Gene-environment Interactions Research Articles

Paternally Inherited Noonan Syndrome Caused by a PTPN11 Variant May Exhibit Mild Symptoms: A Case Report and Literature Review.

Noonan syndrome (NS)/Noonan syndrome with multiple lentigines (NSML) is commonly characterized by distinct facial features, a short stature, cardiac problems, and a developmental delay of variable degrees. However, as many as 50% of individuals diagnosed with NS/NSML have a mildly affected parent or relative due to variable expressivity and possibly incomplete penetrance of the disorder, and those who are recognized to have NS only after a diagnosis are established in a more obviously affected index case. In order to collect intergenerational data reported from previous studies, electronic journal databases containing information on the molecular genetics of PTPN11 were searched from 2000 to 2022. We present a case of a proband with a PTPN11 variant (c.1492C > T/p.Arg498Trp) inherited from an asymptomatic father, displaying only mild intellectual disability without classical symptoms of NS. Among our cases and the reported NS cases caused by the PTPN11 p.Arg498Trp variant, cardiac abnormalities (6/11), facial dysmorphism (7/11), skin pigmentation (4/11), growth problems (4/11), and sensorineural hearing loss (2/11) have been observed. NS/NSML patients with the PTPN11 p.Arg498Trp variant tend to exhibit relatively lower frequencies of skin pigmentation, facial dysmorphism and cardiac abnormalities and mild symptoms compared to those carrying any other mutated PTPN11. Paternally inherited NS/NSML caused by a PTPN11 p.Arg498Trp variant, including our cases, may exhibit relatively lower frequencies of abnormal features and mild symptoms. This could be ascribed to potential gene-gene interactions, gene-environment interactions, the gender and phenotype of the transmitting parent, or ethnic differences that influence the clinical phenotype.

Stability analysis and nutritional quality of soybean (Glycine max (L).Merrill.) genotypes for feed in southwestern Ethiopia

Soybean is a leguminous crop known for its multiple utilizations both as food and feed for humans and livestock. The objectives of the study were to identify high dry matter yielder and stable genotypes across environments in southwestern Ethiopia. The effect of genotype environment (G x E) interaction on dry matter yield of soybean genotypes were evaluated in two cropping seasons (2019–2020) under rain fed condition. Eight pre tested soybean genotypes with two checks were used as treatment in a randomized complete block design with three replications. Collected data were recorded and analyzed using GGE biplot models using R software. The combined analysis of variance showed that dry matter yield of soybean genotypes was significantly affected by genotype, environment and genotype-environment (G x E) interaction. The genotype, environment, and genotype-environment interaction, respectively, accounted for 11.4%, 49.5%, and 38.8% of the observed variation to the dry mater yield. This indicates that dry matter yield was significantly more affected by environments and G × E interaction than genotypes. The GGE biplot analysis revealed that six environments used in the current study were grouped into four mega-environments. The mega-environments were identified for genotype evaluation. The biplot showed that the vertex genotypes were G4, G10, and G9 and considered as optimum performance in their respective mega-environments and more responsive to environmental changes. The biplot also showed that ENV5 (Kersa 2020) was an ideal and the most discriminating and representative environment. Genotype G4 (TGX1990-114FN) was the ideal genotype and overall winner in dry matter yield and stability in the findings. Therefore, genotype G4 (TGX-1990-114FN) is the better option to be used as forage soybean in Ethiopia. Further demonstration of the feeding values of high yielders and stable genotypes on animal performances should be done.

