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Purpose of Review This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits. Recent Findings Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. Summary The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.

Aims/hypothesis The aim of this study was to assess the interaction between genetic risk and lifestyle intervention on the occurrence of gestational diabetes mellitus (GDM) and postpartum diabetes. Methods The RADIEL study is an RCT aimed at prevention of GDM and postpartum diabetes through lifestyle intervention. Participants with a BMI ≥30 kg/m2 and/or prior GDM were allocated to intervention and control groups before pregnancy or in early pregnancy. The study visits took place every 3 months before pregnancy, once in each trimester, and at 6 weeks and 6 and 12 months postpartum. We calculated a polygenic risk score (PRS) based on 50 risk variants for type 2 diabetes. Results Altogether, 516 participants provided genetic and GDM data. The PRS was associated with higher glycaemic levels (fasting glucose and/or HbA1c) and a lower insulin secretion index in the second and third trimesters and at 12 months postpartum, as well as with a higher occurrence of GDM and glycaemic abnormalities at 12 months postpartum (n = 356). There was an interaction between the PRS and lifestyle intervention (p=0.016 during pregnancy and p=0.024 postpartum) when analysing participants who did not have GDM at the first study visit during pregnancy (n = 386). When analysing women in tertiles according to the PRS, the intervention was effective in reducing the age-adjusted occurrence of GDM only among those with the highest genetic risk (OR 0.37; 95% CI 0.17, 0.82). The risk of glycaemic abnormalities at 12 months postpartum was reduced in the same group after adjusting additionally for BMI, parity, smoking and education (OR 0.35; 95% CI 0.13, 0.97). Conclusions/interpretation Genetic predisposition to diabetes modifies the response to a lifestyle intervention aimed at prevention of GDM and postpartum diabetes. This suggests that lifestyle intervention may benefit from being tailored according to genetic risk. Clinical trial registration ClinicalTrials.gov identifier: NCT01698385 Graphical abstract

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

The Dog Aging Project is a long-term longitudinal study of ageing in tens of thousands of companion dogs. The domestic dog is among the most variable mammal species in terms of morphology, behaviour, risk of age-related disease and life expectancy. Given that dogs share the human environment and have a sophisticated healthcare system but are much shorter-lived than people, they offer a unique opportunity to identify the genetic, environmental and lifestyle factors associated with healthy lifespan. To take advantage of this opportunity, the Dog Aging Project will collect extensive survey data, environmental information, electronic veterinary medical records, genome-wide sequence information, clinicopathology and molecular phenotypes derived from blood cells, plasma and faecal samples. Here, we describe the specific goals and design of the Dog Aging Project and discuss the potential for this open-data, community science study to greatly enhance understanding of ageing in a genetically variable, socially relevant species living in a complex environment.

BACKGROUND: Interleukin 6 (IL-6) is a moderately heritable pleiotropic cytokine whose elevated concentrations in coronary artery disease, peripheral arterial disease, pulmonary arterial hypertension, Eales’ disease, Sjògren’s syndrome, osteoarthritis, adenocarcinoma, neuroblastoma, polymyalgia rheumatica, pulmonary tuberculosis, and enterovirus 71-infection, and following coronary artery bypass graft, show larger genetic effects than in unaffected low IL-6 controls. We hypothesize that genetic effects may depend upon whether average IL-6 concentrations are high or low, i.e., quantile-dependent expressivity. METHOD: Quantile-specific offspring-parent (β(OP)) and full-sib regression slopes (β(FS)) were estimated by applying quantile regression to the age- and sex-adjusted serum IL-6 concentrations in families surveyed in the Framingham Heart Study. Quantile-specific heritabilities were calculated as h(2)=2β(OP)/(1+r(spouse)) and h(2)={(1+8r(spouse)β(FS))(0.5)-1}/(2r(spouse))). RESULTS: Heritability (h(2)±SE) of IL-6 concentrations increased from 0.01±0.01 at the 10(th) percentile (NS), 0.02±0.01 at the 25(th) (P =0.009), 0.03±0.01 at the 50(th) (P =0.007), 0.04±0.02 at the 75(th) (P =0.004), and 0.13±0.05 at the 90(th) percentile (P =0.03), or 0.0005±0.0002 for each one-percent increase in the offspring’s phenotype distribution (P(linear trend)=0.02) when estimated from β(OP), and from 0.02±0.02 at the 10(th) (NS), 0.02±0.02 at the 25(th) (NS), 0.06±0.02 at the 50(th) (P =0.01), 0.12±0.04 at the 75(th) (P =0.001), and 0.30±0.03 at the 90(th) percentile (P

BACKGROUND: Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. METHODS: We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, ξ, for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. RESULTS: We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N=11,586) and individual genetic variants in the UK Biobank (N=380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. DISCUSSION: We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether specific GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Background Genome-wide association studies (GWAS) have identified some variants associated with subclinical atherosclerosis (SCA) in general population but lacking sufficient validation. Besides traditional risk factors, whether and how would genetic variants associate with SCA among people with HIV (PWH) remains to be elucidated. Method A large original GWAS and gene-environment interaction analysis of SCA were conducted among Chinese PWH (n = 2850) and age/sex-matched HIV-negative controls (n = 5410). Subgroup analyses by age and functional annotations of variants were also performed. Results Different from HIV-negative counterparts, host genome had a greater impact on young PWH rather than the elders: one genome-wide significant variant (rs77741796, P = 2.20 × 10−9) and eight suggestively significant variants (P −6) were identified to be specifically associated with SCA among PWH younger than 45 years. Seven genomic loci and 15 genes were mapped to play a potential role on SCA among young PWH, which were enriched in the biological processes of atrial cardiac muscle cell membrane repolarization and molecular function of protein kinase A subunit binding. Furthermore, genome-wide interaction analyses revealed significant HIV-gene interactions overall as well as gene-environment interactions with alcohol consumption, tobacco use and obesity among PWH. The identified gene-environment interaction on SCA among PWH might be useful for discovering high-risk individuals for the prevention of SCA, particularly among those with tobacco use and alcohol consumption. Conclusion The present study provides new clues for the genetic contribution of SCA among young PWH and is the starting point of precision intervention targeting HIV-related atherosclerosis.

