F and the adjacent deep-sea area. A total of 67 genera and 32 families were identified. The mean density at cold seep sites ranged from 13.6 to 181.8 ind./10 cm2, and that at the adjacent deep-sea sites ranged from 36.9 to 301.4 ind./10 cm2. At cold seep sites, the most dominant nematode genera were Desmoscolex, Pierrickia, Sabatieria, Halalaimus, and Dorylaimopsis while at deep-sea sites, the most dominant genera were Retrotheristus, Thalassomonhystera, Desmoscolex, Cobbia, and Halalaimus. Deposit feeders of nematodes were dominant at all sites. Results of biological trait analysis showed that there was high environmental heterogeneity for nematodes at Site F. Water depth, sediment organic matter content, and sand proportion had important influences on nematode communities.

Understanding the relationship between circulating lipids and risk of chronic kidney disease: a prospective cohort study and large-scale genetic analyses.

BACKGROUND: This study aims to comprehensively investigate the phenotypic and genetic relationships between four common lipids (high-density lipoprotein cholesterol, HDL-C; low-density lipoprotein cholesterol, LDL-C; total cholesterol, TC; and triglycerides, TG), chronic kidney disease (CKD), and estimated glomerular filtration rate (eGFR). METHODS: We first investigated the observational association of lipids (exposures) with CKD (primary outcome) and eGFR (secondary outcome) using data from UK Biobank. We then explored the genetic relationship using summary statistics from the largest genome-wide association study of four lipids (N = 1,320,016), CKD (Ncase = 41,395, Ncontrol = 439,303), and eGFR(N = 567,460). RESULTS: There were significant phenotypic associations (HDL-C: hazard ratio (HR) = 0.76, 95%CI = 0.60-0.95; TG: HR = 1.08, 95%CI = 1.02-1.13) and global genetic correlations (HDL-C: [Formula: see text] = - 0.132, P = 1.00 × 10-4; TG: [Formula: see text] = 0.176; P = 2.66 × 10-5) between HDL-C, TG, and CKD risk. Partitioning the whole genome into 2353 LD-independent regions, twelve significant regions were observed for four lipids and CKD. The shared genetic basis was largely explained by 29 pleiotropic loci and 36 shared gene-tissue pairs. Mendelian randomization revealed an independent causal relationship of genetically predicted HDL-C (odds ratio = 0.91, 95%CI = 0.85-0.98), but not for LDL-C, TC, or TG, with the risk of CKD. Regarding eGFR, a similar pattern of correlation and pleiotropy was observed. CONCLUSIONS: Our work demonstrates a putative causal role of HDL-C in CKD and a significant biological pleiotropy underlying lipids and CKD in populations of European ancestry. Management of low HDL-C levels could potentially benefit in reducing the long-term risk of CKD.

A genome-wide cross-trait analysis identifies genomic correlation, pleiotropic loci, and causal relationship between sex hormone-binding globulin and rheumatoid arthritis.

BACKGROUND: Our study aims to investigate an intrinsic link underlying sex hormone-binding globulin (SHBG) and rheumatoid arthritis (RA), which remains inconclusive in observational settings. METHODS: Summary statistics were collected from the largest GWAS(s) on SHBG adjusted for BMI (SHBGadjBMI; Noverall = 368,929; Nmen = 180,094; Nwomen = 188,908), crude SHBG (Noverall = 370,125; Nmen = 180,726; Nwomen = 189,473), and RA (Ncase = 22,350; Ncontrol = 74,823). A genome-wide cross-trait design was performed to quantify global and local genetic correlation, identify pleiotropic loci, and infer a causal relationship. RESULTS: Among the overall population, a significant global genetic correlation was observed for SHBGadjBMI and RA ([Formula: see text] = 0.11, P = 1.0 × 10-4) which was further supported by local signal (1q25.2). A total of 18 independent pleiotropic SNPs were identified, of which three were highly likely causal variants and four were found to have effects on both traits through gene expression mediation. A putative causal association of SHBGadjBMI on RA was demonstrated (OR = 1.20, 95% CI = 1.01-1.43) without evidence of reverse causality (OR = 0.999, 95% CI = 0.997-1.000). Sex-specific analyses revealed distinct shared genetic regions (men: 1q32.1-q32.2 and 5p13.1; women: 1q25.2 and 22q11.21-q11.22) and diverse pleiotropic SNPs (16 in men and 18 in women, nearly half were sex-specific) underlying SHBGadjBMI and RA, demonstrating biological disparities between sexes. Replacing SHBGadjBMI with crude SHBG, a largely similar yet less significant pattern of results was observed. CONCLUSION: Our cross-trait analysis suggests an intrinsic, as well as a sex-specific, link underlying SHBG and RA, providing novel insights into disease etiology.

