Gene-environment Interactions Research Articles

Identification of High Erucic Acid Brassica carinata Genotypes through Multi-Trait Stability Index

Brassica carinata is an important and native oilseed crop in Ethiopia. The seed oil from B.carinata attracts global attention for its various industrial applications, mainly due to its high erucic acid levels and its superior agronomic traits. Since the demand for high erucic acid from oilseed brassica has been increasing in the world market due to its wider applications in bio-industries, the breeding target of B. carinata has recently been focused on enhancing its erucic acid. Several high erucic acid B. carinata genotypes have been screened from the pre-breeding activities. Such genotypes, however, need to be tested for their stable performance, for their erucic acid level, and other desirable traits under different environments. The aim of this study was to identify high erucic acid B. carinata genotypes with stable performance in multiple desirable traits. Thirty-two B. carinata genotypes were grown in a randomized complete block design with three replications at three locations for two years. The genotypes were evaluated for nine desirable traits related to seed oil quality (erucic acid and oil content), seed yield, and other agronomic traits. The results showed that the proportion of genotype by environment interaction (GEI) was clearly observed in erucic acid, which led to a stability and mean performance analysis for selecting the most stable and best-performing genotypes for the desired traits. For such an analysis, we used the multi-trait stability index (MTSI) along with the weighted average of absolute score BLUPs (WAASB). As revealed from the MTSI, five genotypes (G13, G18, G10, G22 and G5) were identified as the most stable in erucic acid, oil content, seed yield, and other agronomic traits. The selected genotypes showed on average 45.7% erucic acid, 3185 kg ha−1 seed yield and 45.1% oil content with 4.3%, 25.8% and 6.9% positive selection gain, respectively. The negative selection gain of phonological traits and the plant height of the selected genotypes revealed their early maturity and their lower probability of being affected by lodging. Our findings demonstrated MTSI can be used to select high erucic acid B. carinata with a set of desirable traits, which would facilitate breeding efforts in developing novel and high erucic acid B. carinata varieties. Our results also showed that MTSI is an effective tool for selecting genotypes across different environments due to its unique ability to select multiple traits simultaneously.

Application of AMMI and GGE biplot for genotype by environment interaction and yield stability analysis in potato genotypes grown in Dawuro zone, Ethiopia

The performance of most crop genotypes is greatly influenced by the interactions between genotype and environment. Providing information on well-adapted and high-yielding potato genotypes in a given environment is paramount for small-holder farmers to enhance their productivity. The study aimed to understand GEI effect on tuber yield, and identify and select widely or specifically adapted high-yielding potato genotypes for production. Eleven genotypes were evaluated using a randomized complete block design with three replications. The results of the AMMI analysis of variance showed that tuber yield significantly (p ≤ 0.05) influenced genotype-environment interaction. This reveals that genotypes exhibit varying mean performance in tuber yield across different environments. The sum squared results showed that genotype (62.40 %) and environment (26.73 %) were the main contributors to tuber yield variation, while the genotype-environment interaction effect (10.87 %) contributed least to then tuber yield total variation. The AMMI, GGE biplot, and GSI analysis revealed Gudanie and Gorebella as superior genotypes in tuber yield, demonstrating high mean performance across tested environments. Thus, Gudanie and Gorebella have been chosen as the most broadly adaptable genotypes for production in all potato-growing agroecologies in the Dawuro zone. The GGE and AMMI biplot provided the genotype-by-environment interaction association among environments and genotypes.

Genetic Factors Contributing to Respiratory Disorders in Brachycephalic Dog Breeds in Uganda

Purpose: To aim of the study was to analyze the genetic factors contributing to respiratory disorders in brachycephalic dog breeds. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Genetic factors are primary contributors to respiratory disorders in brachycephalic dog breeds, known for their flattened facial structure. Breeds like Bulldogs and Pugs exhibit narrowed nostrils, elongated soft palates, and other airway obstructions due to selective breeding for these distinctive traits. These anatomical abnormalities restrict airflow, leading to symptoms such as noisy breathing and exercise intolerance. Unique Contribution to Theory, Practice and Policy: Genetic inheritance theory, evolutionary genetics theory & genotype-environment interaction theory may be used to anchor future studies on genetic factors contributing to respiratory disorders in brachycephalic dog breeds. Implement breeding programs that prioritize genetic health over physical appearance. Establish and enforce regulatory standards for breeding brachycephalic dogs that include mandatory genetic testing for respiratory disorder markers.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis.