Introduction Thermotolerant microbes are a group of microorganisms that survive in elevated temperatures. The thermotolerant microbes, which are found in geothermal heat zones, grow at temperatures of or above 45°C. The proteins present in such microbes are optimally active at these elevated temperatures. Hence, therefore, serves as an advantage in various biotechnological applications. In the last few years, scientists have tried to understand the molecular mechanisms behind the maintenance of the structural integrity of the cell and to study the stability of various thermotolerant proteins at extreme temperatures. Proteomic analysis is the solution for this search. Applying novel proteomic tools determines the proteins involved in the thermostability of microbes at elevated temperatures. Methods Advanced proteomic techniques like Mass spectrometry, nano-LC-MS, protein microarray, ICAT, iTRAQ, and SILAC could enable the screening and identification of novel thermostable proteins. Results This review provides up-to-date details on the protein signature of various thermotolerant microbes analyzed through advanced proteomic tools concerning relevant research articles. The protein complex composition from various thermotolerant microbes cultured at different temperatures, their structural arrangement, and functional efficiency of the protein was reviewed and reported. Conclusion This review provides an overview of thermotolerant microbes, their enzymes, and the proteomic tools implemented to characterize them. This article also reviewed a comprehensive view of the current proteomic approaches for protein profiling in thermotolerant microbes.

The vascular blood–brain barrier is a highly regulated interface between the blood and brain. Its primary function is to protect central neurons while signaling the presence of systemic inflammation and infection to the brain to enable a protective sickness behavior response. With increasing degrees and duration of systemic inflammation, the vascular blood–brain barrier becomes more permeable to solutes, undergoes an increase in lymphocyte trafficking, and is infiltrated by innate immune cells; endothelial cell damage may occasionally occur. Perturbation of neuronal function results in the clinical features of encephalopathy. Here, the molecular and cellular anatomy of the vascular blood–brain barrier is reviewed, first in a healthy context and second in a systemic inflammatory context. Distinct from the molecular and cellular mediators of the blood–brain barrier’s response to inflammation, several moderators influence the direction and magnitude at genetic, system, cellular and molecular levels. These include sex, genetic background, age, pre-existing brain pathology, systemic comorbidity, and gut dysbiosis. Further progress is required to define and measure mediators and moderators of the blood–brain barrier’s response to systemic inflammation in order to explain the heterogeneity observed in animal and human studies.

In multisite forest genetic experiments, the presence of genotype × environment interaction (GEI) is common. GEI may negatively affect the estimates of genetic variance and hamper selection decisions in tree breeding programs. Several measures exist to evaluate the stability of tested genotypes’ performance across environments with a choice of the method likely affecting breeders’ decisions. In this study, we evaluated variation in diameter and height growth performance in the progeny test established at 4 sites with 80 open-pollinated half-sib families of Scots pine. We found significant variation among examined progeny at age 10, reaching up to 31% for diameter and 20% for height depending on site, and significant GEI in both traits. We estimated contribution of each family to GEI using various methods and tools of GEI analysis—AMMI, GGE biplots, heterogeneity of regression coefficients (bi’s), the deviation mean squares from regression (s2di) and Kang’s yield-stability index (YSi). Despite the presence of the cross-over interaction, family ranks did not vary much among sites. The selections based on the phenotype, YSi and restricted bi corresponded well to each other leading to the expected response to selection up to 7.8% on diameter and 4.4% on height, whereas those based on the AMMI stability variance were different and lead to a slight loss in both traits. We discuss our results in the context of the usefulness of those measures of genotype stability for tree breeding programs and propose the procedure to follow for making selection decisions in forest experiments with GEI.

Research suggests that genetic variants linked to monoaminergic neurotransmitter function moderate the association between stress and anxiety symptoms, but examining gene-environment (G × E) interactions with individual genes limits power. As one of polygenetic approaches, the multilocus genetic profile score is derived theoretically from combining the effects of multiple candidate genes based on the “biological plausibility”. Using this approach, the current study examined the interaction between monoaminergic multilocus genetic variants and stressful life events on the changes in adolescent anxiety symptoms across a one-year timespan. In a Chinese Han adolescent sample which was derived from three vocational high schools (N = 587; T1: Mage = 16.47 ± 1.53 years; 50.8%, girls), the monoaminergic multilocus genetic profile score was calculated using 5-HTR2C rs6318, TPH2 rs4570625 and DRD2 rs1800497 polymorphisms. Results showed that this monoaminergic multilocus genetic profile score interacted with stressful life events in predicting changes in anxiety symptoms. Consistent with the G×E hypothesis of differential susceptibility, adolescents with more monoaminergic plasticity alleles not only suffered more from high levels of stressful life events, which increased the risk for anxiety symptoms, but also benefited more from low levels of stressful life events, which decreased the risk for anxiety symptoms. There were no significant G × E interactions when individual polymorphisms were examined in isolation. The results highlight the importance of examining aggregated influences of multiple genes in G × E interactions underlying the longitudinal development of adolescent anxiety symptoms.

The objective of this study was to assess genotype by environment interaction for 21 physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids grown in Rodasht Agricultural Research Station in Iran by the additive main effects and multiplicative interaction model. The study comprised of 51 sugar beet genotypes [10 multigerm pollen parents, four monogerm seed parents and 36 F1 hybrids], evaluated at four environments in a randomized complete block design, with three replicates. The additive main effects and multiplicative interaction analyses revealed significant environment main effects with respect to all observed traits, except extraction coefficient of sugar. The additive main effects and multiplicative interaction stability values ranged from 0.009 (G17 for leaf Ca2+) to 9.698 (G09 for extraction coefficient of sugar). The parental forms 2 7233-P.29 (G38) and C CMS (G49) as well as hybrids 2(6)*C (G27) and 5*C (G33) are recommended for further inclusion in the breeding programs because of their stability and good average values of observed traits.

The International Maize and Wheat Improvement Center develops and annually distributes elite wheat lines to public and private breeders worldwide. Trials have been created in multiple sites over many years to assess the lines' performance for use in breeding and release as varieties, and to provide iterative feedback on refining breeding strategies1. The collaborator test sites are experiencing climate change, with new implications for how wheat genotypes are bred and selected2. Using a standard quantitative genetic model to analyse four International Maize and Wheat Improvement Center global spring wheat trial datasets, we examine how genotype-environment interactions have changed over recent decades. Notably, crossover interactions-a critical indicator of changes in the ranking of cultivar performance in different environments-have increased over time. Climatic factors explained over 70% of the year-to-year variability in crossover interactions for yield. Yield responses of all lines in trial environments from 1980 to 2018 revealed that climate change has increased the ranking change in breeding targeted to favourable environments by ~15%, while it has maintained or reduced the ranking change in breeding targeted to heat and drought stress by up to 13%. Genetic improvement has generally increased crossover interactions, particularly for wheat targeted to high-yielding environments. However, the latest wheat germplasm developed under heat stress was better adapted and more stable, partly offsetting the increase in ranking changes under the warmer climate.

