Genome-Wide Contribution of Genotype by Environment Interaction to Variation of Diabetes-Related Traits

While genome-wide association studies (GWAS) and candidate gene approaches have identified many genetic variants that contribute to disease risk as main effects, the impact of genotype by environment (GxE) interactions remains rather under-surveyed. To explore the importance of GxE interactions for diabetes-related traits, a tool for Genome-wide Complex Trait Analysis (GCTA) was used to examine GxE variance contribution of 15 macronutrients and lifestyle to the total phenotypic variance of diabetes-related traits at the genome-wide level in a European American population. GCTA identified two key environmental factors making significant contributions to the GxE variance for diabetes-related traits: carbohydrate for fasting insulin (25.1% of total variance, P-nominal = 0.032) and homeostasis model assessment of insulin resistance (HOMA-IR) (24.2% of total variance, P-nominal = 0.035), n-6 polyunsaturated fatty acid (PUFA) for HOMA-β-cell-function (39.0% of total variance, P-nominal = 0.005). To demonstrate and support the results from GCTA, a GxE GWAS was conducted with each of the significant dietary factors and a control E factor (dietary protein), which contributed a non-significant GxE variance. We observed that GxE GWAS for the environmental factor contributing a significant GxE variance yielded more significant SNPs than the control factor. For each trait, we selected all significant SNPs produced from GxE GWAS, and conducted anew the GCTA to estimate the variance they contributed. We noted the variance contributed by these SNPs is higher than that of the control. In conclusion, we utilized a novel method that demonstrates the importance of genome-wide GxE interactions in explaining the variance of diabetes-related traits.     

Unifying Genetic Canalization,Genetic Constraint,and Genotype-by-Environment Interaction: QTL by Genomic Background by Environment Interaction of Flowering Time in Boechera stricta

Natural populations exhibit substantial variation in quantitative traits. A quantitative trait is typically defined by its mean and variance, and to date most genetic mapping studies focus on loci altering trait means but not (co)variances. For single traits, the control of trait variance across genetic backgrounds is referred to as genetic canalization. With multiple traits, the genetic covariance among different traits in the same environment indicates the magnitude of potential genetic constraint, while genotype-by-environment interaction (GxE) concerns the same trait across different environments. While some have suggested that these three attributes of quantitative traits are different views of similar concepts, it is not yet clear, however, whether they have the same underlying genetic mechanism. Here, we detect quantitative trait loci (QTL) influencing the (co)variance of phenological traits in six distinct environments in Boechera stricta, a close relative of Arabidopsis. We identified nFT as the QTL altering the magnitude of phenological trait canalization, genetic constraint, and GxE. Both the magnitude and direction of nFT''s canalization effects depend on the environment, and to our knowledge, this reversibility of canalization across environments has not been reported previously. nFT''s effects on trait covariance structure (genetic constraint and GxE) likely result from the variable and reversible canalization effects across different traits and environments, which can be explained by the interaction among nFT, genomic backgrounds, and environmental stimuli. This view is supported by experiments demonstrating significant nFT by genomic background epistatic interactions affecting phenological traits and expression of the candidate gene, FT. In contrast to the well-known canalization gene Hsp90, the case of nFT may exemplify an alternative mechanism: Our results suggest that (at least in traits with major signal integrators such as flowering time) genetic canalization, genetic constraint, and GxE may have related genetic mechanisms resulting from interactions among major QTL, genomic backgrounds, and environments.     

Genome-wide interaction of genotype by erythrocyte n-3 fatty acids contributes to phenotypic variance of diabetes-related traits

Background

Little is known about the interplay between n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level. The present study aimed to examine variance contributions of genotype by environment (GxE) interactions for different erythrocyte n-3 fatty acids and genetic variants for diabetes-related traits at the genome-wide level in a non-Hispanic white population living in the U.S.A. (n = 820). A tool for Genome-wide Complex Trait Analysis (GCTA) was used to estimate the genome-wide GxE variance contribution of four diabetes-related traits: HOMA-Insulin Resistance (HOMA-IR), fasting plasma insulin, glucose and adiponectin. A GxE genome-wide association study (GWAS) was conducted to further elucidate the GCTA results. Replication was conducted in the participants of the Boston Puerto Rican Health Study (BPRHS) without diabetes (n = 716).