Epidemiology of Bladder Cancer: Current Insight

Bladder cancer (BC) is a 9th most common cancer in the world that make from 3% of global neoplasms. Bladder cancer is prevalent in the developed countries. Cancer of the bladder is four times more common in men than women. External risk factors and internal triggers contribute to the development of bladder cancer. Thus, comprehensive analysis of these risk factors is a earnest to timely prevention of indicated oncologic pathology. The objective: to do a current systematic review of epidemiology of bladder cancer and modern important risk factors, which appropriate to the given pathology. Evidence acquisition. We carried out a systemic review of epidemiology of bladder cancer embracing PubMed and Embase including March 2023. All studies reporting gender, smoking, E-cigarette use, opium consumption, occupational carcinogene expose, dietary factors, race, gene-environment interaction, socioeconomic status. Microbiome factors, urolithiasis, benign prostate hyperplasiapelvic radiotherapy were excluded. Evidence synthesis. A total of 516 papers, that studied epidemiology of bladder cancer, were involved. According to GLOBOCAN, there were 573 000 new bladder cancer cases and 213 000 deaths worldwide in 2020. Smoking and occupational exposures (aromatic amines and polycyclic aromatic hydrocarbons) are the most important risk factors. Moreover, the additional risk factors include some dietary agents, gene specificities, diesel exhaust emissions and etc. Conclusions. Smoking tobacco and specific occupational exposures are the most important risk factors. Prevention strategies, including smoke cessation, responsible workplace safety, diet, schistosomias prevention are useful for reduction of the incidence BC. But high-quality studies are required to confirm initial results that will support timely prevention of BC.

Abstract 6140: Gene-environment interactions for lifetime water arsenic exposure and bladder cancer risk in the New England Bladder Cancer study

Abstract Environmental arsenic and genetic susceptibility have both been shown to increase bladder cancer risk, yet little is known about their respective interaction. We conducted a gene-environment interaction (GxE) study to characterize lifetime arsenic exposure from drinking water, inherited susceptibility, and bladder cancer risk in the New England Bladder Cancer Study. Exposures were estimated from arsenic measured in water samples collected at home and workplace. We evaluated common single-nucleotide polymorphisms (SNPs) i) from the latest bladder cancer genome-wide association study (n= 28), ii) from candidate gene studies of arsenic or bladder cancer (n= 16 SNPs), and iii) SNPs from a large genome-wide association study of arsenic metabolism (n = 4 SNPs) as potential effect modifiers of the arsenic-bladder cancer relationship. We coded SNPs as the absence or presence of a risk/effect allele for bladder cancer or arsenic metabolism in 2,019 adults (928 bladder cancer cases and 1,091 controls) and arsenic exposures as tertiles. Odds ratios (OR) with 95% confidence intervals (CI) adjusted for age, sex, smoking status, high-risk occupation, and principal components of genetic ancestry were estimated using logistic regression models. P-values for multiplicative interaction were calculated. Out of 48 SNPs, we found evidence of interaction for 7 SNPs at the statistical significance level of P<0.05. Most SNPs involved in GxE interactions are mapped to genomic regions known or suspected to function in arsenic metabolism. For the association between cumulative lifetime water arsenic (mg) and bladder cancer, the strongest evidence of an interaction was with rs1046428 (near GSTZ1 on 14q23); among those with one or two copies of the T risk allele, ORT2vsT1: 1.52, CI: 1.11 - 2.07; ORT3 vsT1: 1.44, CI: 1.05 - 1.98, Pinteraction = 0.01; among those with no risk allele copy, ORT2vsT1: 0.91, CI: 0.62 - 1.35; ORT3 vsT1: 0.97, CI: 0.65 - 1.44. For average daily water arsenic (µg/day), we found evidence of interaction among those with risk allele for rs1801133 (A/A, A/G; MTHFR on 1p36) ORT2 vsT1: 1.70, CI: 1.18 - 2.44; ORT3 vsT1: 1.53, CI: 1.06 - 2.23, Pinteraction = 0.02, in those with no copies of risk allele ORT2vsT1: 0.85, CI: 0.61 - 1.17; ORT3 vsT1: 1.11, CI: 0.79 - 1.54; and rs1801131 (G/G; G/T; MTHFR ) ORT2 vsT1: 1.59, CI: 1.14 - 2.22; ORT3 vsT1: 1.63, CI: 1.16 - 2.29, Pinteraction = 0.01, whereas in those with no risk allele ORT2vsT1: 0.84, CI: 0.59 - 1.19; ORT3 vsT1: 1, CI: 0.69 - 1.44. For 40-year lagged cumulative lifetime water arsenic (mg) and average daily water arsenic (µg/day), we found suggestive interactions with rs2234636 (SLC39A2 on 14q11), and rs1495741 (NAT2 on 8p22). Our study provides evidence of interactions between lifetime arsenic exposure and genetic variants that function in xenobiotic metabolism on bladder cancer occurrence. Citation Format: Hugo Pomares-Millan, Stella Koutros, Nathaniel Rothman, Dalsu Baris, Molly Schwenn, Alison Johnson, Debra T. Silverman, Margaret R. Karagas, Michael N. Passarelli. Gene-environment interactions for lifetime water arsenic exposure and bladder cancer risk in the New England Bladder Cancer study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6140.