Epigenome-wide studies report higher methylation among women than men with decreasing levels with age. Little is known about associations of sex and age with methylation of monoamine oxidase A (MAOA). Methylation of the first exonic and partial first intronic region of MAOA has been shown to strengthen associations of interactions of MAOA-uVNTR genotypes and adversity with aggression and substance misuse. Our study examined associations of sex and age with MAOA first exon and intron methylation levels in 252 women and 157 men aged 14–73 years. Participants included adolescents recruited at a substance misuse clinic, their siblings and parents, and healthy women. Women showed ~ 50% higher levels of exonic, and ~ 15% higher intronic, methylation than men. Methylation levels were similar between younger (M = 22.7 years) and older (M = 46.1 years) participants, and stable across age. Age modified few associations of methylation levels with sex. MAOA genotypes modified few associations of methylation with sex and age. Higher methylation levels among women were not explained by genotype, nor interaction of genotype and sexual abuse. Findings were similar after adjusting for lifetime diagnoses of substance dependence (women = 24.3%; men = 34.2%). Methylation levels were higher among women who experienced sexual abuse than women who did not. Results extend on prior studies by showing that women display higher levels of methylation than men within first intronic/exonic regions of MAOA, which did not decrease with age in either sex. Findings were not conditioned by genotype nor interactions of genotype and trauma, and indicate X-chromosome inactivation.

Rubber clones’ evaluation involves assessment of latex yield and performance stability across multiple locations. Clones with high yield and stability have the potential for wide planting across country, while unstable clones with high yield can be exploited in certain localities. Forty-five rubber clones were evaluated in five locations across Malaysia in the period between 2000 and 2011. There was a significant genotype by environment interaction effect on rubber yield, accounting 26.67% of total variation. Assessment of locations suitability for clone trial led to discovery of two mega-environments, which we predicted to form due to the level of agronomic inputs. Using the data from nine clones, PB 260 was shown to give the best performance compared to other clones in a low input environment. Two methods of stability analyses were used viz. linear regression and mean and coefficient of variation. The former method gave more confidence in the classification of stable clones due to its more systematic and stringent approach. Six clones were classified as stable, i.e., PB 374, PB 371, RRIM 2004, PB 260, RRIM 2001, and RRIM 2002, while three clones render more evaluation to confirm their stability, namely, RRIM 2007, RRIM 3001 and RRIM 2024. Four clones that were shown unstable but high yielding have the potential for planting in targeted region and these are RRIM 2005, PB 373, RRIM 2023 and PB 369.

Under the climate change scenario, high-temperature stress declines crop yields. The present study was designed with the objectives to identify the stable heat-tolerant genotypes for grain yield under normal and heat stress conditions at three locations. The 80 mung bean genotypes and 3 check varieties were sown in three agro-ecological zones of Pakistan, namely, Chakwal, Piplan, and Bhakkar. The experiments were planted in two sowing dates, i.e., the normal sowing and late sowing by using an augmented randomized block design with 6 blocks at each location. The plot size for each genotype was 2.4 m2 at all the locations. All cultural and agronomic practices were carried out as per recommendations. Ten randomly selected plants were used to record the yield-related traits. An infrared gas analyzer (IRGA) was used to measure physiological traits like photosynthesis rate and stomatal conductance. To study the nature of relationship among traits, Pearson correlation analysis was done using SPSS software (v. 16.0). The GGE biplots were constructed to graphically represent the genotype and GE interaction of multi-environment data using the software plant breeding tools. Three views of the GGE biplot were generated to display the comparative performance, rank, and stability of each genotype in the tested environments. Correlation and principal component analysis (PCA) revealed a positive interaction of grain yield with pods plant−1, seed pod−1, 100 grain weight, and harvest index, while days to flowering and maturity had a negative association under normal and heat-stressed conditions. The GGE biplot analysis efficiently explained 85.1% of the total variation and demonstrated that among 8 vertex genotypes of polygon biplot, the G14 and G38 were heat tolerant as these genotypes outclassed others in all the heat-stressed environments. In contrast, the genotypes G51 and G74 produced maximum yield under normal conditions (E3 and E5). Among all the genotypes, genetic diversity was observed for grain yield and other morpho-physiological traits. Significant G × E interaction was observed. The genotypes G14, G38, and G51 were identified as stable and heat tolerant in most of the environments. These findings will be helpful for development of heat-tolerant mung bean breeding material in the future.

Externalizing behavior is substantially affected by genetic effects, which are moderated by environmental exposures. However, little is known about whether these moderation effects differ depending on individual characteristics, and whether moderation of environmental effects generalizes across different environmental domains. With a large sample (N = 1,441 individuals) of early adolescent twins (ages 11 and 13), using a longitudinal multi-informant design, we tested interaction effects between negative emotionality and both positive and negative aspects of three key social domains: parents, peers, and schools, on the phenotypic variance as well as the etiology of externalizing. Negative emotionality moderated some of the environmental effects on the phenotypic, genetic, and environmental variance in externalizing, with adolescents at both ends of the negative emotionality distribution showing different patterns of sensitivity to the tested environmental influences. This is the first use of gene-environment interaction twin models to test individual differences in environmental sensitivity, offering a new approach to study such effects.

Breeding for quality traits in tomato has got boost due to enormous consumer and industrial demand owing to its exceptional nutritional benefits and attributes required for processing industries. Newly evolved varieties/hybrids need recommendation based on their adaptive specificity as a consequence of genotype-environment interaction. Hence, present study analysed comparative stability of 30 hybrids of tomato along with one check hybrid (To 3150) for important quality traits viz., total soluble solids (TSS), titrable acidity, ascorbic acid, carotene and lycopene content based on data of three locations over two years. The identified stable hybrids were Columbia × EC 538380, H 86 × EC 620541 and EC 620541 × EC 620438) for TSS, Superbug × EC 620438 for titrable acidity, Columbia × EC 620438 for ascorbic acid and NDTVR 73 × EC 620541 for carotene content. None of the hybrids was found stable for lycopene content. Stability for one trait was independent of stability of other traits. The responsive hybrids were Columbia × BS 24–2 (for TSS and carotene content), EC 521087 × EC 538380 (for ascorbic acid and carotene content), EC 620438 × EC 538380 (for ascorbic acid, carotene and lycopene content). The environments of Varanasi during both the years had favourable effect on all quality traits except carotene content as evident from environmental indices. The hybrids identified as stable are broad-adapted and can be recommended for general cultivation under different locations while the hybrids identified as responsive can be recommended for specific environments only.