Results

In GOLDN, docosapentaenoic acid (DPA) contributed the most significant GxE variance to the total phenotypic variance of both HOMA-IR (26.5%, P-nominal = 0.034) and fasting insulin (24.3%, P-nominal = 0.042). The ratio of arachidonic acid to eicosapentaenoic acid + docosahexaenoic acid contributed the most significant GxE variance to the total variance of fasting glucose (27.0%, P-nominal = 0.023). GxE variance of the arachidonic acid/eicosapentaenoic acid ratio showed a marginally significant contribution to the adiponectin variance (16.0%, P-nominal = 0.058). None of the GCTA results were significant after Bonferroni correction (P < 0.001). For each trait, the GxE GWAS identified a far larger number of significant single-nucleotide polymorphisms (P-interaction ≤ 10E-5) for the significant E factor (significant GxE variance contributor) than a control E factor (non-significant GxE variance contributor). In the BPRHS, DPA contributed a marginally significant GxE variance to the phenotypic variance of HOMA-IR (12.9%, P-nominal = 0.068) and fasting insulin (18.0%, P-nominal = 0.033).

Conclusion

Erythrocyte n-3 fatty acids contributed a significant GxE variance to diabetes-related traits at the genome-wide level.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-781) contains supplementary material, which is available to authorized users.     

Genetic (co)variance of rainbow trout (Oncorhynchus mykiss) body weight and its uniformity across production environments

Background

When rainbow trout from a single breeding program are introduced into various production environments, genotype-by-environment (GxE) interaction may occur. Although growth and its uniformity are two of the most important traits for trout producers worldwide, GxE interaction on uniformity of growth has not been studied. Our objectives were to quantify the genetic variance in body weight (BW) and its uniformity and the genetic correlation (r_g) between these traits, and to investigate the degree of GxE interaction on uniformity of BW in breeding (BE) and production (PE) environments using double hierarchical generalized linear models. Log-transformed data were also used to investigate whether the genetic variance in uniformity of BW, GxE interaction on uniformity of BW, and r_g between BW and its uniformity were influenced by a scale effect.

Results

Although heritability estimates for uniformity of BW were low and of similar magnitude in BE (0.014) and PE (0.012), the corresponding coefficients of genetic variation reached 19 and 21%, which indicated a high potential for response to selection. The genetic re-ranking for uniformity of BW (r_g = 0.56) between BE and PE was moderate but greater after log-transformation, as expressed by the low r_g (-0.08) between uniformity in BE and PE, which indicated independent genetic rankings for uniformity in the two environments when the scale effect was accounted for. The r_g between BW and its uniformity were 0.30 for BE and 0.79 for PE but with log-transformed BW, these values switched to -0.83 and -0.62, respectively.

Conclusions

Genetic variance exists for uniformity of BW in both environments but its low heritability implies that a large number of relatives are needed to reach even moderate accuracy of selection. GxE interaction on uniformity is present for both environments and sib-testing in PE is recommended when the aim is to improve uniformity across environments. Positive and negative r_g between BW and its uniformity estimated with original and log-transformed BW data, respectively, indicate that increased BW is genetically associated with increased variance in BW but with a decrease in the coefficient of variation. Thus, the scale effect substantially influences the genetic parameters of uniformity, especially the sign and magnitude of its r_g.     