Abstract 6142: Interactions of processed meat and red meat intake with pathway-based polygenic risk scores for colorectal cancer: A novel approach for PRSs construction

Abstract Introduction: Colorectal cancer (CRC) is the third most common cancer, and second cause of cancer death worldwide. Established CRC risk factors (E) include high red meat and processed meat intake. Genome-wide association studies (GWAS) have reported over 200 genetic variants associated with CRC risk. We used functional annotation data to identify subsets of GWAS variants within known pathways and constructed corresponding pathway Polygenic Risk Scores (pPRS). We evaluated pPRS by E interactions to determine whether genes within specific pathways interacted with meat intake to impact CRC risk. Methods: A pooled sample of 54,531 CRC controls and 48,260 cases of European ancestry from 27 studies were analyzed. Study-specific quartiles for red and processed meat intake were generated through in-person interviews and structured self-administered questionnaires. Variants were imported to AnnoQ for annotation (n=203) and analyzed for overrepresentation in PANTHER-reported pathways with Fisher’s exact test. Standard approaches were used to compute polygenic risk score weights relating the 203 variants to CRC. The subset of weights corresponding to each of five pathways were then used to compute the corresponding pPRS. Covariate-adjusted logistic regression models evaluated pPRSxE interactions with red meat and processed meat intake. Results: A total of 34 unique variants were overrepresented in five pathways: Apoptosis signaling, Alzheimer disease-presenilin, Wnt-signaling, Gonadotropin-releasing hormone receptor, and TGF-beta signaling. We found a statistically significant interaction between TGF-beta-pPRS and red meat intake (p = 0.0012). Stratified analyses reported a dose-response trend in the red meat and CRC risk association, with decreasing estimates of red meat and CRC risk association, comparing first to fourth quartiles of meat intake, with increasing quartiles of TGF-beta-pPRS: 13% (Q1), 12% (Q2), 7% (Q3), and 6% (Q4). This association remained significant after adjustment for all other variants (p = 0.0013), and all other pPRS with variants that did not overlap with the TGF-beta-pPRS (p = 0.0012). Independent GxE interaction models for individual variants that were included in the TGF-beta pathway showed significant interactions with red meat for rs2337113 (intron SMAD7 gene, Chr18), and rs2208603 (intergenic region BMP5, Chr6) (p = 0.013 & 0.0108, respectively). We did not find significant pPRS x red meat interactions for the other four pathways or with any pPRS x processed meat. Conclusion: This pathway-based interaction analysis revealed a statistical interaction between SNPs in the TGF-beta pathway and red meat consumption that impacts CRC risk. These findings shed light into the possible mechanistic link between CRC risk and red meat consumption. Citation Format: Joel Sanchez Mendez, Mariana C. Stern, Yubo Fu, John Morrison, Juan P. Lewinger, Eric Kawaguchi, Bryan Queme, Huaiyu Mi, Flora Qu, Ulrike Peters, Li Hsu, Stephen B. Gruber, Li Li, Michelle Cotterchio, Loic Le Marchand, Andrew J. Pellat, Elizabeth A. Platz, W James Gauderman. Interactions of processed meat and red meat intake with pathway-based polygenic risk scores for colorectal cancer: A novel approach for PRSs construction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6142.

Gene‒environment interaction effect of hypothalamic‒pituitary‒adrenal axis gene polymorphisms and job stress on the risk of sleep disturbances.

Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38-2.22], 1.68 [1.30-2.18] and 1.43 [1.09-1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51-0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.