The interplay between light receptors and PHYTOCHROME-INTERACTING FACTORs (PIFs) serves as a regulatory hub that perceives and integrates environmental cues into transcriptional networks of plants(1,2). Although occupancy of the histone variant H2A.Z and acetylation of histone H3 have emerged as regulators of environmentally responsive gene networks, how these epigenomic features interface with PIF activity is poorly understood(3–7). By taking advantage of rapid and reversible light-mediated manipulation of PIF7 subnuclear localization and phosphorylation, we simultaneously assayed the DNA-binding properties of PIF7, as well as its impact on chromatin dynamics genome wide. We found that PIFs act rapidly to reshape the H2A.Z and H3K9ac epigenetic landscape in response to a change in light quality. Furthermore, we discovered that PIFs achieve H2A.Z removal through direct interaction with EIN6 ENHANCER (EEN), the Arabidopsis thaliana homolog of the chromatin remodeling complex subunit INO80 Subunit 6 (Ies6). Thus, we describe a PIF–INO80 regulatory module that is an intermediate step for allowing plants to change their growth trajectory in response to environmental changes.

Cane yield and juice quality traits are essential and important in release of sugarcane variety for commercial cultivation. Twenty elite sugarcane clones along with two popular standards (Co 86032 and PI 1110) were evaluated at Sakthi sugars, Sivaganga, in two plant crops and one ratoon crop for 2 years to identify a variety suitable for this location. Cane yield (t/ha), commercial cane sugar (CCS) yield (t/ha), CCS % at harvest, juice sucrose %, juice brix %, juice purity %, single cane weight (kg), stalk height (cm) and stalk diameter (cm) at harvest were considered for the study. Within environment analysis of variance (ANOVA) indicated significant differences among genotypes for almost all the traits in all three environments (two plant and one ratoon crops). Combined ANOVA results displayed non-significant genotype environment interaction for all the traits except height and diameter. Cane yield and sugar yield were significant higher in II (second) plant crop compared to I (first) plant and ratoon crops, whereas no significance was observed for other traits. Heritability estimates were higher for juice quality traits as compared to cane traits. Single cane weight recorded highest estimates for repeatability and heritability of means over harvests. Best linear unbiased predictions were estimated for all the traits through restricted maximum likelihood method for individual crops and pooled data. The clone Co 09004 recorded high estimates for quality traits, and clone Co 11015 was found to be superior for both yield and quality traits.

Background Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated. Results Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight. Conclusions Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress.

The serotonin-transporter-linked promoter region (5-HTTLPR) has been widely investigated as contributing to depression vulnerability. Nevertheless, empirical research provides wide contrasting findings regarding its involvement in the etiopathogenesis of the disorder. Our hypothesis was that such discrepancy can be explained considering time as moderating factor. We explored this hypothesis, exploiting a meta analytic approach. We searched PubMed, PsychoINFO, Scopus and EMBASE databases and 1096 studies were identified and screened, resulting in 22 studies to be included in the meta-analyses. The effect of the 5-HTTLPR x stress interaction on depression risk was found to be moderated by the following temporal factors: the duration of stress (i.e. chronic vs. acute) and the time interval between end of stress and assessment of depression (i.e. within 1 year vs. more than 1 year). When stratifying for the duration of stress, the effect of the 5-HTTLPR x stress interaction emerged only in the case of chronic stress, with a significant subgroup difference (p = 0.004). The stratification according to time interval revealed a significant interaction only for intervals within 1 year, though no difference between subgroups was found. The critical role of time interval clearly emerged when considering only chronic stress: a significant effect of the 5-HTTLPR and stress interaction was confirmed exclusively within 1 year and a significant subgroup difference was found (p = 0.01). These results show that the 5-HTTLPR x stress interaction is a dynamic process, producing different effects at different time points, and indirectly confirm that s-allele carriers are both at higher risk and more capable to recover from depression. Overall, these findings expand the current view of the interplay between 5-HTTLPR and stress adding the temporal dimension, that results in a three-way interaction: gene x environment x time.

Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p −9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p −7), of which 132 are independent (p ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.

We examined interactions among genetic, biological, and ecological variables predicting externalizing behaviors in preschool and middle childhood. Specifically, we examined prediction of externalizing behaviors from birth complications and negative emotionality, each moderated by genetic risk for aggression and ecological risk factors of insensitive parenting and low family income. At ages 4 and 5 years, 170 twin pairs and 5 triplet sets (N = 355 children) were tested; 166 of those children were tested again at middle childhood (M = 7.9 years). Multilevel linear modeling results showed generally that children at high genetic risk for aggression or from low-income families were likely to have high scores on externalizing, but for children not at high risk, those with increased birth complications or more negative emotionality had high scores on externalizing. This study underscores the importance of considering biological variables as moderated by both genetic and ecological variables as they predict externalizing behaviors across early childhood.

Key message Integrating genomics technologies and breeding methods to tweak core parameters of the breeder’s equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Abstract Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers’ fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder’s equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder′s equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

Compared to peer alcohol use, less is known on how parenting practices may modify genetic and environmental contributions to alcohol use longitudinally across adolescence. This study examined whether two maternal parenting behaviors, supervision and harsh parenting, may suppress or amplify genetic and environmental influences on three distinct developmental trajectories of adolescent alcohol use: normative increasing, early-onset, and low trajectories. Participants were drawn from a longitudinal study of a population-based twin sample (N = 842, 84% European descent, 52.7% female). Adolescents self-reported their past year alcohol use at ages 13, 14, 15, and 17 years, and their mothers reported their supervision and harsh parenting when twins were 13, 15, and 17 years old. Maternal supervision amplified non-shared environmental influence on the normative increasing and early-onset trajectories, whereas maternal harsh parenting amplified shared environmental influence on the early-onset trajectory and non-shared environmental influence on the low trajectory, respectively. The findings suggest maternal parenting practices as a potent developmental context that modulates the environmental influences of other proximal processes on adolescent alcohol use, and suggest that family-based parenting-focused intervention could be especially beneficial for adolescents following the early-onset trajectory.