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Stability analysis of male sterile-derived pearl millet (Pennisetum glaucum) hybrids under variable growing condition

The identification and deployment of high-yielding pearl millet [Pennisetum glaucum (L.) R.Br.] hybrids adapted to various stress agro-ecologies are crucial for enhancing food and nutrition security in northern India. A study was carried out during the rainy (kharif) seasons of 2019 and 2020 at CCS Haryana Agricultural University, Hisar, Haryana in different growing situations (early-sown and late-sown patterns and rainfed to irrigated conditions) to investigate the genotype-environment interaction (GEI) of 59 pearl millet restorer lines, male sterile lines and their derived hybrids. Pooled analysis of variance for genotype, environment and GEI was significant for days to 50% flowering, seed yield and harvest index. These results indicate the significant differences among the genotypes, various environments and response of GEI. By combining the results of regression analysis and additive main effects and multiplicative interaction (AMMI) biplot, the genotypes HMS58 A1×EMRL-14/105, HMS54 A5×EMRL-14/127 and EMRL-15/109 exhibited high mean seed yield and high stability across environments. Among the tested environments, irrigated late-sown condition for seed yield and harvest index; and rainfed early-sown condition for days to 50% flowering had the highest discrimination ability. Hence, this study can help for the grouping of pearl millet potential hybrids and allow multi-year trials for identifying the best genotypes in the northern India.

Comparative Field Evaluation and Transcriptome Analysis Reveals that Chromosome Doubling Enhances Sheath Blight Resistance in Rice

Rice sheath blight, caused by Rhizoctonia solani Kihn (R. solani), poses a significant threat to rice production and quality. Autotetraploid rice, developed through chromosome doubling of diploid rice, holds great potential for enhancing biological and yield traits. However, its resistance to sheath blight in the field has remained unclear. In this study, the field resistance of 35 autotetraploid genotypes and corresponding diploids was evaluated across three environments from 2020 to 2021. The booting stage was optimal for inoculating period based on the inoculation and analysis of R. solani at five rice growth stages. We found autotetraploids generally exhibited lower disease scores than diploids, indicating enhanced resistance after chromosome doubling. Among the 35 genotypes, 16 (45.71%) displayed increased resistance, 2 (5.71%) showed decreased resistance, and 17 (48.57%) displayed unstable resistance in different sowing dates. All combinations of the genotype, environment and ploidy, including the genotype-environment-ploidy interaction, contributed significantly to field resistance. Chromosome doubling increased sheath blight resistance in most genotypes, but was also dependent on the genotype-environment interaction. To elucidate the enhanced resistance mechanism, RNA-seq revealed autotetraploid recruited more down-regulated differentially expressed genes (DEGs), additionally, more resistance-related DEGs, were down-regulated at 24 h post inoculation in autotetraploid versus diploid. The ubiquinone/terpenoid quinone and diterpenoid biosynthesis pathways may play key roles in ploidy-specific resistance mechanisms. In summary, our findings shed light on the understanding of sheath blight resistance mechanisms in autotetraploid rice.

Gene-environment interaction in expertise acquisition: Practice effects on musical expertise vary by polygenic scores for cognitive performance

Expert performance is associated with practice, partly because of causal effects of practice on skill (i.e., learning). However, the practice-expertise association is also influenced by a complex interplay between genes and environment including partly overlapping genetic influences. The importance of cognitive ability in the practice-expertise association is less well understood. Therefore, we first examined whether genetic predisposition for cognitive performance, operationalized as a polygenic score, is associated with music practice and expertise. Next, we tested whether there is evidence for gene × environment interaction, i.e., whether effects of practice on expertise differ depending on an individual's genetic predisposition for cognitive performance. Polygenic scores for cognitive performance (PGScp) and multi-trait cognitive performances, including educational attainment and mathematical performances (PGScps) were calculated for approximately 3800 genotyped Swedish individuals with information available on their cumulative amount of music practice, musical achievement, and musical auditory discrimination. We found that higher PGScp and PGScps were associated with higher levels of achievement, musical auditory discrimination, and more practice, although the association with practice weakened when controlling for education. Music practice was linked to both expertise outcomes, and the effect sizes of these associations varied depending on an individual's PGScp and PGScps (with the exception of PGScp for musical auditory discrimination). These results suggest genetic pleiotropy between cognitive performance and musical expertise. Additionally, they reveal the presence of G × E interaction in skill acquisition, as effects of practice on musical expertise are stronger for individuals with a higher genetic predisposition for cognitive performance.