Analysis of Pearl Millet's G x E Interaction with the Proposed New Index

Developing cultivars that are stable in a variety of conditions has been a problem for plant breeders. The environment can have an impact on a cultivar's phenotypic performance, or different environments can have different effects on different cultivars. Variance resulting from a combination of an individual's genetic composition and the environment in which they were raised is referred to as the genotype-environment interaction. Reducing genotype-environment interaction through breeding stable genotypes facilitates selection of stable, high-yielding genotypes. In multi-environment cultivar trials, AMMI and GGE biplot analyses are frequently utilized to explain G×E interactions. In order to assess breeding material effectively, India's pearl millet agriculture has been split into three main zones, A1, A, and B, based on climatic circumstances. Using AMMI and GGE biplot analysis, the current study assessed the G×E interaction in pearl millet genotypes from Zone-B in India. Based on normalized grain yield and ASV indices, a new weighted index (WI) has been developed to assess stable and high-yielding genotypes. For this zone, the three interaction principal component axes (IPCA1, IPCA2, and IPCA3) have been found to be important. The indices YSI and WI have been used to identify both the high-yield and most stable genotypes, while the AMMI Stability Value (ASV) and Stability Index have been used to find the most stable genotypes. Based on WI, the genotypes G24 and G13 have been identified stable and high yielding genotypes for zone-B.

Food additives and brain complications: A risk to public health

Food additives comprise the amounts of xenobiotics that are known to affect the physiological metabolic pathways of living tissues. Although organic and inorganic chemicals are considered xenobiotics, organic compounds, the basic chemical entity for most constituents, are reported to be responsible for 80-90% xenobiotics-induced toxicity. Food additives have been the major source of generating these harmful toxins in the human population. Exposure to xenobiotics via dietary habits, environment or medication is inexorable in living creatures. Reports have shown the bidirectional interaction between xenobiotics and biological systems. When ingested, xenobiotics induce physiological, biochemical or pathological changes in the biological system. One study reported that exposure to titanium dioxide nanoparticles can cause cytotoxicity of glial cells and hippocampal neurons, leading to brain damage and changes in hippocampal function, which plays a vital role in the process of memory and learning. Therefore, the complexity and limited plasticity of the brain has always challenged researchers to seek solutions to neuronal and cognitive problems. Including new compounds in the food in the form of pesticides, preservatives, additives, and coloring agents in daily meals is the latest aspect of gene-environment interaction. The sporadic cause of neurodegenerative diseases and its epidemiological distribution can be better understood and countermeasures can be applied only if this relationship is represented quantitatively. Hence, this review focuses on the effect of food additives as xenobiotics, which leads to the potential for a compromised nervous system.

Advancements in Enhancing Oil Quality in Rapeseed and Mustard: A Comprehensive Review

Rapeseed and mustard are important oilseed crops cultivated worldwide for their high oil content and versatile applications in food, feed, and industrial sectors. The quality of oil derived from these crops is influenced by various genetic, environmental, and agronomic factors. Breeding efforts aimed at improving oil quality traits in rapeseed and mustard have garnered significant attention in recent years due to their impact on market value, nutritional attributes, and industrial utility. In this review, we provide a comprehensive overview of breeding strategies and methodologies employed to enhance oil quality traits in rapeseed and mustard. We explore the genetic basis of oil quality traits, including fatty acid composition, erucic acid content, glucosinolate content, and tocopherol content, and discuss the importance of these traits for different end-uses. Furthermore, we highlight the role of molecular markers, genomics-assisted breeding, and biotechnological approaches in accelerating the breeding process and achieving targeted improvements in oil quality. The review also addresses the challenges and constraints associated with breeding for oil quality in rapeseed and mustard, including genotype-environment interactions, trait stability, and regulatory considerations. Additionally, we discuss emerging trends and future prospects in oil quality breeding, such as genome editing, metabolic engineering, and precision breeding, which offer novel avenues for achieving desired oil quality profiles while addressing sustainability and consumer preferences. Overall, this review underscores the significance of breeding for oil quality in rapeseed and mustard and provides insights into the latest advancements, challenges, and opportunities in this field. By integrating multidisciplinary approaches and harnessing the power of modern breeding tools and technologies, rapeseed and mustard breeders can continue to drive innovation and deliver oilseed crops with enhanced nutritional value, functional properties, and market competitiveness.

Relative Discriminatory Ability of AMMI and GGE Biplot in Analysis of GxE Interaction for Yield and Seed Index in Chickpea (Cicer arietinum L.)