The studies investigating gene-gene and gene-environment (or gene-behavior) interactions provide valuable insight into the pathomechanisms underlying obese phenotypes. The Pakistani population due to its unique characteristics offers numerous advantages for conducting such studies. In this view, the current study was undertaken to examine the effects of gene-gene and gene-environment/behavior interactions on the risk of obesity in a sample of Pakistani population. A total of 578 adult participants including 290 overweight/obese cases and 288 normal-weight controls were involved. The five key obesity-associated genetic variants namely MC4R rs17782313, BDNF rs6265, FTO rs1421085, TMEM18 rs7561317, and NEGR1 rs2815752 were genotyped using the TaqMan allelic discrimination assays. The data related to behavioral factors, such as eating pattern, diet consciousness, the tendency toward fat-dense food (TFDF), sleep duration, sleep-wake cycle (SWC), shift work (SW), and physical activity levels were collected via a questionnaire. Gene-gene and gene-behavior interactions were analyzed by multifactor dimensionality reduction and linear regression, respectively. In our study, only TMEM18 rs7561317 was found to be significantly associated with anthropometric traits with no significant effect of gene-gene interactions were observed on obesity-related phenotypes. However, the genetic variants were found to interact with the behavioral factors to significantly influence various obesity-related anthropometric traits including BMI, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and percentage of body fat. In conclusion, the interaction between genetic architecture and behavior/environment determines the outcome of obesity-related anthropometric phenotypes. Thus, gene-environment/behavior interaction studies should be promoted to explore the risk of complex and multifactorial disorders, such as obesity.

Spot blotch, an important foliar fungal disease caused by Bipolaris sorokiniana (Sacc.) Shoemaker [stat. anam.], is the influential production restraint over the global wheat-growing regions. A set of eight diverse wheat genotypes including a few breeding lines from Uttar Banga Krishi Viswavidyalaya and commonly accepted susceptible cultivars were screened for spot blotch disease and grain yield across five different locations in West Bengal, India, under environments for natural disease infestation in post-rainy seasons of 2018–2019 and 2019–2020. The present investigation revealed significant variation among the genotypes for spot blotch resistance in different environments as a result of genotype main effect and genotype environment interaction. This study also recorded two genotypes as resistant against spot blotch disease over the five locations. Identified resistant genotypes increased grain yield as a consequence of resistance offered against spot blotch disease suggesting the possibility of considering disease resistance as a must-have trait in all the wheat cultivars that could be identified for cultivation in the North-Eastern Plain Zone of India. Two genotypes resistant to spot blotch recorded 1.26–1.46 times higher grain yield over control under disease pressure that can be recommended for identification and exploited in a resistance breeding program for wheat spot blotch disease-prone region.

OBJECTIVE: To study the correlation between SNPs at phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) rs9838117 site, erb-b2 receptor tyrosine kinase 2 (ERBB2) rs1058808 site, and their interactions with environmental factors and the epithelial ovarian cancer (EOC) risk. METHODS: Sanger sequencing was used to analyze the genotypes of PIK3CA rs9838117 and ERBB2 rs1058808 site in 587 patients with epithelial ovarian cancer (EOC). Multi-factor dimensionality reduction (MDR) was applied to analyze the interaction between PIK3CA rs9838117 and ERBB2 rs1058808 site and the clinical data. RESULTS: The risk of EOC in T allele carriers at PIK3CA rs9838117 was 1.95 times (95%CI: 1.55–2.46, P

Key message We propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations. Abstract Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. Linear mixed modelling can provide predictions for new locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. To account for correlations between zones, as well as for intercepts and slopes for the regression on covariates, we fitted random coefficient (RC) models. The results showed that the RC model with appropriate covariate scaling and model for covariate terms improved the precision of predictions of genotypic performance for new locations. The prediction accuracy of the RC model was competitive compared to the model without covariates. The RC model reduced the standard errors of predictions for individual genotypes and standard errors of predictions of genotype differences in new locations by 30–38% and 12–40%, respectively.

The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.

B cell acute lymphoblastic leukaemia (B-ALL) is the most common form of childhood cancer. Although treatment has advanced remarkably in the past 50 years, it still fails in ~20% of patients. Recent studies revealed that more than 5% of healthy newborns carry preleukaemic clones that originate in utero, but only a small percentage of these carriers will progress to overt B-ALL. The drivers of progression are unclear, but B-ALL incidence seems to be increasing in parallel with the adoption of modern lifestyles. Emerging evidence shows that a major driver for the conversion from the preleukaemic state to the B-ALL state is exposure to immune stressors, such as infection. Here, we discuss our current understanding of the environmental triggers and genetic predispositions that may lead to B-ALL, highlighting lessons from epidemiology, the clinic and animal models, and identifying priority areas for future research., Research at C.C.’s laboratory was partially supported by the European Regional Development Fund (FEDER)/MINECO (SAF2017-83061-R), Fundación Ramón Areces and a research contract with Fundación Síndrome de Wolf-Hirschhorn o 4p-. Institutional grants from Fundación Ramón Areces and Banco de Santander to Centro de Biología Molecular Severo Ochoa are also acknowledged. The C.C. and C.V.-D. laboratories are members of the EU COST Action LEGEND (CA16223). Research in the C.V.-D. group is partially supported by an FEDER Miguel Servet grant (CPII19/00024 — AES 2017-2020) from Instituto de Salud Carlos III (Ministerio de Economía y Competitividad), Fondo de Investigaciones Sanitarias/Instituto de Salud Carlos III (PI17/00167). Research in the I.S.-G. group is partially supported by the FEDER and by MINECO/FEDER (SAF2015-64420-R), MCIU/AEI/FEDER (RTI2018-093314-B-I00), Junta de Castilla y León (UIC-017, CSI001U16, CSI234P18 and CSI144P20), the German Carreras Foundation (DJCLS 07R/2019) and Fundacion Unoentrecienmil (CUNINA project). The I.S.-G. laboratory is a member of EuroSyStem and the DECIDE Network funded by the European Union under the Seventh Framework Programme. C.V.-D. and I.S.-G. have been supported by the German Federal Office for Radiation Protection (BfS) (FKZ: 3618S32274).

Recent studies consider lifestyle risk score (LRS), an aggregation of multiple lifestyle exposures, in identifying association of gene-lifestyle interaction with disease traits. However, not all cohorts have data on all lifestyle factors, leading to increased heterogeneity in the environmental exposure in collaborative meta-analyses. We compared and evaluated four approaches (Naive, Safe, Complete and Moderator Approaches) to handle the missingness in LRS-stratified meta-analyses under various scenarios. Compared to "benchmark" results with all lifestyle factors available for all cohorts, the Complete Approach, which included only cohorts with all lifestyle components, was underpowered due to lower sample size, and the Naive Approach, which utilized all available data and ignored the missingness, was slightly inflated. The Safe Approach, which used all data in LRS-exposed group and only included cohorts with all lifestyle factors available in the LRS-unexposed group, and the Moderator Approach, which handled missingness via moderator meta-regression, were both slightly conservative and yielded almost identical p values. We also evaluated the performance of the Safe Approach under different scenarios. We observed that the larger the proportion of cohorts without missingness included, the more accurate the results compared to "benchmark" results. In conclusion, we generally recommend the Safe Approach, a straightforward and non-inflated approach, to handle heterogeneity among cohorts in the LRS based genome-wide interaction meta-analyses.