Deciphering benthic ecosystem functioning and resilience in a major port and marine protected area via the multi-trait approach.

The preservation of ecosystem functioning of coastal zones, in face of increasing environmental stressors and species extinctions, relies on the functional redundancy and inherent resilience of its inhabitants. To compare the benthic functioning and resilience of a disturbed area with a relatively less impacted area, a study was conducted in Mumbai Port and Malvan Marine Protected Area (MPA), which exhibited contrasting characteristics. The hypothesis posited that the anthropogenically influenced Mumbai port would exhibit lower functional parameters and resilience compared to Malvan. Overall, the MPA presented higher species richness and functional diversity with a greater presence of sensitive species, while Mumbai was dominated by the presence of opportunistic species, as anticipated. However, our findings demonstrated that despite varied trends in species diversity metrics, in both the coastal areas, the resemblance in benthic functioning was high due to similarity in dominant trait profiles. Surprisingly, Functional Richness was higher at Mumbai, while Functional Evenness, Divergence and Dispersion were comparable at both sites. The resilience, as quantified by Functional Redundancy, was also comparable at both areas attributable to the presence of clusters of species with similar traits and a low occurrence of rare traits. The combination of traits observed in both areas was influenced by the extant environmental conditions, as revealed by RLQ analyses. This study underscores the valuable insights provided by the application of Biological Trait Analysis (BTA) tool in deciphering the relationship between species diversity and ecosystem functioning, as well as the resilience capabilities of ecosystems subjected to varying levels of perturbation. Moreover, the incorporation of functional diversity indices yielded valuable inferences regarding ecosystems resilience, which can aid future ecosystem management strategies.

A shared genetic contribution to osteoarthritis and COVID-19 outcomes: a large-scale genome-wide cross-trait analysis.

Background: Patients with osteoarthritis (OA) are exposed to an increased risk of adverse outcomes of COVID-19, and they tend to experience disruption in access to healthcare services and exercise facilities. However, a deep understanding of this comorbidity phenomenon and the underlying genetic architecture of the two diseases is still unclear. In this study, we aimed to untangle the relationship between OA and COVID-19 outcomes by conducting a large-scale genome-wide cross-trait analysis. Methods: Genetic correlation and causal relationships between OA and COVID-19 outcomes (critical COVID-19, COVID-19 hospitalization, and COVID-19 infection) were estimated by linkage disequilibrium score regression and Mendelian Randomization approaches. We further applied Multi-Trait Analysis of GWAS and colocalization analysis to identify putative functional genes associated with both OA and COVID-19 outcomes. Results: Significant positive genetic correlations between OA susceptibility and both critical COVID-19 (rg=0.266, P=0.0097) and COVID-19 hospitalization (rg=0.361, P=0.0006) were detected. However, there was no evidence to support causal genetic relationships between OA and critical COVID-19 (OR=1.17[1.00-1.36], P=0.049) or OA and COVID-19 hospitalization OR=1.08[0.97-1.20], P=0.143). These results were robustly consistent after the removal of obesity-related single nucleotide polymorphisms (SNPs). Moreover, we identified a strong association signal located near the FYCO1 gene (lead SNPs: rs71325101 for critical COVID-19, Pmeta=1.02×10-34; rs13079478 for COVID-19 hospitalization, Pmeta=1.09×10-25). Conclusion: Our findings further confirmed the comorbidity of OA and COVID-19 severity, but indicate a non-causal impact of OA on COVID-19 outcomes. The study offers an instructive perspective that OA patients did not generate negative COVID-19 outcomes during the pandemic in a causal way. Further clinical guidance can be formulated to enhance the quality of self-management in vulnerable OA patients.