Background: The research focused on investigating the impact of genotype-environment interaction on the yield stability and performance of eight chickpea genotypes across three different locations (Bhatapara, Kawardha and Raipur). Method: The study employed a randomized block design with two replications in each environment in Rabi season during 2021-22. Analysis of variance (ANOVA) revealed significant variations between environments and genotype-environment interaction (G x E) based on AMMI and GGE biplot. Results: To visualize the genotype-environment relationship, a two-dimensional GGE biplot was created using the first two principal components, which accounted for 87.8% and 11.5% of the interaction’s variation. The GGE biplot indicated that genotype g4 (ICCX 161100-B-B-B-B) in environment e2 (Kawardha) and g8 (JG-24) in environment e1 (Bhatapara) exhibited high yield. Genotype g3 (ICCV 201112) in environment e3 (Raipur) also showed high yield, while genotype g5 (JG 2020-10) demonstrated stability across all three environments. Furthermore, the AMMI analysis identified genotype g2 (ICCV 201103) as a superior performer in terms of yield stability across environments. By employing the GGE biplot and AMMI analysis, the study categorized the genotypes into three groups, facilitating simplified visual evaluations. The GGE biplot highlighted that genotype g8 (JG-24) exhibited better seed index in environment e3 (Raipur), while genotype g1 (ICCV 201210) performed well in environment e1 (Bhatapara). Genotype g2 (ICCV 201103) demonstrated a good seed index in all three environments. The AMMI analysis indicated that genotype g3 (ICCV 201112) had a slightly lower seed index but maintained stability across all environments. Overall, the findings of this investigation, as represented by the GGE and AMMI biplots, visually illustrated the relationship between genotypes and environments. The identified genotypes hold potential for improving yield production and further research and breeding efforts can be based on these findings.

Genetic Diversity and Population Structure of Maize (Zea mays L.) Inbred Lines in Association with Phenotypic and Grain Qualitative Traits Using SSR Genotyping

Maize (Zea mays L.) is an important cereal and is affected by climate change. Therefore, the production of climate-smart maize is urgently needed by preserving diverse genetic backgrounds through the exploration of their genetic diversity. To achieve this, 96 maize inbred lines were used to screen for phenotypic yield-associated traits and grain quality parameters. These traits were studied across two different environments (Anand and Godhra) and polymorphic simple sequence repeat (SSR) markers were employed to investigate the genetic diversity, population structure, and trait-linked association. Genotype–environment interaction (GEI) reveals that most of the phenotypic traits were governed by the genotype itself across the environments, except for plant and ear height, which largely interact with the environment. The genotypic correlation was found to be positive and significant among protein, lysine and tryptophan content. Similarly, yield-attributing traits like ear girth, kernel rows ear−1, kernels row−1 and number of kernels ear−1 were strongly correlated to each other. Pair-wise genetic distance ranged from 0.0983 (1820194/T1 and 1820192/4-20) to 0.7377 (IGI-1101 and 1820168/T1). The SSRs can discriminate the maize population into three distinct groups and shortlisted two genotypes (IGI-1101 and 1820168/T1) as highly diverse lines. Out of the studied 136 SSRs, 61 were polymorphic to amplify a total of 131 alleles (2–3 per loci) with 0.46 average gene diversity. The Polymorphism Information Content (PIC) ranged from 0.24 (umc1578) to 0.58 (umc2252). Similarly, population structure analysis revealed three distinct groups with 19.79% admixture among the genotypes. Genome-wide scanning through a mixed linear model identifies the stable association of the markers umc2038, umc2050 and umc2296 with protein, umc2296 and umc2252 with tryptophan, and umc1535 and umc1303 with total soluble sugar. The obtained maize lines and SSRs can be utilized in future maize breeding programs in relation to other trait characterizations, developments, and subsequent molecular breeding performances for trait introgression into elite genotypes.

Influence of arsenic exposure and TGF-β gene single nucleotide polymorphisms (gene-environment interaction) on cardiovascular risk biomarkers levels in Mexican people from San Luis Potosi, Mexico

Influence of arsenic exposure and TGF-β gene single nucleotide polymorphisms (gene-environment interaction) on cardiovascular risk biomarkers levels in Mexican people from San Luis Potosi, Mexico

GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and factor-analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA, which integrates geographic information systems (GIS) techniques, FA models, partial least squares (PLS) regression, and enviromics to predict phenotypic performance in untested environments. The GIS-FA approach enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.