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

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

Geographical adaptability for optimizing the recommendation of soybean cultivars in the Brazilian Cerrado

Yield multi-location trials associated to geostatistical techniques with environmental covariables can provide a better understanding of G x E interactions and, consequently, adaptation limits of soybean cultivars. Thus, the main objective of this study is understanding the environmental covariables effects on soybean adaptation, as well as predicting the adaptation of soybean under environmental variations and then recommend each soybean cultivar to favorable environments aiming maximize the average yield. The trials were carried out in randomized block design (RBD) with three replicates over three years, in 28 locations. Thirty-two genotypes (commercial and pre-commercial) representing different maturity groups (7.5–8.5) were evaluated in each trial were covering the Edaphoclimatic Region (REC) 401, 402 and 403. The covariables adopted as environmental descriptors were accumulated rainfall, minimum temperature, mean temperature, maximum temperature, photoperiod, relative humidity, soil clay content, soil water avaibility and altitude. After fitting means through Mixed Linear Model, the Regression-Kriging procedure was applied to spacialize the grain yield using environmental covariables as predictors. The covariables explained 32.54% of the GxE interaction, being the soil water avaibility the most important to the adaptation of soybean cultivars, contributing with 7.80%. Yield maps of each cultivar were obtained and, hence, the yield maximization map based on cultivar recommendation was elaborated.

Using eco‐evolutionary models to improve management of introgression in brown trout

AbstractThe management of intraspecific diversity in many species is usually disconnected from eco‐evolutionary processes happening in natura. A classic example is embodied in the problem of introgression in hybridized fish populations, wherein management practices are generally unaware of any selective process at work, and therefore generally rely on numbers (adding or removing individuals) to reduce introgression. Such an example can be observed in the French Alps, where native Mediterranea (MED) brown trout have been highly introgressed through decades of stocking with domesticated Atlantic (ATL) brown trout. Recently however, new results shed light on a potential selective mechanism that may affect differentially the fitness of MED and ATL genes depending on the environment (GxE interaction). Using a demogenetic agent‐based model able to account for such GxE interaction, we simulate a management scenario implemented in 2005 by some biodiversity managers and scientists, who attempted to restore the Mediterranea gene pool using translocation of near pure MED individuals in Atlantic‐dominated areas. We show that the model is unable to recreate the observed introgression dynamics if the GxE interaction is not included. This finding implies that (i) eco‐evolutionary mechanisms can have large effects on introgression dynamics on very short time scales and (ii) management of intraspecific diversity should increasingly rely on these natural mechanisms, so as to improve management targets and facilitate adaptation to rapid environmental changes.

Genomic analysis of genotype-environment interaction in age at first calving of Murrah buffaloes.

Age at first calving (AFC) is a measure of sexual maturity associated with the start of productive life of dairy animals. Additionally, a lower AFC reduces the generation interval and early culling of females. However, AFC has low heritability, making it a trait highly influenced by environmental factors. In this scenario, one way to improve the reproductive performance of buffalo cows is to select robust animals according to estimated breeding value (EBV) using models that include genotype-environment interaction (GEI) with the application of reaction norm models (RNMs). This can be achieved by understanding the genomic basis related to GEI of AFC. Thus, in this study, we aimed to predict EBV considering GEI via the RNM and identify candidate genes related to this component in dairy buffaloes through genome-wide association studies (GWAS). We used 1795 AFC records from three Murrah buffalo herds and formed environmental gradients (EGs) from contemporary group solutions obtained from genetic analysis of 270-day cumulative milk yield. Heritability estimates ranged from 0.15 to 0.39 along the EG. GWAS of the RNM slope parameter identified important genomic regions. The genomic window that explained the highest percentage of genetic variance of the slope (0.67%) was located on BBU1. After functional analysis, five candidate genes were detected, involved in two biological processes. The results suggested the existence of a GEI for AFC in Murrah buffaloes, with reclassification of animals when different environmental conditions were considered. The inclusion of genomic information increased the accuracy of breeding values for the intercept and slope of the reaction norm. GWAS analysis suggested that important genes associated with the AFC reaction norm slope were possibly also involved in biological processes related to lipid metabolism and immunity.

