Gene-environment Interactions Research Articles

Cohort profile: COBLAnCE: a French prospective cohort to study prognostic and predictive factors in bladder cancer and to generate real-world data on treatment patterns, resource use and quality of life

PurposeBladder cancer is a complex disease with a wide range of outcomes. Clinicopathological factors only partially explain the variability between patients in prognosis and treatment response. There is a need...

Lifestyle for Brain Health Score, Genetic Risk and Incidence of Dementia

AbstractBackgroundAssessing whether modifiable factors are associated with dementia risk regardless of genetic risk for the disease is primordial for precision prevention. So far, studies on gene‐by‐environment interactions (GxE) in dementia have been controversial. We leveraged a large cohort of older persons to investigate the association between numerous modifiable factors, reflected by the lifestyle for brain‐health (LIBRA) score (defined in figure 1), and the incidence of dementia, evaluating effect modification by genetic risk.MethodWe included participants from the Three‐City (3C) study, free of dementia at baseline and followed for up to 17 years. The LIBRA score and a genetic risk score (GRS) based on the latest Alzheimer’s disease GWAS (excluding the apolipoprotein E {APOE} locus) were constructed. Illness‐death models for interval‐censored data were used to assess the relation of LIBRA, GRS and carriership of the e4 allele of the APOE gene (and their interactions) to the incidence of dementia, accounting for potential competing risk by death.ResultThe population included 5170 3C participants aged on average 73.8 years at baseline. Both genetic factors and LIBRA were significantly associated with dementia risk. In multivariable‐adjusted models, each one‐point increase in LIBRA was associated with a 10% higher risk of dementia (95% CI 1.07‐1.13). We found no evidence of GxE interaction (p≥0.2 for two‐order [LIBRA by each genetic variable] and three‐order interaction terms [LIBRA‐by‐GRS‐by‐APOE4]); a higher LIBRA was significantly associated with increased dementia risk in both APOE4 carriers and non‐carriers (figure 1) and in low, medium or high GRS level (figure 2), with similar magnitude of associations in all strata. Although power was limited to examine APOE4 carriers by GRS strata, the trend of association of LIBRA (continuous) to dementia risk was not meaningfully different from what found in other groups (figure 3).ConclusionIn this large cohort of older persons, increasing number of health and lifestyle risk factors for dementia was associated with higher dementia risk in all levels of genetic risk. These findings suggest prevention programs with targeted lifestyle modifications and health management may be efficient even in those with highest risk for dementia based on their genetic susceptibility.

Genetic Architecture of Cortical Thickness and White Matter hyperintensities: Evidence of Gene‐Environment Interaction with Cardiovascular Health and Late‐Life Depression

AbstractBackgroundModifiable risk factors account for over 40% of dementias, offering potential for intervention. However, the impact of these risk factors, including depression and vascular disease, on brain health in the context of variable genetic influences is not well understood. Here we map the genetic landscape of brain structure, testing for gene‐by‐environment (GxE) interactions and relevance to cognitive performance in mid‐ and late‐life.MethodWe conducted genome‐wide association studies (GWAS) to identify genetic variants associated with cortical thickness in over 34,500 UK Biobank participants. We used postmortem bulk RNA sequencing of frontal cortex paired with antemortem MRI in a late‐life sample (n = 66) to directly corroborate implicated GWAS loci. Focusing on GWAS‐significant loci, we tested for GxE interactions and associations with cognition in 25,254 Canadian Longitudinal Study of Ageing (CLSA) participants.ResultWe identified 367 loci in the UK Biobank participants (age 45‐81), associated with global or regional cortical thickness (p<5×10−8), with genetic correlations between pairs of anatomical regions identifying three distinct modules. Corroboration with postmortem bulk RNA sequencing and MRI in a late‐life sample confirmed effects of STMN4, ARL17A and ARL17B genes on cortical thickness. Ten GWAS‐implicated loci from the UK Biobank were also associated with executive function or memory in CLSA, including rs1562330, a locus linked to the STMN4 gene. Notably, 37 variants also showed significant interactions with cardiovascular health or depression (P<2×10−4).ConclusionThese findings advance our understanding of the genetic architecture of brain structure and show that modifiable factors alter the effects of heritable genetic background on brain and cognitive health.

Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review.

Rapid advancements in high-throughput biological techniques have facilitated the generation of high-dimensional omics datasets, which have provided a solid foundation for precision medicine and prognosis prediction. Nonetheless, the problem of missing heritability persists. To solve this problem, it is essential to explain the genetic structure of disease incidence risk and prognosis by incorporating interactions. The development of the Bayesian theory has provided new approaches for developing models for interaction identification and estimation. Several Bayesian models have been developed to improve the accuracy of model and identify the main effect, gene-environment (G×E) and gene-gene (G×G) interactions. Studies based on single-nucleotide polymorphisms (SNPs) are significant for the exploration of rare and common variants. Models based on the effect heredity principle and group-based models are relatively flexible and do not require strict constraints when dealing with the hierarchical structure between the main effect and interactions (M-I). These models have a good interpretability of biological mechanisms. Machine learning-based Bayesian approaches are highly competitive in improving prediction accuracy. These models provide insights into the mechanisms underlying the occurrence and progression of complex diseases, identify more reliable biomarkers, and develop higher predictive accuracy. In this paper, we provide a comprehensive review of these Bayesian approaches.

A PROSAIL model with a vegetation index lookup table optimized with in-situ statistics for rapeseed leaf area index estimation using diverse unmanned aerial vehicle sensors in the Yangtze River Basin

For winter rapeseed, timely monitoring of the leaf area index (LAI) near the winter solstice is critical for estimating its overwintering survival rate and yield. While unmanned aerial vehicle (UAV) remote sensing techniques have emerged as a promising means for crop LAI monitoring, many existing crop LAI models have only been evaluated using a single sensor system, a single cultivar, a single development stage, and/or a single site. As a result, the generalization capability of these LAI models is limited. In this study, four empirical statistical models (ESMs) and a new PROSAIL-based lookup table (LUT) method were developed to estimate the LAI of 24 winter rapeseed cultivars mainly grown in the Yangtze River Basin. The raw LUT was optimized using a vegetation index (VI) strategy after screening out inefficient parameter combinations using in-situ UAV reflectance and measured LAIs. The model transferability was evaluated across three years, six regional sites around Yangtze River Basin, different development stages, and two data sources from different sensors. An artificial neural network model in the ESMs performed best for data acquired in 2019 and 2020, but could not be used to estimate the LAI in 2018 due to the use of different sensors for data acquisition. In contrast, the optimized VI-LUT method was robust and outperformed the raw LUT for LAI retrieval with a root mean squared error of <0.7 for all data sets. These findings suggest that the optimized VI-LUT method has potential for estimating LAI at different spatial and temporal scales, especially for the situation where sensor types are difficult to be unified in multiple locations. Moreover, the genotype in these cultivars did not appear to dominate LAI generation before overwintering, but the climatic zone did. These findings have implications for the study of genotype-environment interactions in rapeseed cultivation.

Overview of Risk Factors for Esophageal Squamous Cell Carcinoma in China

(1) Background: China has the highest esophageal squamous cell carcinoma (ESCC) incidence areas in the world, with some areas of incidence over 100 per 100,000. Despite extensive public health efforts, its etiology is still poorly understood. This study aims to review and summarize past research into potential etiologic factors for ESCC in China. (2) Methods: Relevant observational and intervention studies were systematically extracted from four databases using key terms, reviewed using Rayyan software, and summarized into Excel tables. (3) Results: Among the 207 studies included in this review, 129 studies were focused on genetic etiologic factors, followed by 22 studies focused on dietary-related factors, 19 studies focused on HPV-related factors, and 37 studies focused on other factors. (4) Conclusions: ESCC in China involves a variety of factors including genetic variations, gene–environment interactions, dietary factors like alcohol, tobacco use, pickled vegetables, and salted meat, dietary behavior such as hot food/drink consumption, infections like HPV, poor oral health, gastric atrophy, and socioeconomic factors. Public health measures should prioritize genetic screening for relevant polymorphisms, conduct comprehensive investigations into environmental, dietary, and HPV influences, enhance oral health education, and consider socioeconomic factors overall as integral strategies to reduce ESCC in high-risk areas of China.