Genetic and epigenetic interactions in adaptive thermogenesis pathways in association with obesity from a Public Health Genomics perspective

Background: studying and apprehending the pathways and mechanisms by which overweight and obesity trigger complex disease progression is of prime importance for the development of therapy and prevention measures of this major public health burden. This review describes Single Nucleotide Polymorphisms (SNPs) and epigenetic methylation as well as histone modification in genes with relevance in adaptive thermogenesis and their possible role in the development of obesity. Epigenetic marks are discussed as solid biomarkers for gene-environment interactions. Methods: a PubMed search on genetic and epigenetic variation of genes involved in adaptive thermogenesis was performed. The search included English publications between December 1996 and July 2010 reporting associations between SNPs and obesity in Caucasians. The search on epigenetic regulation was limited to DNA methylation and histone modifications. Genes that were found to be associated with the adaptive thermogenesis pathway included beta-3 adrenergic receptor (ADRB3), uncoupling protein 1 (UCP1), the transcription factors peroxisome proliferated activator receptor gamma (PPARγ), peroxisome proliferated activator receptor gamma co-activator 1 alpha (PGC1α), retinoid acid X receptor alpha (RXRα), CCAAT/enhancer-binding protein alpha (C/EBPα), fatty acid binding protein 4 (FABP4) and lipoprotein lipase (LPL). Results: epigenetic studies are mainly discovery orientated and do not test hypotheses. However, SNPs as well as epigenetic mechanisms seem to regulate obesity and adaptive thermogenesis whereas genetic association studies are inconsistent. Conclusions: the aim of this work was to confirm evidences on the contribution of genetic variations as well as epigenetic regulatory mechanisms of genes associated to obesity. The integration of epigenetic markers in epidemiologic research could help to unravel multi gene-environment interactions. With this article we show the importance to introduce aspects of the epigenetic regulation in the assessment of obesity: We also discuss the benefits of including epigenomics as an integrative way to account for an individual’s environmental impact in public health policies.

A Matched Case-Control Study of the Serum Level and Gene Expression of IL-6 and IL-12 in Patients with Schizophrenia Spectrum Disorder: An Azeri Acute Phase/Recent-Onset Psychosis Survey (ARAS) Study

Background: The causes of schizophrenia spectrum disorders are believed to be multifactorial, with both genetic and environmental factors, as well as gene-environment interactions, influencing the course of this disorder. Research suggests that immunological factors may play a role in the development of psychotic disorders. Objectives: The aim of this study is to evaluate and compare the serum levels of interleukin-6 (IL-6) and interleukin-12 (IL-12), as well as the expression levels of IL-6 and IL-12 genes, between an Iranian Azeri population of patients with first-episode psychosis and healthy controls. Methods: Forty patients who had recently experienced first-episode non-affective psychosis were allocated to the patient group. Forty healthy volunteers, matched for age and gender, were also recruited as controls. The study was conducted in 2020 in Tabriz, Iran. Peripheral blood samples were collected (using the Ficoll-Paque process) and assessed to determine the expression levels of IL-6 and IL-12 mRNA (using real-time Polymerase Chain Reaction-PCR- system), as well as the serum levels of IL-6 and IL-12 (with a sandwich ELISA kit). GraphPad Prism Version 6.0 was used for all statistical analyses. Results: Each group consisted of 40 participants in this study. Patients had significantly higher levels of IL-6 mRNA expression compared to the healthy controls (P &lt; 0.0001), while no significant difference was found between the two groups in terms of IL-12 mRNA expression (P = 0.5697). A similar pattern was reported for the serum levels of IL-6 (P &lt; 0.0001) and IL-12 (P = 0.0777). The area under the curve (AUC), which indicates the accuracy of the predictive model, was 0.9669 for the level of IL-6 mRNA expression (P &lt; 0.0001). This Area (AUC) was 0.9584 for the serum concentration of IL-6 (P &lt; 0.0001). The levels of IL-6 or IL-12 were not correlated with the severity of symptoms, gender, or age of the patients. Conclusions: The expression level of IL-6 mRNA and the serum concentration of IL-6 were altered in patients with first-episode non-affective psychosis. This finding supports the role of inflammatory pathways in the pathogenesis of psychosis. According to our results, measurement of the circulating level of IL-6 may be useful in distinguishing patients with first-episode psychosis from healthy individuals.