Responses of biological traits of macrobenthic fauna to a eutrophication gradient in a semi-enclosed bay, China.

The impacts of eutrophication on benthic ecological functions are of increasing concern in recent years. In order to assess the response of macrobenthic fauna to increasing eutrophication, two field sampling surveys were conducted during the summer (July-August 2020) and autumn (October-November 2020) from offshore, nearshore to estuarine sediments in Bohai Bay, northern China. Biological trait analysis was employed for the assessment of macrofaunal samples. The results indicated that there was an increase in the proportion of benthic burrowering or tube-dwelling sediment feeders and taxa with higher larval dispersal ability, but a decrease in the proportion of taxa showing high motility in areas with higher nutrient levels. Seasonal differences were also noted in the shift in biological traits, with a significantly lower similarity among the sampling areas in summer and a higher proportion of carnivorous taxa in autumn. The findings suggested that long-term disturbance can lead to the dominance of smaller body-sized benthic species and reduced sediment quality, impeding ecological recovery of benthic organisms under such harsh environment.

Characterization of direct and/or indirect genetic associations for multiple traits in longitudinal studies of disease progression.

When quantitative longitudinal traits are risk factors for disease progression and subject to random biological variation, joint model analysis of time-to-event and longitudinal traits can effectively identify direct and/or indirect genetic association of single nucleotide polymorphisms (SNPs) with time-to-event. We present a joint model that integrates: (1) a multivariate linear mixed model describing trajectories of multiple longitudinal traits as a function of time, SNP effects, and subject-specific random effects and (2) a frailty Cox survival model that depends on SNPs, longitudinal trajectory effects, and subject-specific frailty accounting for dependence among multiple time-to-event traits. Motivated by complex genetic architecture of type 1 diabetes complications (T1DC) observed in the Diabetes Control and Complications Trial (DCCT), we implement a 2-stage approach to inference with bootstrap joint covariance estimation and develop a hypothesis testing procedure to classify direct and/or indirect SNP association with each time-to-event trait. By realistic simulation study, we show that joint modeling of 2 time-to-T1DC (retinopathy and nephropathy) and 2 longitudinal risk factors (HbA1c and systolic blood pressure) reduces estimation bias in genetic effects and improves classification accuracy of direct and/or indirect SNP associations, compared to methods that ignore within-subject risk factor variability and dependence among longitudinal and time-to-event traits. Through DCCT data analysis, we demonstrate feasibility for candidate SNP modeling and quantify effects of sample size and Winner's curse bias on classification for 2 SNPs identified as having indirect associations with time-to-T1DC traits. Joint analysis of multiple longitudinal and multiple time-to-event traits provides insight into complex traits architecture.

An integrated leaf trait analysis of two Paleogene leaf floras.

Objectives: This study presents the Integrated Leaf Trait Analysis (ILTA), a workflow for the combined application of methodologies in leaf trait and insect herbivory analyses on fossil dicot leaf assemblages. The objectives were (1) to record the leaf morphological variability, (2) to describe the herbivory pattern on fossil leaves, (3) to explore relations between leaf morphological trait combination types (TCTs), quantitative leaf traits, and other plant characteristics (e.g., phenology), and (4) to explore relations of leaf traits and insect herbivory. Material and Methods: The leaves of the early Oligocene floras Seifhennersdorf (Saxony, Germany) and Suletice-Berand (Ústí nad Labem Region, Czech Republic) were analyzed. The TCT approach was used to record the leaf morphological patterns. Metrics based on damage types on leaves were used to describe the kind and extent of insect herbivory. The leaf assemblages were characterized quantitatively (e.g., leaf area and leaf mass per area (LMA)) based on subsamples of 400 leaves per site. Multivariate analyses were performed to explore trait variations. Results: In Seifhennersdorf, toothed leaves of TCT F from deciduous fossil-species are most frequent. The flora of Suletice-Berand is dominated by evergreen fossil-species, which is reflected by the occurrence of toothed and untoothed leaves with closed secondary venation types (TCTs A or E). Significant differences are observed for mean leaf area and LMA, with larger leaves tending to lower LMA in Seifhennersdorf and smaller leaves tending to higher LMA in Suletice-Berand. The frequency and richness of damage types are significantly higher in Suletice-Berand than in Seifhennersdorf. In Seifhennersdorf, the evidence of damage types is highest on deciduous fossil-species, whereas it is highest on evergreen fossil-species in Suletice-Berand. Overall, insect herbivory tends to be more frequently to occur on toothed leaves (TCTs E, F, and P) that are of low LMA. The frequency, richness, and occurrence of damage types vary among fossil-species with similar phenology and TCT. In general, they are highest on leaves of abundant fossil-species. Discussion: TCTs reflect the diversity and abundance of leaf architectural types of fossil floras. Differences in TCT proportions and quantitative leaf traits may be consistent with local variations in the proportion of broad-leaved deciduous and evergreen elements in the ecotonal vegetation of the early Oligocene. A correlation between leaf size, LMA, and fossil-species indicates that trait variations are partly dependent on the taxonomic composition. Leaf morphology or TCTs itself cannot explain the difference in insect herbivory on leaves. It is a more complex relationship where leaf morphology, LMA, phenology, and taxonomic affiliation are crucial.

Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis.

BACKGROUND: Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. METHODS: We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and mendelian randomization also provided to research the relationship between type 2 diabetes and psychiatric disorders. RESULTS: We discovered that type 2 diabetes and psychiatric disorders had a significant correlation. We identified 138 related loci, 32 were novel loci. Post-GWAS analysis revealed that 86 differentially expressed genes were located in different brain regions and peripheral blood in type 2 diabetes and related psychiatric disorders. MAPK signaling pathway plays an important role in neural development and insulin signaling. In addition, there is a causal relationship between T2D and mental disorders. In PPI analysis, the central genes of the DEG PPI network were FTO and TCF7L2. CONCLUSION: This large-scale genome-wide cross-trait analysis identified shared genetics andpotential causal links between type 2 diabetes and related psychiatric disorders, suggesting potential new biological functions in common among them.

Stability of symptoms of psychological distress: A 13-year study using Australian national data.

This study examined the stability of 10 symptoms of psychological distress measured by the Kessler Psychological Distress Scale (K10) in an Australian adult sample. Data were collected at seven time points, 2 years apart, between 2007 and 2019. The average stability of the 10 symptoms was 49%. However, there were differences between items. The items can be categorised based on two characteristics: Degree of extremity and arousal. More extreme symptoms (i.e., those beginning with 'so', such as 'so sad that...') tended to change more over time, whereas milder symptoms (e.g., "nervous") tended to be more stable. Symptoms with low levels of arousal (e.g., 'hopeless') tended to reflect more situation-specific influences, whereas symptoms with high levels of arousal (e.g., 'restless or fidgety') tended to reflect more nonsystematic influences.

Taxonomic and functional structure of macrobenthic invertebrate communities and their response to environmental variables along the subbranches of the Nile River (rayahs), Egypt.

Macrobenthic invertebrate communities serve as markers of anthropogenic stress in freshwater ecosystems. In this study, 17 sampling sites were selected from two Nile river subbranches (El-Rayah El-Behery and El-Rayah El-Nassery) and subjected to different anthropogenic influences to explore the ecological environment and characteristics of macrobenthos communities. Macrobenthos were studied using taxonomic diversity and biological trait analysis to investigate how human activity and variation in water quality affect their structure and function. A total of 37 taxa represented by 43,389 individuals were recognized. The communities are composed chiefly of Oligochaeta and aquatic insects. Multivariate statistical analyses found that the most influential environmental variables in the structural and functional community were sodium, dissolved oxygen, silicate, pH, calcium, and cadmium. At high levels of pollution, notably sewage and industrial pollution in the northern part of El-Rayah El-Behery, characteristics such as larger body size, detritus feeders, burrowers, and high tolerance to pollution predominated, whereas at low levels of pollution, features such as small body sizes, scraper and predator feeders, intolerant and fairly tolerant of pollution, and climber and swimmer mobility are predominant. The results confirm our prediction that the distribution of macroinvertebrate traits varies spatially in response to environmental changes. The diversity-based method distinguished impacted sewage and industrial sites from thermal effluent sites, while the trait-based approach illustrated an apparent variance between the ecological status of contaminated regions. Therefore, the biological features should be employed in addition to structural aspects for assessing the biodiversity of macroinvertebrate communities under environmental stressors.