Association between cumulative maternal exposures related to inflammation and child attention-deficit/hyperactivity disorder: A cohort study.

Preclinical studies suggest synergistic effects of maternal inflammatory exposures on offspring neurodevelopment, but human studies have been limited. To examine the cumulative association and potential interactions between seven maternal exposures related to inflammation and child attention-deficit/hyperactivity disorder (ADHD). We conducted a population-based cohort study of children born from July 2001 to December 2011 in New South Wales, Australia, and followed up until December 2014. Seven maternal exposures were identified from birth data and hospital admissions during pregnancy: autoimmune disease, asthma, hospitalization for infection, mood or anxiety disorder, smoking, hypertension, and diabetes. Child ADHD was identified from stimulant prescription records. Multivariable Cox regression assessed the association between individual and cumulative exposures and ADHD and potential interaction between exposures, controlling for potential confounders. The cohort included 908,770 children, one-third (281,724) with one or more maternal exposures. ADHD was identified in 16,297 children (incidence 3.5 per 1000 person-years) with median age of 7 (interquartile range 2) years at first treatment. Each exposure was independently associated with ADHD, and risk increased with additional exposures: one exposure (hazard ratio (HR) 1.59, 95% confidence interval (CI) 1.54, 1.65), two exposures (HR 2.25, 95% CI 2.13, 2.37), and three or more exposures (HR 3.28, 95% CI 2.95, 3.64). Positive interaction was found between smoking and infection. The largest effect size was found for cumulative exposure of asthma, infection, mood or anxiety disorder, and smoking (HR 6.12, 95% CI 3.47, 10.70). This study identifies cumulative effects of multiple maternal exposures related to inflammation on ADHD, most potentially preventable or modifiable. Future studies should incorporate biomarkers of maternal inflammation and consider gene-environment interactions.

Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study.

(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.

Escherichia coli triggers α-synuclein pathology in the LRRK2 transgenic mouse model of PD

ABSTRACT Alpha-synuclein (α-syn) pathology is the hallmark of Parkinson‘s disease (PD). The leucine-rich repeat kinase 2 (LRRK2) gene is a major-effect risk gene for sporadic PD (sPD). However, what environmental factors may trigger the formation of α-syn pathology in carriers of LRRK2 risk variants are still unknown. Here, we report that a markedly increased abundance of Escherichia coli (E. coli) in the intestinal microbiota was detected in LRRK2 risk variant(R1628P or G2385R) carriers with sPD compared with carriers without sPD. Animal experiments showed that E. coli administration triggered pathological α-syn accumulation in the colon and spread to the brain via the gut-brain axis in Lrrk2 R1628P mice, due to the co-occurrence of Lrrk2 variant-induced inhibition of α-syn autophagic degradation and increased phosphorylation of α-syn caused by curli in E. coli-derived extracellular vesicles. Fecal microbiota transplantation (FMT) effectively ameliorated motor deficits and α-syn pathology in Lrrk2 R1628P mice. Our findings elaborate on the mechanism that E. coli triggers α-syn pathology in Lrrk2 R1628P mice, and highlight a novel gene-environment interaction pattern in LRRK2 risk variants. Even more importantly, the findings reveal the interplay between the specific risk gene and the matched environmental factors triggers the initiation of α-syn pathology in sPD.

