How Do Genetic and Environmental Factors Influence Cardiometabolic Risk Factors? Findings from the Isfahan Twins Study.

BACKGROUND: Disease-discordant twins are excellent subjects for matched case-control studies as they allow for the control of confounding factors such as age, gender, genetic background, and intrauterine and early environment factors. Study design: A cross-sectional study. METHODS: Past medical history documentation and physical examination were conducted for all participants. Fasting venous blood samples were taken to measure fasting blood glucose (FBG) and lipid levels. The ACE model, a structural equation model, was used to assess heritability. RESULTS: This study included 710 twin pairs (210 monozygotic and 500 dizygotic) ranging in age from 2 to 52 years (mean age: 11.67±10.71 years). The study was conducted using participants from the Isfahan Twin Registry (ITR) in 2017. Results showed that in early childhood (2-6 years), height, weight, and body mass index (BMI) were influenced by shared environmental factors (76%, 75%, and 73%, respectively). In late childhood (7-12 years), hip circumference, waist circumference (WC), and low-density lipoprotein (LDL) cholesterol were found to be highly heritable (90%, 76%, and 64%, respectively). In adolescents, height (94%), neck circumference (85%), LDL-cholesterol (81%), WC (70%), triglycerides (69%), weight (68%), and BMI (65%) were all found to be highly or moderately heritable. In adult twins, arm circumference (97%), weight (86%), BMI (82%), and neck circumference (81%) were highly heritable. CONCLUSION: This study demonstrates that both genetic and environmental factors play a role in influencing individuals at different stages of their lives. Notably, while certain traits such as obesity have a high heritability during childhood, their heritability tends to decrease as individuals transition into adulthood.

Environmental and Genetic Determinants of Ankylosing Spondylitis.

Exposure to heavy metals and lifestyle factors like smoking contribute to the production of free oxygen radicals. This fact, combined with a lowered total antioxidant status, can induce even more damage in the development of ankylosing spondylitis (AS). Despite the fact that some researchers are looking for more genetic factors underlying AS, most studies focus on polymorphisms within the genes encoding the human leukocyte antigen (HLA) system. The biggest challenge is finding the effective treatment of the disease. Genetic factors and the influence of oxidative stress, mineral metabolism disorders, microbiota, and tobacco smoking seem to be of great importance for the development of AS. The data contained in this review constitute valuable information and encourage the initiation and development of research in this area, showing connections between inflammatory disorders leading to the pathogenesis of AS and selected environmental and genetic factors.

Factor-Analytic Variance-Covariance Structures for Prediction Into a Target Population of Environments.

Finlay-Wilkinson regression is a popular method for modeling genotype-environment interaction in plant breeding and crop variety testing. When environment is a random factor, this model may be cast as a factor-analytic variance-covariance structure, implying a regression on random latent environmental variables. This paper reviews such models with a focus on their use in the analysis of multi-environment trials for the purpose of making predictions in a target population of environments. We investigate the implication of random versus fixed effects assumptions, starting from basic analysis-of-variance models, then moving on to factor-analytic models and considering the transition to models involving observable environmental covariates, which promise to provide more accurate and targeted predictions than models with latent environmental variables.

The Impact of Rheumatoid Arthritis on First Nations and How We Can Work With Communities to Prevent It.

Rheumatoid arthritis (RA) is prevalent in many Indigenous North American First Nations (FN) and tends to be seropositive, familial, and disabling, as well as associated with highly unfavorable outcomes such as early mortality. The risk of developing RA is based on a perfect storm of gene-environment interactions underpinning this risk. The gene-environment interactions include a high frequency of shared epitope encoding HLA alleles, particularly HLA-DRB1*1402, in the background population, and prevalent predisposing environmental factors such as smoking and periodontal disease. Together, these provide a compelling rationale for an RA prevention agenda in FN communities. Our research team has worked in partnership with several FN communities to prospectively follow the first-degree relatives of FN patients with RA, with the aim of better understanding the preclinical stages of RA in this population. We have focused on specific features of the anticitrullinated protein antibodies (ACPA) and other proteomic biomarkers as predictors of future development of RA. These studies have now led us to consider interventions having a favorable risk-benefit ratio if applied at a stage prior to a hypothetical "point of no return," when the autoimmunity potentially becomes irreversible. Based on a supportive mouse model and available human studies of curcumin, omega-3, and vitamin D supplements, we are undertaking studies where we screen communities using dried blood spot technology adapted for the detection of ACPA, and then enrolling ACPA-positive individuals in studies that use a combination of these supplements. These studies are guided by shared decision-making principles.

LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information.

Gene-environment (GE) interactions are essential in understanding human complex traits. Identifying these interactions is necessary for deciphering the biological basis of such traits. In this study, we review state-of-art methods for estimating the proportion of phenotypic variance explained by genome-wide GE interactions and introduce a novel statistical method Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (LDER-GE). LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics. LDER-GE leverages the complete Linkage Disequilibrium (LD) matrix, as opposed to only the diagonal squared LD matrix utilized by LDSC (Linkage Disequilibrium Score)-based methods. Our extensive simulation studies demonstrate that LDER-GE performs better than LDSC-based approaches by enhancing statistical efficiency by ~23%. This improvement is equivalent to a sample size increase of around 51%. Additionally, LDER-GE effectively controls type-I error rate and produces unbiased results. We conducted an analysis using UK Biobank data, comprising 307 259 unrelated European-Ancestry subjects and 966 766 variants, across 217 environmental covariate-phenotype (E-Y) pairs. LDER-GE identified 34 significant E-Y pairs while LDSC-based method only identified 23 significant E-Y pairs with 22 overlapped with LDER-GE. Furthermore, we employed LDER-GE to estimate the aggregated variance component attributed to multiple GE interactions, leading to an increase in the explained phenotypic variance with GE interactions compared to considering main genetic effects only. Our results suggest the importance of impacts of GE interactions on human complex traits.

Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars.

This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype-environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.

Estimation of environment stability for fruit yield and capsaicin content by using two models in Capsicum chinense Jacq. (Ghost Pepper) with multi-year evaluation.

Background: Capsicum chinense Jacq. (Ghost Pepper) is well-known for its high pungency and pleasant aroma. The recent years witnessed a significant decline in popularity of this important crop due to the use of inferior planting material and lack of elite lines. To maintain constant performance across a variety of settings, it is crucial to choose stable lines with high yield and capsaicin content, as these are the most promising traits of Ghost Pepper. Method: In this study, 120 high-capsaicin genotypes were subjected to a 3-year (kharif 2017, 2018 and 2019) stability investigation utilizing two well-known stability methods: Eberhart-Russell (ER) and additive main effects and multiple interaction (AMMI). Three replications were used following Randomized Complete Block Design for 11 traits. The experiment soil was sandy loam with pH 4.9. Minimum and maximum temperature of 18.5 °C, 17.5 °C, 17.4 °C and 32.2 °C, 31.3 °C, 32.7 °C and rainfall of 1,781, 2,099, 1,972 mm respectively was recorded for the study period. Result: The genotype-environment linear interaction (G×E Lin.) was highly significant for days to 50% flowering, capsaicin content, fruit length and girth, fruit yield per plant and number of fruits per plant at p < 0.005. G×E interaction for fruit yield and capsaicin content in AMMI-analysis of variance reported 67.07% and 71.51% contribution by IPCA-1 (interactive principal component axis) and 32.76% and 28.49% by IPCA-2, respectively. Eight genotypes were identified to be stable with high yield and capsaicin content. The identified stable lines can be opted for cultivation to reduce the impact of crop failure when grown in different macro-environments. Moreover, the pharmaceutical and spice sectors will also be benefitted from the lines with high capsaicin content. Further research assessing the lines' performance across various regions of India can provide a solid foundation for the crop's evaluation at national level.