Gene–environment interactions at the FKBP5 locus: sensitive periods,mechanisms and pleiotropism

Psychiatric phenotypes are multifactorial and polygenic, resulting from the complex interplay of genes and environmental factors that act cumulatively throughout an organism's lifetime. Adverse life events are strong predictors of risk for a number of psychiatric disorders and a number of studies have focused on gene–environment interactions (GxEs) occurring at genetic loci involved in the stress response. Such a locus that has received increasing attention is the gene encoding FK506 binding protein 51 (FKBP5), a heat shock protein 90 cochaperone of the steroid receptor complex that among other functions regulates sensitivity of the glucocorticoid receptor. Interactions between FKBP5 gene variants and life stressors alter the risk not only for mood and anxiety disorders, but also for a number of other disease phenotypes. In this review, we will focus on molecular and system‐wide mechanisms of this GxE with the aim of establishing a framework that explains GxE interactions. We will also discuss how an understanding of the biological effects of this GxE may lead to novel therapeutic approaches .     

Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks

Genome-wide association studies (GWAS) are routinely being used to examine the genetic contribution to complex human traits, such as high-density lipoprotein cholesterol (HDL-C). Although HDL-C levels are highly heritable (h²∼0.7), the genetic determinants identified through GWAS contribute to a small fraction of the variance in this trait. Reasons for this discrepancy may include rare variants, structural variants, gene-environment (GxE) interactions, and gene-gene (GxG) interactions. Clinical practice-based biobanks now allow investigators to address these challenges by conducting GWAS in the context of comprehensive electronic medical records (EMRs). Here we apply an EMR-based phenotyping approach, within the context of routine care, to replicate several known associations between HDL-C and previously characterized genetic variants: CETP (rs3764261, p = 1.22e-25), LIPC (rs11855284, p = 3.92e-14), LPL (rs12678919, p = 1.99e-7), and the APOA1/C3/A4/A5 locus (rs964184, p = 1.06e-5), all adjusted for age, gender, body mass index (BMI), and smoking status. By using a novel approach which censors data based on relevant co-morbidities and lipid modifying medications to construct a more rigorous HDL-C phenotype, we identified an association between HDL-C and TRIB1, a gene which previously resisted identification in studies with larger sample sizes. Through the application of additional analytical strategies incorporating biological knowledge, we further identified 11 significant GxG interaction models in our discovery cohort, 8 of which show evidence of replication in a second biobank cohort. The strongest predictive model included a pairwise interaction between LPL (which modulates the incorporation of triglyceride into HDL) and ABCA1 (which modulates the incorporation of free cholesterol into HDL). These results demonstrate that gene-gene interactions modulate complex human traits, including HDL cholesterol.     

Genome-Wide Association Study of Gene by Smoking Interactions in Coronary Artery Calcification

Many GWAS have identified novel loci associated with common diseases, but have focused only on main effects of individual genetic variants rather than interactions with environmental factors (GxE). Identification of GxE interactions is particularly important for coronary heart disease (CHD), a major preventable source of morbidity and mortality with strong non-genetic risk factors. Atherosclerosis is the major cause of CHD, and coronary artery calcification (CAC) is directly correlated with quantity of coronary atherosclerotic plaque. In the current study, we tested for genetic variants influencing extent of CAC via interaction with smoking (GxS), by conducting a GxS discovery GWAS in Genetic Epidemiology Network of Arteriopathy (GENOA) sibships (N = 915 European Americans) followed by replication in Framingham Heart Study (FHS) sibships (N = 1025 European Americans). Generalized estimating equations accounted for the correlation within sibships in strata-specific groups of smokers and nonsmokers, as well as GxS interaction. Primary analysis found SNPs that showed suggestive associations (p≤10⁻⁵) in GENOA GWAS, but these index SNPs did not replicate in FHS. However, secondary analysis was able to replicate candidate gene regions in FHS using other SNPs (+/−250 kb of GENOA index SNP). In smoker and nonsmoker groups, replicated genes included TCF7L2 (p = 6.0×10⁻⁵) and WWOX (p = 4.5×10⁻⁶); and TNFRSF8 (p = 7.8×10⁻⁵), respectively. For GxS interactions, replicated genes included TBC1D4 (p = 6.9×10⁻⁵) and ADAMTS9 (P = 7.1×10⁻⁵). Interestingly, these genes are involved in inflammatory pathways mediated by the NF-κB axis. Since smoking is known to induce chronic and systemic inflammation, association of these genes likely reflects roles in CAC development via inflammatory pathways. Furthermore, the NF-κB axis regulates bone remodeling, a key physiological process in CAC development. In conclusion, GxS GWAS has yielded evidence for novel loci that are associated with CAC via interaction with smoking, providing promising new targets for future population-based and functional studies of CAC development.     