Emerging Role of Environmental Epitranscriptomics and RNA Modifications in Parkinson's Disease.

Environmental risk factors and gene-environment interactions play a critical role in Parkinson's disease (PD). However, the relatively large contribution of environmental risk factors in the overwhelming majority of PD cases has been widely neglected in the field. A "PD prevention agenda" proposed in this journal laid out a set of research priorities focused on preventing PD through modification of environmental risk factors. This agenda includes a call for preclinical studies to employ new high-throughput methods for analyzing transcriptomics and epigenomics to provide a deeper understanding of the effects of exposures linked to PD. Here, we focus on epitranscriptomics as a novel area of research with the potential to add to our understanding of the interplay between genes and environmental exposures in PD. Both epigenetics and epitranscriptomics have been recognized as potential mediators of the complex relationship between genes, environment, and disease. Multiple studies have identified epigenetic alterations, such as DNA methylation, associated with PD and PD-related exposures in human studies and preclinical models. In addition, recent technological advancements have made it possible to study epitranscriptomic RNA modifications, such as RNA N6-methyladenosine (m6A), and a handful of recent studies have begun to explore epitranscriptomics in PD-relevant exposure models. Continued exploration of epitranscriptomic mechanisms in environmentally relevant PD models offers the opportunity to identify biomarkers, pre-degenerative changes that precede symptom onset, and potential mitigation strategies for disease prevention and treatment.

Gene-environment interactions between clonal hematopoiesis of indeterminate potential and air pollution in non-small cell lung cancer among non-smokers.

10514 Background: Small particulate matter air pollution (≤ 2.5µm diameter; PM2.5) is a recognized driver of non-small cell lung cancer (NSCLC) among non-smokers. PM2.5 recruits pro-inflammatory macrophages to the lungs, which facilitate the clonal expansion of mutated epithelial cells and eventual transformation to NSCLC. Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related condition caused by the acquisition of somatic mutations in hematopoietic stem cells. CHIP and its accompanying pro-inflammatory phenotype have been linked with various age-related diseases, including incident NSCLC; however, the mechanisms underlying this relationship are unknown. In this study, we aimed to explore the interaction between CHIP and exposure to PM2.5 in the development of incident NSCLC. Methods: This study was conducted using data from participants in the UK Biobank (n = 451,095). CHIP status was determined from peripheral blood whole exome sequencing data, and defined as the presence of a somatic driver mutation in the blood at variant allele frequency (VAF) ≥2% (PMID: 36652671). Incident NSCLC was determined from UK cancer registry data, and PM2.5 exposure was determined based on ambient regional measures from 2010. Cox proportional hazard models were used to evaluate associations between CHIP, PM2.5, and NSCLC. Proteomics data was measured using OLINK proteomics in a subset of UK Biobank participants (n = 44,625). Results: CHIP was prevalent in 3.4% of UK Biobank participants (n = 15,633; never smokers: 3.0%, n = 6,916/227,133). There were 1983 incident cases of NSCLC (never smokers: n = 307). CHIP status was associated with incident NSCLC when adjusting for smoking status (HR = 1.73, 95% CI: 1.48–2.02) and analyzing exclusively non-smokers (HR = 2.01, 95% CI: 1.34-3.00). PM2.5 levels were not associated with NSCLC in non-smokers (HR = 1.00, 95% CI: 0.89–1.12 per µg/m3 increase in PM2.5); however, there was a significant interaction between PM2.5 and CHIP (HR = 1.46, 95% CI: 1.02–2.11, pint = 0.04), suggesting that it is the interplay between the two that drives incident NSCLC risk. We found no association between PM2.5 levels and increased CHIP prevalence or CHIP VAF. PM2.5 and CHIP were also found to interact to increase systemic inflammatory markers C-reactive peptide (pint = 0.01) and IL-6 (pint = 0.002). Conclusions: PM2.5 and CHIP act as a novel gene x environment interaction pair that play a key role in NSCLC etiology among non-smokers. Rather than acting in isolation to increase risk of NSCLC, PM2.5 intensifies the relationship between CHIP and NSCLC, presumably by exacerbating systemic inflammation and the hyper-inflammatory lung microenvironment. With PM2.5 levels in the UK among the lowest in the world, we posit that the CHIP x PM2.5 interaction contributes substantially to the global burden of NSCLC in non-smokers.