Association of Maternal Dietary Habits and Infant MTHFR Gene Polymorphisms with Ventricular Septal Defect in Offspring: A Case-Control Study.

This study aimed to explore the association of maternal diet, infant MTHFR gene polymorphisms, and their interactions with the risk of ventricular septal defects (VSDs). This case-control study recruited 448 mothers of VSD children and 620 mothers of healthy counterparts. Multivariable-adjusted logistic regression models were constructed to examine the association between maternal dietary habits during the first trimester of gestation, MTHFR gene polymorphisms, and VSD. Gene-environment interaction effects were analyzed through logistic regression models, with false discovery rate p-value (FDR_p) < 0.05. Maternal excessive intake of fermented bean curd (OR = 2.00, 95%CI: 1.59-2.52), corned foods (OR = 2.23, 1.76-2.84), fumatory foods (OR = 1.75, 1.37-2.23), grilled foods (OR = 1.34, 1.04-1.72), and fried foods (OR = 1.80, 1.42-2.27) was associated with an increased risk of VSD. Regular intake of fish and shrimp (OR = 0.42, 0.33-0.53), fresh eggs (OR = 0.58, 0.44-0.75), soy products (OR = 0.69, 0.56-0.85), and dairy products (OR = 0.71, 0.59-0.85) was found to reduce the occurrence of VSD. Moreover, MTHFR gene polymorphisms at rs2066470 (homozygous: OR = 4.28, 1.68-10.90), rs1801133 (homozygous: OR = 2.28, 1.39-3.74), and rs1801131 (heterozygous: OR = 1.75, 1.24-2.47; homozygous: OR = 3.45, 1.50-7.95) elevated offspring susceptibility to VSDs. Furthermore, significant interactions of MTHFR polymorphisms with maternal dietary habits were observed, encompassing corned foods, fermented bean curd, fried foods, and grilled foods. Maternal dietary habits; MTHFR polymorphisms at rs2066470, rs1801131, and rs1801133; and their interactions were significantly associated with the occurrence of VSDs in offspring.

Across-environment seed protein stability and genetic architecture of seed components in soybean.

The recent surge in the plant-based protein market has resulted in high demands for soybean genotypes with improved grain yield, seed protein and oil content, and essential amino acids (EAAs). Given the quantitative nature of these traits, complex interactions among seed components, as well as between seed components and environmental factors and management practices, add complexity to the development of desired genotypes. In this study, the across-environment seed protein stability of 449 genetically diverse plant introductions was assessed, revealing that genotypes may display varying sensitivities to such environmental stimuli. The EAAs valine, phenylalanine, and threonine showed the highest variable importance toward the variation in stability, while both seed protein and oil contents were among the explanatory variables with the lowest importance. In addition, 56 single nucleotide polymorphism (SNP) markers were significantly associated with various seed components. Despite the strong phenotypic Pearson's correlation observed among most seed components, many independent genomic regions associated with one or few seed components were identified. These findings provide insights for improving the seed concentration of specific EAAs and reducing the negative correlation between seed protein and oil contents.

Quantum-like environment adaptive model for creation of phenotype.

The textbook conceptualization of phenotype creation, "genotype (G) + environment (E) + genotype & environment interactions (GE) ↦ phenotype (Ph)", is modeled with open quantum systems theory (OQST) or more generally with adaptive dynamics theory (ADT). The model is quantum-like, i.e., it is not about quantum physical processes in biosystems. Generally such modeling is about applications of the quantum formalism and methodology outside of physics. Macroscopic biosystems, in our case genotypes and phenotypes, are treated as information processors which functioning matches the laws of quantum information theory. Phenotypes are the outputs of the E-adaptation processes described by the quantum master equation, Gorini-Kossakowski-Sudarshan-Lindblad equation (GKSL). Its stationary states correspond to phenotypes. We highlight the class of GKSL dynamics characterized by the camel-like graphs of (von Neumann) entropy: in the process of E-adaptation phenotype's state entropy (disorder) first increases and then falls down - a stable and well-ordered phenotype is created. Traits, an organism's phenotypic characteristics, are modeled within the quantum measurement theory, as generally unsharp observables given by positive operator valued measures (POVMs. This paper is also a review on the methods and mathematical apparatus of quantum information biology.