An assessment of gene‐by‐environment interactions in developmental dyslexia‐related phenotypes

While the genetic and environmental contributions to developmental dyslexia (DD) have been studied extensively, the effects of identified genetic risk susceptibility and of specified environmental hazardous factors have usually been investigated separately. We assessed potential gene‐by‐environment (GxE) interactions on DD‐related reading, spelling and memory phenotypes. The presence of GxE effects were investigated for the DYX1C1, DCDC2, KIAA0319 and ROBO1 genes, and for seven specified environmental moderators in 165 nuclear families in which at least one member had DD, by implementing a general test for GxE interaction in sib‐pair‐based association analysis of quantitative traits. Our results support a diathesis‐stress model for both reading and memory composites: GxE effects were found between some specified environmental moderators (i.e. maternal smoke during pregnancy, birth weight and socio‐economic status) and the DYX1C1‐1259C/G marker. We have provided initial evidence that the joint analysis of identified genetic risk susceptibility and measured putative risk factors can be exploited in the study of the etiology of DD and reading‐related neuropsychological phenotypes, and may assist in identifying/preventing the occurrence of DD.     

Candidate gene–environment interactions and their relationships with timing of breeding in a wild bird population

Monitoring and predicting evolutionary changes underlying current environmental modifications are complex challenges. Recent approaches to achieve these objectives include assessing the genetic variation and effects of candidate genes on traits indicating adaptive potential. In birds, for example, short tandem repeat polymorphism at four candidate genes (CLOCK, NPAS2, ADCYAP1, and CREB1) has been linked to variation in phenological traits such as laying date and timing of migration. However, our understanding of their importance as evolutionary predictors is still limited, mainly because the extent of genotype–environment interactions (GxE) related to these genes has yet to be assessed. Here, we studied a population of Tree swallow (Tachycineta bicolor) over 4 years in southern Québec (Canada) to assess the relationships between those four candidate genes and two phenological traits related to reproduction (laying date and incubation duration) and also determine the importance of GxE in this system. Our results showed that NPAS2 female genotypes were nonrandomly distributed across the study system and formed a longitudinal cline with longer genotypes located to the east. We observed relationships between length polymorphism at all candidate genes and laying date and/or incubation duration, and most of these relationships were affected by environmental variables (breeding density, latitude, or temperature). In particular, the positive relationships detected between laying date and both CLOCK and NPAS2 female genotypes were variable depending on breeding density. Our results suggest that all four candidate genes potentially affect timing of breeding in birds and that GxE are more prevalent and important than previously reported in this context.     

Gene-environment interaction and affected sib pair linkage analysis

OBJECTIVES: Gene-environment (GxE) interaction influences risk for many complex disease traits. However, genome screens using affected sib pair linkage techniques are typically conducted without regard for GxE interaction. We propose a simple extension of the commonly used mean test and evaluate its power for several forms of GxE interaction. METHODS: We compute expected IBD sharing by sibling exposure profile, that is by whether two sibs are exposed (EE), unexposed (UU), or are discordant for exposure (EU). We describe a simple extension of the mean test, the "mean-interaction" test that utilizes heterogeneity in IBD sharing across EE, EU, and UU sib pairs in a test for linkage. RESULTS: The mean-interaction test provides greater power than the mean test for detecting linkage in the presence of moderate or strong GxE interaction, typically when the interaction relative risk (R(ge)) exceeds 3 or is less than 1/3. In the presence of strong interaction (R(ge) = 10), the required number of affected sib pairs to achieve 80% power for detecting linkage is approximately 30% higher when the environmental factor is ignored in the mean test, than when it is utilized in the mean-interaction test. CONCLUSION: Linkage methods that incorporate environmental data and allow for interaction can lead to increased power for localizing a disease gene involved in a GxE interaction.     