In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing $$r^2$$ r 2 values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ($$r^2=.05$$ r 2 = . 05 ), standard errors of these standardized estimates are reasonable ($$< .05$$ < . 05 ) for $$n=16K$$ n = 16 K trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., $$r^2>.025)$$ r 2 > . 025 ) as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS’s on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.

Cowpea is one of the most important indigenous food and forage legumes in Africa. It serves as a primary source of protein for poor farmers in drought-prone areas of Ethiopia. The crop is used as a source of food, and insurance crop during the dry season. Cowpea is adaptable to a wide range of climatic conditions. Despite this, the productivity of the crop is generally low due to lack of stable and drought tolerant varieties. In this study, 25 cowpea genotypes were evaluated in five environments using a triple lattice design during the 2017 and 2018 main cropping seasons. The objectives of this study were to estimate the magnitude of genotype by environment interaction (GEI) and grain yield stability of selected drought tolerant cowpea genotypes across different environments. The additive main effect and multiplicative interaction (AMMI) model indicated the contribution of environment, genotype and GEI as 63.98 6%, 2.66% and 16.30% of the total variation for grain yield, respectively. The magnitudes of the GEI sum of squares were 6.12 times that of the genotypes for grain yield. The IPCA1, IPCA2 and IPCA3 were all significant and explained 45.47%, 28.05% and 16.59% of the GEI variation, respectively. The results from AMMI, cultivar superior measure (Pi), genotype plus genotype-by-environment (GGE) biplot yield stability index (YSI), and AMMI stability value (ASV) analyses identified NLLP-CPC-07-145-21, NLLP-CPC-103-B and NLLP_CPC-07-54 as stable and high yielding genotypes across environments. Thus, these genotypes should be recommended for release for production for drought prone areas. NLLP-CPC-07-143, Kanketi and CP-EXTERETIS were the least stable. The AMMI1 biplot showed that Jinka was a high potential and favorable environment while Babile was an unfavorable environment for cowpea production.

We investigated gene–environment effects on structural brain endophenotype in bipolar disorder (BD) using a novel method of combining polygenic risk scores with epigenetic signatures since traditional methods of examining the family history and trauma effects have significant limitations. The study enrolled 119 subjects, including 55 BD spectrum (BDS) subjects diagnosed with BD or major depressive disorder (MDD) with subthreshold BD symptoms and 64 non-BDS subjects comprising 32 MDD subjects without BD symptoms and 32 healthy subjects. The blood samples underwent genome-wide genotyping and methylation quantification. We derived polygenic risk score (PRS) and methylation profile score (MPS) as weighted summations of risk single nucleotide polymorphisms and methylation probes, respectively, which were considered as molecular measures of genetic and environmental risks for BD. Linear regression was used to relate PRS, MPS, and their interaction to 44 brain structure measures quantified from magnetic resonance imaging (MRI) on 47 BDS subjects, and the results were compared with those based on family history and childhood trauma. After multiplicity corrections using false discovery rate (FDR), MPS was found to be negatively associated with the volume of the medial geniculate thalamus (FDR = 0.059, partial R2 = 0.208). Family history, trauma scale, and PRS were not associated with any brain measures. PRS and MPS show significant interactions on whole putamen (FDR = 0.09, partial R2 = 0.337). No significant gene–environment interactions were identified for the family history and trauma scale. PRS and MPS generally explained greater proportions of variances of the brain measures (range of partial R2 = [0.008, 0.337]) than the clinical risk factors (range = [0.004, 0.228]).

As cardio metabolic disease manifestations tend to cluster in families there is a need to better understand the underlying mechanisms in order to further develop preventive strategies. In fact, genetic markers used in genetic risk scores, important as they are, will not be able alone to explain these family clusters. Therefore, the search goes on for the so called missing heritability to better explain these associations. Shared lifestyle and social conditions in families, but also early life influences may be of importance. Gene-environmental interactions should be explored. In recent years interest has grown for the role of diet-microbiota associations, as microbiota patterns may be shared by family members. In the Malmö Offspring Study that started in 2013, we have so far been able to examine about 4700 subjects (18–71 years) representing children and grandchildren of index subjects from the first generation, examined in the Malmö Diet Cancer Study during 1991 to 1996. This will provide rich data and opportunities to analyse family traits of chronic disease across three generations. We will provide extensive genotyping and phenotyping including cardiovascular and respiratory function, as well as markers of glucose metabolism. In addition, also cognitive function will be assessed. A 4-day online dietary recall will be conducted and gut as well as oral microbiota analysed. The ambition is to provide one of the first large-scale European family studies with individual data across three generations, which could deepen our knowledge about the role of family traits for chronic disease and its underlying mechanisms. Electronic supplementary material The online version of this article (10.1007/s10654-020-00695-4) contains supplementary material, which is available to authorized users.

The effects of genetic and environmental factors on Pinus koraiensis growth were studied based on a 35 year-old progeny trial composed of open-pollinated offspring of twenty-one plus trees. Height, DBH and volume of the offspring was analyzed using restricted maximum likelihood/best linear unbiased prediction in mixed model analysis. Significant site and family effects on the three traits were observed. The distinct growth of offspring by site with disparate climates corroborated the importance of planting species in suitable conditions. Growth differences by family was significant, emphasizing the importance of identifying families with either superior or inferior performance. The parental ranking was assigned in the sites, inferring the breeding value of each plus tree. The estimates of individual heritability ( $$\widehat{{h}_{i}^{2}}$$ ) of height, DBH and volume growth were 0.169–0.645, 0.108–0.331 and 0.129–0.343 respectively, with higher $$\widehat{{h}_{i}^{2}}$$ of the height than DBH on each site. Coefficient of variance of genetic effect was higher with DBH in some cases, indicating the scope for selection is larger for this trait despite the lower heritability compared to height. For the variation between families in terms of the performance stability across sites, consideration of the genotype by environment interaction is required in selecting materials to be used in reforestation with Korean pine. A few families with either superior or inferior performance retained their parental ranking for at least a decade. Other families with increased growth on a particular site were identified, indicating their high breeding value and low stability. Differences in the genetic performance of the families by site requires delineation of the breeding region of the species.

Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder resulting from both genetic and environmental risk factors, is manifested by deficits in cognitive function. Elucidating the cognitive disorder-relevant biological mechanisms may open up promising therapeutic approaches. In this work, we mined ASD cognitive phenotype proteins to construct and analyze protein–protein and gene–environment interaction networks. Incorporating the protein–protein interaction (PPI), human cognition proteins, and connections of autism-cognition proteins enabled us to generate an autism-cognition network (ACN). With the topological analysis of ACN, important proteins, highly clustered modules, and 3-node motifs were identified. Moreover, the impact of environmental exposures in cognitive impairment was investigated through chemicals that target the cognition-related proteins. Functional enrichment analysis of the ACN-associated modules and chemical targets revealed biological processes involved in the cognitive deficits of ASD. Among the 17 identified hub-bottlenecks in the ACN, PSD-95 was recognized as an important protein through analyzing the module and motif interactions. PSD-95 and its interacting partners constructed a cognitive-specific module. This hub-bottleneck interacted with the 89 cognition-related 3-node motifs. The identification of gene–environment interactions indicated that most of the cognitive-related proteins interact with bisphenol A (BPA) and valproic acid (VPA). Moreover, we detected significant expression changes of 56 cognitive-specific genes using four ASD microarray datasets in the GEO database, including GSE28521, GSE26415, GSE18123 and GSE29691. Our outcomes suggest future endeavors for dissecting the PSD-95 function in ASD and evaluating the various environmental conditions to discover possible mechanisms of the different levels of cognitive impairment.

This study focused on the role of CLE1-CLE7 peptides as environmental mediators and indicated that root-induced CLE2 functions systemically in light-dependent carbohydrate metabolism in shoots. Plants sense environmental stimuli and convert them into cellular signals, which are transmitted to distinct cells and tissues to induce adequate responses. Plant hormones and small secretory peptides often function as environmental stress mediators. In this study, we investigated whether CLAVATA3/EMBRYO SURROUNDING REGION-RELATED proteins, CLE1-CLE7, which share closely related CLE domains, mediate environmental stimuli in Arabidopsis thaliana. Expression analysis of CLE1-CLE7 revealed that these genes respond to different environmental stimuli, such as nitrogen deprivation, nitrogen replenishment, cold, salt, dark, and sugar starvation, in a sophisticated manner. To further investigate the function of CLE2, we generated transgenic Arabidopsis lines expressing the β-glucuronidase gene under the control of the CLE2 promoter or expressing the CLE2 gene under the control of an estradiol-inducible promoter. We also generated cle2-1 and cle2-2 mutants using the CRISPR/Cas9 technology. In these transgenic lines, dark induced the expression of CLE2 in the root vasculature. Additionally, induction of CLE2 in roots induced the expression of various genes not only in roots but also in shoots, and genes related to light-dependent carbohydrate metabolism were particularly induced in shoots. In addition, cle2 mutant plants showed chlorosis when subjected to a shade treatment. These results suggest that root-induced CLE2 functions systemically in light-dependent carbohydrate metabolism in shoots.

We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an “omics” scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of “envirotypes” at multiple “enviromic” markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the “GIS–GEI”) which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.

Advances in molecular methods and the ability to share large population-based datasets are uncovering heterogeneity within diabetes types, and some commonalities between types. Within type 1 diabetes, endotypes have been discovered based on demographic (e.g. age at diagnosis, race/ethnicity), genetic, immunological, histopathological, metabolic and/or clinical course characteristics, with implications for disease prediction, prevention, diagnosis and treatment. In type 2 diabetes, the relative contributions of insulin resistance and beta cell dysfunction are heterogeneous and relate to demographics, genetics and clinical characteristics, with substantial interaction from environmental exposures. Investigators have proposed approaches that vary from simple to complex in combining these data to identify type 2 diabetes clusters relevant to prognosis and treatment. Advances in pharmacogenetics and pharmacodynamics are also improving treatment. Monogenic diabetes is a prime example of how understanding heterogeneity within diabetes types can lead to precision medicine, since phenotype and treatment are affected by which gene is mutated. Heterogeneity also blurs the classic distinctions between diabetes types, and has led to the definition of additional categories, such as latent autoimmune diabetes in adults, type 1.5 diabetes and ketosis-prone diabetes. Furthermore, monogenic diabetes shares many features with type 1 and type 2 diabetes, which make diagnosis difficult. These challenges to the current classification framework in adult and paediatric diabetes require new approaches. The ‘palette model’ and the ‘threshold hypothesis’ can be combined to help explain the heterogeneity within and between diabetes types. Leveraging such approaches for therapeutic benefit will be an important next step for precision medicine in diabetes. Since the first remedies for ‘honey urine’ were described over 25 centuries ago, untangling the pathways that lead to what we call today diabetes has enabled better treatments. The current classification of diabetes recognises the major forms of type 1 diabetes, type 2 diabetes, gestational diabetes and other less common types, such as monogenic diabetes (including neonatal diabetes and MODY), diseases of the exocrine pancreas (e.g. cystic fibrosis-related diabetes and pancreatogenic [or type 3c] diabetes) or drug-induced diabetes (Table 1) [1]. Advances in molecular biology and genetic methods during the past decades have facilitated the discovery of a myriad of genetic associations that suggest diverse pathophysiology within each of those types, as well as some commonalities between types. The recent acceleration in the ability to share large population-based datasets has further increased the evidence for heterogeneity within and between diabetes types [2]. Integrating our understanding of genetics, immunology and metabolic abnormalities in each type of diabetes will boost the ability to use precision medicine to tailor treatments and improve health outcomes.

Diel and seasonal oscillations are two major environmental changes in nature. While organisms cope with the former by the well-characterized mechanism of the circadian clock1,2, there is limited information on the molecular mechanisms underlying long-term responses to the latter3-5. Histone H3 lysine 27 trimethylation (H3K27me3), a repressive histone modification, imparts stability and plasticity to gene regulation during developmental transitions6-9. Here we studied the seasonal and diel dynamics of H3K27me3 at the genome-wide level in a natural population of perennial Arabidopsis halleri and compared these dynamics with those of histone H3 lysine 4 trimethylation (H3K4me3), an active histone modification. Chromatin immunoprecipitation sequencing revealed that H3K27me3 exhibits seasonal plasticity and diel stability. Furthermore, we found that the seasonal H3K27me3 oscillation is delayed in phase relative to the H3K4me3 oscillation, particularly for genes associated with environmental memory. Our findings suggest that H3K27me3 monitors past transcriptional activity to create long-term gene expression trends during organismal responses over weeks in natural fluctuating environments.