Whole-exome sequencing of individuals from an isolated population under extreme conditions implicates rare risk variants of schizophrenia.

Schizophrenia (SCZ), which affects approximately 1% of the world's population, is a global public health concern. It is generally considered that the interplay between genes and the environment is important in the onset and/or development of SCZ. Although several whole-exome sequencing studies have revealed rare risk variants of SCZ, no rare coding variants have been strongly replicated. Assessing isolated populations under extreme conditions might lead to the discovery of variants with a recent origin, which are more likely to have a higher frequency than chance to reflect gene-environment interactions. Following this approach, we examined a unique cohort of Tibetans living at an average altitude above 4500 meters. Whole-exome sequencing of 47 SCZ cases and 53 controls revealed 275 potential novel risk variants and two known variants (12:46244485: A/G and 22:18905934: A/G) associated with SCZ that were found in existing databases. Only one gene (C5orf42) in the gene-based statistics surpassed the exome-wide significance in the cohort. Metascape enrichment analysis suggested that novel risk genes were strongly enriched in pathways relevant to hypoxia, neurodevelopment, and neurotransmission. Additionally, 47 new risk genes were followed up in Han sample of 279 patients with SCZ and 95 controls, only BAI2 variant appearing in one case. Our findings suggest that SCZ patients living at high altitudes may have a unique risk gene signature, which may provide additional information on the underlying biology of SCZ, which can be exploited to identify individuals at greater risk of exposure to hypoxia.

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

KEY MESSAGE: Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with high-dimensional data such as climate and soil information is becoming a common practice in breeding programs. This study explored new ways to combine non-genetic information in genomic prediction models using machine learning. Using the multi-environment trial data from the Genomes To Fields initiative, different models to predict maize grain yield were adjusted using various inputs: genetic, environmental, or a combination of both, either in an additive (genetic-and-environmental; G+E) or a multiplicative (genotype-by-environment interaction; GEI) manner. When including environmental data, the mean prediction accuracy of machine learning genomic prediction models increased up to 7% over the well-established Factor Analytic Multiplicative Mixed Model among the three cross-validation scenarios evaluated. Moreover, using the G+E model was more advantageous than the GEI model given the superior, or at least comparable, prediction accuracy, the lower usage of computational memory and time, and the flexibility of accounting for interactions by construction. Our results illustrate the flexibility provided by the ML framework, particularly with feature engineering. We show that the feature engineering stage offers a viable option for envirotyping and generates valuable information for machine learning-based genomic prediction models. Furthermore, we verified that the genotype-by-environment interactions may be considered using tree-based approaches without explicitly including interactions in the model. These findings support the growing interest in merging high-dimensional genotypic and environmental data into predictive modeling.

Genotypic variation in the promoter region of the CRH-248 gene interacts with early rearing experiences to disrupt the development of the HPA axis in infant rhesus macaques (Macaca mulatta).

Aberrant functioning of the hypothalamic-pituitary-adrenal (HPA) axis is a hallmark of conditions such as depression, anxiety disorders, and post-traumatic stress disorder. Early-life adversity and genetic variation can interaction to disrupt HPA axis regulation, potentially contributing to certain forms of psychopathology. This study employs a rhesus macaque model to investigate how early parental neglect interacts with a single nucleotide polymorphism within the promoter region of the corticotropin-releasing hormone (CRH-248) gene, impacting the development of the HPA axis. For the initial six months of life, 307 rhesus monkey infants (n = 146 females, n = 161 males) were either reared with their mothers (MR) in conditions emulating the natural environment (control group) or raised without maternal care in groups with constant or 3-hours daily access to same-aged peers (NR). Blood samples collected on days 30, 60, 90, and 120 of life under stressful conditions were assayed for plasma cortisol and adrenocorticotropic hormone (ACTH) concentrations. Findings revealed that NR subjects exhibited a significant blunting of both ACTH and cortisol concentrations. Notably, there was a gene-by-environment interaction observed for ACTH and cortisol levels, with NR subjects with the polymorphism displaying higher ACTH concentrations and lower cortisol concentrations. To the extent that these results generalize to humans, they suggest that early parental neglect may render individuals vulnerable to HPA axis dysfunction, a susceptibility that is modulated by CRH-248 genotype-a gene-by-environment interaction that leaves a lasting developmental signature.