Beyond the horizon: Innovations and future directions in axial-spondyloarthritis.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the spine and sacroiliac joints. This review discusses recent advances across multiple scientific fields that promise to transform axSpA management. Traditionally, axSpA was considered an immune-mediated disease driven by human leukocyte antigen B27 (HLA-B27), interleukin (IL)-23/IL-17 signaling, biomechanics, and dysbiosis. Diagnosis relies on clinical features, laboratory tests, and imaging, particularly magnetic resonance imaging (MRI) nowadays. Management includes exercise, lifestyle changes, non-steroidal anti-inflammatory drugs and if this is not sufficient to achieve disease control also biological and targeted-synthetic disease modifying anti-rheumatic drugs. Beyond long-recognized genetic risks like HLA-B27, high-throughput sequencing has revealed intricate gene-environment interactions influencing dysbiosis, immune dysfunction, and aberrant bone remodeling. Elucidating these mechanisms promises screening approaches to enable early intervention. Advanced imaging is revolutionizing the assessment of axSpA's hallmark: sacroiliac bone-marrow edema indicating inflammation. Novel magnetic resonance imaging (MRI) techniques sensitively quantify disease activity, while machine learning automates complex analysis to improve diagnostic accuracy and monitoring. Hybrid imaging like synthetic MRI/computed tomography (CT) visualizes structural damage with new clarity. Meanwhile, microbiome analysis has uncovered gut ecosystem alterations that may initiate joint inflammation through HLA-B27 misfolding or immune subversion. Correcting dysbiosis represents an enticing treatment target. Moving forward, emerging techniques must augment patient care. Incorporating patient perspectives will be key to ensure innovations like genetics, microbiome, and imaging biomarkers translate into improved mobility, reduced pain, and increased quality of life. By integrating cutting-edge, multidisciplinary science with patients' lived experience, researchers can unlock the full potential of new technologies to deliver transformative outcomes. The future is bright for precision diagnosis, tightly controlled treatment, and even prevention of axSpA.

Exposure to ambient air toxicants and the risk of amyotrophic lateral sclerosis (ALS): A matched case control study

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder with few risk factors identified and no known cure. Gene-environment interaction is hypothesized especially for sporadic ALS cases (90–95%) which are of unknown etiology. We aimed to investigate risk factors for ALS including exposure to ambient air toxics. MethodsThis population-based case-control study included 267 ALS cases (from the United States [U.S.] Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry National ALS Registry and Biorepository) and 267 age, sex, and county-matched controls identified via a commercial database. Exposure assessment for 34 ambient air toxicants was performed by assigning census tract-level U.S. Environmental Protection Agency (EPA) 2011 National Air Toxics Assessment (NATA) data to participants’ residential ZIP codes. Conditional logistic regression was used to compute adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for individual compounds, chemical classes, and overall exposure. Sensitivity analyses using both conditional logistic regression and Bayesian grouped weighted quartile sum (GWQS) models were performed to assess the integrity of findings. ResultsUsing the 2011 NATA, the highest exposure quartile (Q4) compared to the lowest (Q1) of vinyl chloride (aOR = 6.00, 95% CI: 1.87–19.25), 2,4-dinitrotoluene (aOR = 5.45, 95% CI: 1.53–19.36), cyanide (aOR = 4.34, 95% CI: 1.52–12.43), cadmium (aOR = 3.30, 95% CI: 1.11–9.77), and carbon disulfide (aOR = 2.98, 95% CI: 1.00–8.91) was associated with increased odds of ALS. Residential air selenium showed an inverse association with ALS (second quartile [Q2] vs. Q1: aOR = 0.38, 95% CI: 0.18–0.79). Additionally, residential exposure to organic/chlorinated solvents (Q4 vs Q1: aOR = 2.62, 95% CI: 1.003–6.85) was associated with ALS. ConclusionsOur findings using the 2011 NATA linked by census tract to residential area provide evidence of increased ALS risk in cases compared to controls for 2,4-dinitrotoluene, vinyl chloride, cyanide, and the organic/chlorinated solvents class. This underscores the importance of ongoing surveillance of potential exposures for at-risk populations.

Enviromic analysis for prediction of Eucalyptus globulus genotype-environment interaction and mapping of breeding zones