A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits

Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.

Maternal behaviours disrupted by Gprasp2 deletion modulate neurodevelopmental trajectory in progeny

Autism spectrum disorders (ASDs) are known to present sex-specific differences. At the same time, understanding how maternal behaviours are affected by pathogenic mutations is crucial to translate research efforts since rearing may recursively modulate neurodevelopment phenotype of the progeny. In this work, we focused on the effects of Gprasp2 deletion in females and its impact in progeny care and development. Female mice, wild-type (WT), Gprasp2+/− (HET) or Gprasp2−/− (KO) mutants and their progeny were used and behavioural paradigms targeting anxiety, memory, maternal care, and other social behaviours were performed. Analysis of communication was carried out through daily recordings of ultrasonic vocalizations in isolated pups and cross-fostering experiments were performed to understand the effect of maternal genotype in pup development. We found that Gprasp2−/− females presented striking impairments in social and working memory. Females also showed disruptions in maternal care, as well as physiological and molecular alterations in the reproductive system and hypothalamus, such as the structure of the mammary gland and the expression levels of oxytocin receptor (OxtR) in nulliparous versus primiparous females. We observed alterations in pup communication, particularly a reduced number of calls in Gprasp2 KO pups, which resulted from an interaction effect of the dam and pup genotype. Cross-fostering mutant pups with wild-type dams rescued some of the early defects shown in vocalizations, however, this effect was not bidirectional, as rearing WT pups with Gprasp2−/− dams was not sufficient to induce significant phenotypical alterations. Our results suggest Gprasp2 mutations perturb social and working memory in a sex-independent manner, but impact female-specific behaviours towards progeny care, female physiology, and gene expression. These changes in mutant dams contribute to a disruption in early stages of progeny development. More generally, our results highlight the need to better understand GxE interactions in the context of ASDs, when female behaviour may present a contributing factor in postnatal neurodevelopmental trajectory.

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Genotype by environment interaction and stability study in bread wheat (Triticum aestivum L.) genotypes in Guji Zone, Southern Ethiopia

Variability of polygenic prediction for body mass index in Africa

BackgroundPolygenic prediction studies in continental Africans are scarce. Africa’s genetic and environmental diversity pose a challenge that limits the generalizability of polygenic risk scores (PRS) for body mass index (BMI) within the continent. Studies to understand the factors that affect PRS variability within Africa are required.MethodsUsing the first multi-ancestry genome-wide association study (GWAS) meta-analysis for BMI involving continental Africans, we derived a multi-ancestry PRS and compared its performance to a European ancestry-specific PRS in continental Africans (AWI-Gen study) and a European cohort (Estonian Biobank). We then evaluated the factors affecting the performance of the PRS in Africans which included fine-mapping resolution, allele frequencies, linkage disequilibrium patterns, and PRS-environment interactions.ResultsPolygenic prediction of BMI in continental Africans is poor compared to that in European ancestry individuals. However, we show that the multi-ancestry PRS is more predictive than the European ancestry-specific PRS due to its improved fine-mapping resolution. We noted regional variation in polygenic prediction across Africa’s East, South, and West regions, which was driven by a complex interplay of the PRS with environmental factors, such as physical activity, smoking, alcohol intake, and socioeconomic status.ConclusionsOur findings highlight the role of gene-environment interactions in PRS prediction variability in Africa. PRS methods that correct for these interactions, coupled with the increased representation of Africans in GWAS, may improve PRS prediction in Africa.

Joint Modeling of Gene-Environment Correlations and Interactions Using Polygenic Risk Scores in Case-Control Studies.

Polygenic risk scores (PRS) are rapidly emerging as a way to measure disease risk by aggregating multiple genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally using simulated case-control data sets within the UK Biobank study, we demonstrate the power of our method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

Genotype by environment interaction and performance stability of common bean (Phaseolus vulgaris L.) cultivars grown in Dawuro zone, Southwestern Ethiopia

Abstract The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Gene-environment interactions in human health.

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.