Depression and coronary heart disease (CHD) are prevalent and often co-occurring disorders. Both have been associated with a dysregulated stress system. As a central element of the stress system, the FKBP5 gene has been shown to be associated with depression. In a prospective design, this study aims to investigate the association of FKBP5 with depressive symptoms in CHD patients. N = 268 hospitalized CHD patients were included. Depressive symptoms were measured using the Hospital Anxiety and Depression Scale (HADS-D) at four time points (baseline, and after 1 month, 6 months, and 12 months). The functional FKBP5 single-nucleotide polymorphism (SNP) rs1360780 was selected for genotyping. Linear regression models showed that a higher number of FKBP5 C alleles was associated with more depressive symptoms in CHD patients both at baseline (p = 0.015) and at 12-months follow-up (p = 0.025) after adjustment for confounders. Further analyses revealed that this effect was driven by an interaction of FKBP5 genotype with patients’ prior CHD course. Specifically, only in patients with a prior myocardial infarction or coronary revascularization, more depressive symptoms were associated with a higher number of C alleles (baseline: p = 0.046; 1-month: p = 0.026; 6-months: p = 0.028). Moreover, a higher number of C alleles was significantly related to a greater risk for dyslipidemia (p = .016). Our results point to a relevance of FKBP5 in the association of the two stress-related diseases depression and CHD.

The Nile tilapia (Oreochromis niloticus) is a globally important aquaculture fish species, particularly in tropical and subtropical areas. However, aquaculture in many African countries including Ethiopia is not well developed. One of the main challenges for the development of aquaculture in Ethiopia is the lack of better performing Nile tilapia strain that can grow well in a wide range of production environments, and thus, it is crucial to develop fast growing Nile tilapia strain that can grow in a wide range of environments. Therefore, the main objectives of this study were to evaluate growth performance and genetic parameters such as heritability, additive genetic and genetic correlation of different strain combinations, and level of genotype by environment interaction (GxE) for harvest body weight (HBW) of 2409 fish from 81 full-sib families reared in high- (Hi-P) and low-input (Lo-P) pond-based production systems. The full-sib families were produced in a complete diallel cross of three Ethiopia Nile tilapia strains collected from Lake Chamo, Lake Koka, and Lake Ziway. After 5 months of rearing, HBW were measured and then phenotypic and genetic parameters were analyzed using univariate and bivariate animal models using SAS and ASReml softwares. The results of the study showed that the Chamo and the Koka strains and their crossbreds preformed significantly better than that of the Ziway strain and its crossbreds with the other two strains. The HBW of the fish obtained from the two production systems (111.4 g for Hi-P and 100.5 for Lo-P was not significantly different (P > 0.05).The heritability estimates from bivariate model were 0.23 and 0.20 for Hi-P and Lo-P, respectively. The heritability estimates of common full-sib for HBW of the fish reared in the Hi-P were higher (0.03) than found in the Lo-P (0.002). Re-ranking of the different strain combinations in the two production systems was observed. However, the high genetic correlation (0.95) and the non-significant (P > 0.05) GxE resulted in a weak re-ranking of strain combinations. In conclusion, the higher genetic correlation with low genotype by environment interaction between the harvest body weight of the fish reared in the two production systems indicated that environment-specific breeding program is not essential. However, the use of Hi-P prefereble.

Key message Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Abstract Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

Climate variability and associated droughts are major environmental factors limiting crop productivity worldwide. Identification and better understanding of factors influencing genotype × environment (GE) interaction in drought-prone environments will allow breeders and producers to improve crop productivity. The objectives of this study were to identify genotypic and climatic factors responsible for GE interactions in durum wheat yield trials under highland cold and moderate cold rainfed areas of Iran. Field experiments were carried out using 20 durum wheat genotypes, differing in growth habit, in three highland rainfed research stations for three cropping seasons (2015–2018). Environments were characterized by climatic co-variables measured during the three seasons. Genotypes were described by co-variables related to phenology, agronomy and disease resistance. Possible factors influencing GE interaction were examined by factorial regression (FR), partial least squares (PLS) regression and the additive main effects and multiplicative interaction (AMMI) models. The results indicated that both AMMI IPCA1 and IPCA2 scores had positive and negative values, resulting in crossover GE interaction and leading to inconsistent yield performance of genotypes across environments. The AMMI, PLS and FR identified similar genotypic and climatic variables that explained a large proportion of the GE interaction for grain yield. Based on the results, the climatic variables of average temperature, rainfall, relative humidity, minimum and maximum temperature, and genotypic variables of heading date, plant height and 1000-kernel weight were among the most important factors responsible for differential genotype adaptation. The PLS biplot showed that the genotypic responses to environmental covariates depended strongly on the growth habit type. This information can contribute toward the identification and biological understanding of adaptive characteristics relevant for durum wheat breeding in highland cold rainfed conditions of Iran and facilitate cultivar recommendation at eco-regional level. In conclusion, the AMMI, PLS and FR complement each other and the PLS biplot offered further interpretations of the GE interaction including differential genotype adaptation.

FGF2 is a neurotrophic factor that can act as a key regulatory molecule of neuroprotection, neurogenesis, and angiogenesis in various injuries. To explore the genetic background of the FGF2 gene on DPN development, this study analyzed the correlation between SNPs in the 3'UTR of the FGF2 gene and their interaction with environmental factors in DPN patients of Han Chinese nationality. Sanger sequencing was used to analyze the FGF2 genotypes at the rs1048201, rs3804158, rs41348645, rs6854081, rs3747676, rs7683093, rs1476215, and rs1476217 loci in 150 DPN patients, 150 NDPN patients, and 150 healthy control patients. Plasma FGF2 levels were measured in all subjects by using ELISAs. Subjects carrying the T allele at the rs1048201 locus in the FGF2 gene had a significantly lower risk of developing DPN compared with subjects carrying the C allele (OR = 0.43, 95% CI = 0.33–0.56, p

Drought stress is one of the major constraints affecting sorghum [Sorghum bicolor (L.) Moench] production in rain-fed regions of the Semi-Arid Tropics. Ethiopia is the suspected center of origin for sorghum and as such has tremendous variability for a wide range of traits. Ironically, there has been little systematic characterization of Ethiopian sorghum for traits of agronomic interest including drought tolerance. Hence, the objectives of this study were to evaluate the phenotypic diversity of 315 sorghum accessions (314 landraces plus 1 standard check) collected from farmers’ fields of diverse geographic regions in Ethiopia, and to identify potential sources of drought tolerance for future breeding programs. We phenotyped these accessions for ten agro-morphological traits at two water limited environments (Kobo and Mieso). Significant (p