Genotype-environment interaction (GE) is pervasive in forest genetics and is a substantial cause of erosion in genetic gain. Delineation of spatial breeding zones (BZs) is fundamental for controlling GE. We developed a BZ classification pipeline for Eucalyptus globulus in 2 Australian regions based on phenotypic, genomic and pedigree data, as well as on a detailed environmental characterization (‘envirotyping’) and spatial mapping of BZs. First, the factor analytic method was used to model additive genetic variance and site-site correlations (rB) in stem volume across 48 trials of 126,467 full-sib progeny from 2 separate advanced-generation breeding programs. Single-step genomic BLUP (ssGBLUP) was used to create a joint relationship matrix combining both programs for factor analytic analysis. Thirty-three trials were envirotyped using 145 environmental variables (EVs), involving soil and landscape (71), climate (73), and management (1) EVs. Next, sparse partial least squares-discriminant analysis (sPLS-DA) was used to identify EVs that were required to predict classification of sites into 5 non-exclusive BZ classes on the basis of rB. These BZs were then spatially mapped across the West Australian (WA) and ‘Green Triangle’ (GT) commercial estates by enviromic prediction using EVs for 80 locations and 15 sets of observed climate data to represent temporal variation. Finally, stem volume breeding value accuracy and genetic gain from ssGBLUP across 40 trials were compared using the new enviromic BZ classification and the previous regional BZ classification. The FA model explained 85.9% of additive genetic variation. Our environmental classification system produced within-zone mean rB between 0.76 and 0.84, which improves upon the existing values of 0.62 for WA and 0.67 for GT as regional BZs. One to two sPLS-DA components were required for each BZ definition, with 5 to 8 EV’s per BZ. The enviromic BZ classification provided for more accurate breeding values and greater stem volume gain. This work raises highly relevant topics for discussion, including a) the drivers of GE in E. globulus, b) methods of collecting and curating environmental data for envirotyping with eucalypts, c) the advantages and challenges of adopting non-exclusive (i.e. overlapping) BZs, d) the need for developing a phenological system for eucalypts, e) accommodating temporal environmental variation in GE analysis, and f) breeding and improvement strategies for a changing climate.

The effect of disease misclassification on the ability to detect a gene-environment interaction: implications of the specificity of case definitions for research on Gulf War illness

BackgroundSince 1997, research on Gulf War illness (GWI) has predominantly used 3 case definitions—the original Research definition, the CDC definition, and modifications of the Kansas definition—but they have not been compared against an objective standard.MethodsAll 3 case definitions were measured in the U.S. Military Health Survey by a computer-assisted telephone interview in a random sample (n = 6,497) of the 1991 deployed U.S. military force. The interview asked whether participants had heard nerve agent alarms during the conflict. A random subsample (n = 1,698) provided DNA for genotyping the PON1 Q192R polymorphism.ResultsThe CDC and the Modified Kansas definition without exclusions were satisfied by 41.7% and 39.0% of the deployed force, respectively, and were highly overlapping. The Research definition, a subset of the others, was satisfied by 13.6%. The majority of veterans meeting CDC and Modified Kansas endorsed fewer and milder symptoms; whereas, those meeting Research endorsed more symptoms of greater severity. The group meeting Research was more highly enriched with the PON1 192R risk allele than those meeting CDC and Modified Kansas, and Research had twice the power to detect the previously described gene-environment interaction between hearing alarms and RR homozygosity (adjusted relative excess risk due to interaction [aRERI] = 7.69; 95% CI 2.71–19.13) than CDC (aRERI = 2.92; 95% CI 0.96–6.38) or Modified Kansas without exclusions (aRERI = 3.84; 95% CI 1.30–8.52) or with exclusions (aRERI = 3.42; 95% CI 1.20–7.56). The lower power of CDC and Modified Kansas relative to Research was due to greater false-positive disease misclassification from lower diagnostic specificity.ConclusionsThe original Research case definition had greater statistical power to detect a genetic predisposition to GWI. Its greater specificity favors its use in hypothesis-driven research; whereas, the greater sensitivity of the others favor their use in clinical screening for application of future diagnostic biomarkers and clinical care.

Effects of environmental and genetic interactions on job burnout in coal miners: interactions between occupational stress, coping styles, and NR3C2 gene polymorphisms.