Genetic gains in forage sorghum for adaptive traits for non - conventional area through multi-trait-based stability selection methods.

In the Asian tropics, unpredictable weather increases the risk of abiotic stresses in sorghum areas, making it harder to meet predicted demand. Genotype-by environment interaction (GEI) and the lack of an effective multi-trait-based selection approach make it challenging to breed climateresilient forage sorghum that adapts to nonconventional areas. The present investigation carried out to estimate genetic parameters, inter trait associations, genetic gain under selection (SGs) of 95 diverse forage sorghum genotypes. Fourteen forage yield and other secondary traits were evaluated at five different growing seasons at two locations. Negative and positive genetic gains under selection were estimated across different growing seasons including Kharif, Rabi and Summer in the year 2020 and 2021. The GEI effects were significant (P < 0.001) for all the studied traits. The multi trait based stability indices have been said to assist breeders in ensuring sustained progress in primary traits likeforage yield without sacrificing genetic advancement in secondary traits. Fourteen genotypes were selected through each evaluation methods including genotype - ideotype distance index (MGIDI), multi-trait stability index (MTSI), multi-trait stability and mean performance (MTMPS) and multi-trait index based on factor analysis and genotype-ideotype distance (FAIBLUP Index), assuming 15% selection intensity. According to MGIDI, the selected genotypes exhibited desired positive genetic gains for dry forage yield per plant, inter-nodal length, green forage yield per plant, and plant height and negative genetic gains for days to 50% flowering. The strength and weakness plot is a potential graphical tool as portrayed by MGIDI, to identify and develop desirable genotype for particular environment. Two genotypes, G36 (302B) and G89 (348B) were found to be common across all four evaluation methods based on all the studied traits. Multi-trait stability evaluation approaches are reliable and accessible for selecting multiple traits under varied testing environments with low multicollinearity issues. These tools proved effective in enhancing selection strategies and optimising breeding schemes for the development of climate-resilient forage sorghum genotypes. The aforementioned genotypes were found to be the most reliable, high-yielding, and earlymaturing and could be suggested for variety and hybrid development and ideotype breeding programmes to ensure the food and nutritional security.

Associations between disturbed sleep and attenuated psychotic experiences in people at clinical high risk for psychosis.

Pre-diagnostic stages of psychotic illnesses, including 'clinical high risk' (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis. Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population. Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa. In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population.

Genetics of chronic respiratory disease.

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.

Ethics of early detection of disease risk factors: A scoping review

BackgroundScientific and technological advancements in mapping and understanding the interrelated pathways through which biological and environmental exposures affect disease development create new possibilities for detecting disease risk factors. Early detection of such risk factors may help prevent disease onset or moderate the disease course, thereby decreasing associated disease burden, morbidity, and mortality. However, the ethical implications of screening for disease risk factors are unclear and the current literature provides a fragmented and case-by-case picture.MethodsTo identify key ethical considerations arising from the early detection of disease risk factors, we performed a systematic scoping review. The Scopus, Embase, and Philosopher’s Index databases were searched for peer-reviewed, academic records, which were included if they were written in English or Dutch and concerned the ethics of (1) early detection of (2) disease risk factors for (3) disease caused by environmental factors or gene-environment interactions. All records were reviewed independently by at least two researchers.ResultsAfter screening 2034 titles and abstracts, and 112 full papers, 55 articles were included in the thematic synthesis of the results. We identified eight common ethical themes: (1) Reliability and uncertainty in early detection, (2) autonomy, (3) privacy, (4) beneficence and non-maleficence, (5) downstream burdens on others, (6) responsibility, (7) justice, and (8) medicalization and conceptual disruption. We identified several gaps in the literature, including a relative scarcity of research on ethical considerations associated with environmental preventive health interventions, a dearth of practical suggestions on how to address expressed concerns about overestimating health capacities, and a lack of insights into preventing undue attribution of health responsibility to individuals.ConclusionsThe ethical concerns arising with the early detection of risk factors are often interrelated and complex. Comprehensive ethical analyses are needed that are better embedded in normative frameworks and also assess and weigh the expected benefits of early risk factor detection. Such research is necessary for developing and implementing responsible and fair preventive health policies.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings.

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.