Five years of change in adult twins: longitudinal changes of genetic and environmental influence on epigenetic clocks.

BACKGROUND: Epigenetic clocks were known as promising biomarkers of aging, including original clocks trained by individual CpG sites and principal component (PC) clocks trained by PCs of CpG sites. The effects of genetic and environmental factors on epigenetic clocks are still unclear, especially for PC clocks. METHODS: We constructed univariate twin models in 477 same-sex twin pairs from the Chinese National Twin Registry (CNTR) to estimate the heritability of five epigenetic clocks (GrimAge, PhenoAge, DunedinPACE, PCGrimAge, and PCPhenoAge). Besides, we investigated the longitudinal changes of genetic and environmental influences on epigenetic clocks across 5 years in 134 same-sex twin pairs. RESULTS: Heritability of epigenetic clocks ranged from 0.45 to 0.70, and those for PC clocks were higher than those for original clocks. For five epigenetic clocks, the longitudinal stability was moderate to high and was largely due to genetic effects. The genetic correlations between baseline and follow-up epigenetic clocks were moderate to high. Special unique environmental factors emerged both at baseline and at follow-up. PC clocks showed higher longitudinal stability and unique environmental correlations than original clocks. CONCLUSIONS: For five epigenetic clocks, they have the potential to identify aging interventions. High longitudinal stability is mainly due to genetic factors, and changes of epigenetic clocks over time are primarily due to changes in unique environmental factors. Given the disparities in genetic and environmental factors as well as longitudinal stability between PC and original clocks, the results of studies with original clocks need to be further verified with PC clocks.

Gene-environment interaction between gaming addiction and perceived stress in late adolescents and young adults: A twin study.

Background and aims: The association between perceived stress (PS) and gaming addiction (GA) is well documented. However, the mechanism for explaining this association remains unclear. Using a genetically informative design, this study aims to distinguish between the diathesis-stress and bio-ecological models of gene by environment interaction (G x E) to explain the underlying mechanism of the relationship. Methods: In total, 1,468 twins (mean age = 22.6 ± 2.8 years) completed an online survey including the GA and PS scales. Twin correlations for GA and PS were computed and univariate model-fitting analysis was conducted to determine genetic and environmental influences on GA and PS. The bivariate G x E model-fitting analysis was performed to determine the best G x E interaction model. Results: Additive genetic, shared environmental, and non-shared environmental effects were 0.70 (95%CI = 0.61, 0.77), 0.00, and 0.30 (95%CI = 0.26, 0.33), and 0.38 (95%CI = 0.24, 0.55), 0.35 (95% CI = 0.18, 0.51), and 0.22 (95%CI = 0.20, 0.26) for GA and PS, respectively. Bivariate G x E model-fitting analysis supported the diathesis-stress model, where genetic influences on GA were greater in higher levels of PS, whereas environmental influences on GA were small and constant across levels of PS. Discussion and conclusions: The evidence for the diathesis-stress model for GA is consistent with the etiological process of many forms of psychopathology. The findings should be incorporated in clinical settings to improve the treatment of GA, and used in developments of intervention and prevention methods for GA.

Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index, and genotype-environment interaction at different irrigation regimes with adaptation to universal warming.