To investigate the current situation regarding occupational burnout among coal miners, explore the relationship between NR3C2 gene polymorphism and occupational burnout, and analyze the influence of the interaction between environment and gene on occupational burnout. This study provides a scientific basis for formulating health strategies to combat job burnout. A total of 1,500 first-line coal mine workers were selected by cluster random sampling, and the job burnout scale, job content questionnaire (JCQ), and simplified coping style questionnaire (SCSQ) were used for the questionnaire survey. A total of 150 workers were randomly selected from the high burnout group and the low burnout group, and a total of 300 workers were selected as the research objects to examine the relationship between gene polymorphism, environment-gene interactions and burnout. This study employed iMLDRTM genotyping technology for NR3C2 gene (rs5522, rs2070950) polymorphism analysis. The relationship between the occurrence of job burnout, occupational stress, coping styles and the NR3C2 gene was analyzed. Finally, a total of 1,282 valid questionnaires were retrieved, with an effective recovery rate of 85.5%. The study included 128 participants (10%) with zero burnout, 400 (31.2%) with mild burnout, 649 (50.6%) with moderate burnout and 105 (8.2%) with severe burnout. There were significant differences in the rate of burnout among miners with respect to sex, age, working years, educational level, shifts, and marital status (P < 0.05). The difference in occupational stress between the different job burnout groups was statistically significant (P < 0.05). Compared with the GG genotype of rs2070950 of the NR3C2 gene, the CC genotype was identified as a susceptibility gene for occupational burnout (P < 0.05). In respect to rs5522, rs2070950, occupational stress, positive coping, and negative coping, the low-risk group was unlikely to suffer from job burnout compared with the high-risk group (OR = 0.103, 95%CI: 0.058-0.182). In addition to demographic characteristics, occupational stress and negative coping styles were also identified as risk factors for job burnout. The interaction between locus rs5522, locus rs2070950, occupational stress, positive response, and negative response were found to affect the incidence of occupational burnout.

Genotype-environment interaction in wood basic density of Eucalyptus camaldulensis and its four inter-specific hybrid clones

Genotype-environment interaction in wood basic density of Eucalyptus camaldulensis and its four inter-specific hybrid clones

Changes in Refractive Error During Young Adulthood: The Effects of Longitudinal Screen Time, Ocular Sun Exposure, and Genetic Predisposition.

Changes in refractive error during young adulthood is common yet risk factors at this age are largely unexplored. This study explored risk factors for these changes, including gene-environmental interactions. Spherical equivalent refraction (SER) and axial length (AL) for 624 community-based adults were measured at 20 (baseline) and 28 years old. Participants were genotyped and their polygenic scores (PGS) for refractive error calculated. Self-reported screen time (computer, television, and mobile devices) from 20 to 28 years old were collected prospectively and longitudinal trajectories were generated. Past sun exposure was quantified using conjunctival ultraviolet autofluorescence (CUVAF) area. Median change in SER and AL were -0.023 diopters (D)/year (interquartile range [IQR] = -0.062 to -0.008) and +0.01 mm/year (IQR = 0.000 to 0.026), respectively. Sex, baseline myopia, parental myopia, screen time, CUVAF, and PGS were significantly associated with myopic shift. Collectively, these factors accounted for approximately 20% of the variance in refractive error change, with screen time, CUVAF, and PGS each explaining approximately 1% of the variance. Four trajectories for total screen time were found: "consistently low" (n = 148), "consistently high" (n = 250), "consistently very high" (n = 76), and "increasing" (n = 150). Myopic shift was faster in those with "consistently high" or "consistently very high" screen time compared to "consistently-low" (P ≤ 0.031). For each z-score increase in PGS, changes in SER and AL increased by -0.005 D/year and 0.002 mm/year (P ≤ 0.045). Of the three types of screen time, only computer time was associated with myopic shift (P ≤ 0.040). There was no two- or three-way interaction effect between PGS, CUVAF, or screen time (P ≥ 0.26). Higher total or computer screen time, less sun exposure, and genetic predisposition are each independently associated with greater myopic shifts during young adulthood. Given that these factors explained only a small amount of the variance, there are likely other factors driving refractive error change during young adulthood.

Identification of the Initial Anthesis of Soybean Varieties Based on UAV Multispectral Time-Series Images

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data.

Analysis of exposome and genetic variability suggests stress as a major contributor for development of pancreatic ductal adenocarcinoma