Climate change has brought an alarming situation in the scarcity of fresh water for irrigation due to the present global water crisis, climate variability, drought, increasing demands of water from the industrial sectors, and contamination of water resources. Accurately evaluating the potential of future rice genotypes in large-scale, multi-environment experiments may be challenging. A key component of the accurate assessment is the examination of stability in growth contexts and genotype-environment interaction. Using a split-plot design with three replications, the study was carried out in nine locations with five genotypes under continuous flooding (CF) and alternate wet and dry (AWD) conditions. Utilizing the web-based warehouse inventory search tool (WIST), the water status was determined. To evaluate yield performance for stability and adaptability, AMMI and GGE biplots were used. The genotypes clearly reacted inversely to the various environments, and substantial interactions were identified. Out of all the environments, G3 (BRRI dhan29) had the greatest grain production, whereas G2 (Binadhan-8) had the lowest. The range between the greatest and lowest mean values of rice grain output (4.95 to 4.62 t ha-1) was consistent across five distinct rice genotypes. The genotype means varied from 5.03 to 4.73 t ha-1 depending on the environment. In AWD, all genotypes out performed in the CF system. With just a little interaction effect, the score was almost zero for several genotypes (E1, E2, E6, and E7 for the AWD technique, and E5, E6, E8, and E9 for the CF method) because they performed better in particular settings. The GGE biplot provided more evidence in support of the AMMI study results. The study's findings made it clear that the AMMI model provides a substantial amount of information when evaluating varietal performance across many environments. Out of the five accessions that were analyzed, one was found to be top-ranking by the multi-trait genotype ideotype distance index, meaning that it may be investigated for validation stability measures. The study's findings provide helpful information on the variety selection for the settings in which BRRI dhan47 and BRRI dhan29, respectively, performed effectively in AWD and CF systems. Plant breeders might use this knowledge to choose newer kinds and to design breeding initiatives. In conclusion, intermittent irrigation could be an effective adaptation technique for simultaneously saving water and mitigating GHG while maintaining high rice grain yields in rice cultivation systems.

Partitioning variance in cortical morphometry into genetic, environmental, and subject-specific components.

The relative contributions of genetic variation and experience in shaping the morphology of the adolescent brain are not fully understood. Using longitudinal data from 11,665 subjects in the ABCD Study, we fit vertex-wise variance components including family effects, genetic effects, and subject-level effects using a computationally efficient framework. Variance in cortical thickness and surface area is largely attributable to genetic influence, whereas sulcal depth is primarily explained by subject-level effects. Our results identify areas with heterogeneous distributions of heritability estimates that have not been seen in previous work using data from cortical regions. We discuss the biological importance of subject-specific variance and its implications for environmental influences on cortical development and maturation.

Genetic, epigenetic and environmental factors in diverticular disease: systematic review.

BACKGROUND: Diverticulosis is a normal anatomical variant of the colon present in more than 70% of the westernized population over the age of 80. Approximately 3% will develop diverticulitis in their lifetime. Many patients present emergently, suffer high morbidity rates and require substantial healthcare resources. Diverticulosis is the most common finding at colonoscopy and has the potential for causing a significant morbidity rate and burden on healthcare. There is a need to better understand the aetiology and pathogenesis of diverticular disease. Research suggests a genetic susceptibility of 40-50% in the formation of diverticular disease. The aim of this review is to present the hypothesized functional effects of the identified gene loci and environmental factors. METHODS: A systematic literature review was performed using PubMed, MEDLINE and Embase. Medical subject headings terms used were: 'diverticular disease, diverticulosis, diverticulitis, genomics, genetics and epigenetics'. A review of grey literature identified environmental factors. RESULTS: Of 995 articles identified, 59 articles met the inclusion criteria. Age, obesity and smoking are strongly associated environmental risk factors. Intrinsic factors of the colonic wall are associated with the presence of diverticula. Genetic pathways of interest and environmental risk factors were identified. The COLQ, FAM155A, PHGR1, ARHGAP15, S100A10, and TNFSF15 genes are the strongest candidates for further research. CONCLUSION: There is increasing evidence to support the role of genomics in the spectrum of diverticular disease. Genomic, epigenetic and omic research with demographic context will help improve the understanding and management of this complex disease.