BackgroundThe current knowledge on pancreatic ductal adenocarcinoma (PDAC) risk factors is limited and no study has comprehensively tested the exposome in combination with the genetic variability in relation to the disease susceptibility. AimThe aim of this study was to analyze the exposome and its interaction with known genetic susceptibility loci, in relation to PDAC risk. MethodsA case-control study nested in UK Biobank cohort was conducted on 816 PDAC cases and 302,645 controls. A total of 347 exposure variables, and a polygenic risk score (PRS) were analyzed through logistic regression. Gene-environment interaction analyses were conducted. ResultsA total of 52 associations under the Bonferroni corrected threshold of p < 1.46 × 10−4 were observed. Known risk factors such as smoking, pancreatitis, diabetes, PRS, heavy alcohol drinking and overweight were replicated in this study. As for novel associations, a clear indication for length and intensity of mobile phone use and the stress-related factors and stressful events with increase of PDAC risk was observed. Although the PRS was associated with PDAC risk (P = 2.09 × 10−9), statistically significant gene-exposome interactions were not identified. ConclusionIn conclusion, our results suggest that a stressful lifestyle and sedentary behaviors may play a major role in PDAC susceptibility independently from the genetic background.

Air pollution and oxidative stress in adults suffering from airway diseases. Insights from the Gene Environment Interactions in Respiratory Diseases (GEIRD) multi-case control study

Air pollution is a leading risk factor for global mortality and morbidity. Oxidative stress is a key mechanism underlying air-pollution-mediated health effects, especially in the pathogenesis/exacerbation of airway impairments. However, evidence lacks on subgroups at higher risk of developing more severe outcomes in response to air pollution. This multi-centre study aims to evaluate the association between air pollution and oxidative stress in healthy adults and in patients affected by airway diseases from the Italian GEIRD (Gene Environment Interactions in Respiratory Diseases) multi-case control study. Overall, 1841 adults (49 % females, 20–83 years) were included from four Italian centres: Pavia, Sassari, Turin, and Verona. Following a 2-stage screening process, we identified 1273 cases of asthma, chronic bronchitis, rhinitis, or COPD and 568 controls. Systemic oxidative stress was quantified by urinary 8-isoprostane and 8-OH-dG. Individual residential exposures to NO2, PM10, PM2.5, and O3 were derived using an innovative five-stage machine-learning-based approach. Linear mixed regression models tested the association between oxidative stress biomarkers and air pollution tertiles, adjusting by age, sex, BMI, smoking, education and season, with recruiting centres as random intercept. Only cases exhibited higher levels of log-transformed 8-isoprostane and 8-OH-dG in association with NO2 (β: 0.30 95 % CI: 0.08–0.52 and 0.20 95 % CI: 0.03–0.37), PM10 (0.34 95 % CI: 0.12–0.55 and 0.21 95 % CI: 0.05–0.37) and PM2.5 (0.27 95 % CI: 0.09–0.49 and 0.18 95 % CI: 0.02–0.34) as compared to the first tertile of exposure. No significant associations were observed for summer O3. Our findings suggest that exposure to air pollution may increase systemic oxidative stress levels in people suffering from airway diseases. This introduces a potential novel approach available for future epidemiological studies and Public Health for effective prevention strategies oriented at the quantification of early biological effects in susceptible people, whose additional risk level might be currently underrated. Air-pollution-mediated exacerbations, driven by oxidative stress, still deserve our attention.

Genotype-environment interaction in the variability of yield associated indices under stress conditions in sunflower

Abstract The impact of biotic (infestation with the Orobanche cumana) and abiotic stress (drought) factors on the productivity indicators of sunflower hybrids was evaluated in two years, 2020 and 2021, which were significantly different regarding to the weather conditions. The Principal Component Analysis (PCA) was applied on 10 parameters: plant height (PH); leaf number (LN); leaf area (LA); seed yield (SY); leaf area index (LAI); chlorophyll a content (Chla); chlorophyll b content (Chlb); carotenoids content (Car); chlorophyll pigment ratio (Chla/b); total of chlorophyll pigments (Chla+b), that are directly or indirectly associated with the productivity of sunflower hybrids. The first two PCs explained 75 % (drought stress for 2020) and 83 %, respectively (stress caused by broomrape infestation), of the total variance of parameters or hybrids. A higher number of positive correlations were identified between the studied morpho-physiological indices differentiating the hybrids. The SY index correlated with PH, LN, LAI, LA (r = 0.54–0.78) under biotic and abiotic stress. Also, seed yield related with the content of pigments (r = 0.65–0.79) in the case of infested hybrids with broomrape. The infested hybrid H11, in both years, showed the highest values for most of the analyzed indices, indicating a relatively high degree of tolerance to the combined stressors.