Genetics of incipient speciation in Drosophila mojavensis. I. Male courtship song, mating success, and genotype x environment interactions

Few studies have examined genotype by environment (GxE) effects on premating reproductive isolation and associated behaviors, even though such effects may be common when speciation is driven by adaptation to different environments. In this study, mating success and courtship song differences among diverging populations of Drosophila mojavensis were investigated in a two-environment quantitative trait locus (QTL) analysis. Baja California and mainland Mexico populations of D. mojavensis feed and breed on different host cacti, so these host plants were used to culture F2 males to examine host-specific QTL effects and GxE interactions influencing mating success and courtship songs. Linear selection gradient analysis showed that mainland females mated with males that produced songs with significantly shorter L(long)-IPIs, burst durations, and interburst intervals. Twenty-one microsatellite loci distributed across all five major chromosomes were used to localize effects of mating success, time to copulation, and courtship song components. Male courtship success was influenced by a single detected QTL, the main effect of cactus, and four GxE interactions, whereas time to copulation was influenced by three different QTLs on the fourth chromosome. Multiple-locus restricted maximum likelihood (REML) analysis of courtship song revealed consistent effects linked with the same fourth chromosome markers that influenced time to copulation, a number of GxE interactions, and few possible cases of epistasis. GxE interactions for mate choice and song can maintain genetic variation in populations, but alter outcomes of sexual selection and isolation, so signal evolution and reproductive isolation may be slowed in diverging populations. Understanding the genetics of incipient speciation in D. mojavensis clearly depends on cactus-specific expression of traits associated with courtship behavior and sexual isolation.     

Species-Specific Effects on Ecosystem Functioning Can Be Altered by Interspecific Interactions

Biological assemblages are constantly undergoing change, with species being introduced, extirpated and experiencing shifts in their densities. Theory and experimentation suggest that the impacts of such change on ecosystem functioning should be predictable based on the biological traits of the species involved. However, interspecific interactions could alter how species affect functioning, with the strength and sign of interactions potentially depending on environmental context (e.g. homogenous vs. heterogeneous conditions) and the function considered. Here, we assessed how concurrent changes to the densities of two common marine benthic invertebrates, Corophium volutator and Hediste diversicolor, affected the ecological functions of organic matter consumption and benthic-pelagic nutrient flux. Complementary experiments were conducted within homogenous laboratory microcosms and naturally heterogeneous field plots. When the densities of the species were increased within microcosms, interspecific interactions enhanced effects on organic matter consumption and reduced effects on nutrient flux. Trait-based predictions of how each species would affect functioning were only consistently supported when the density of the other species was low. In field plots, increasing the density of either species had a positive effect on organic matter consumption (with no significant interspecific interactions) but no effect on nutrient flux. Our results indicate that species-specific effects on ecosystem functioning can be altered by interspecific interactions, which can be either facilitative (positive) or antagonistic (negative) depending on the function considered. The impacts of biodiversity change may therefore not be predictable based solely on the biological traits of the species involved. Possible explanations for why interactions were detected in microcosms but not in the field are discussed.     

Plasticity in Vegetative Growth over Contrasted Growing Sites of an F1 Olive Tree Progeny during Its Juvenile Phase

Climatic changes impact fruit tree growth and severely limit their production. Investigating the tree ability to cope with environmental variations is thus necessary to adapt breeding and management strategies in order to ensure sustainable production. In this study, we assessed the genetic parameters and genotype by environment interaction (GxE) during the early tree growth. One hundred and twenty olive seedlings derived from the cross ‘Olivière’ x ‘Arbequina’ were examined across two sites with contrasted environments, accounting for ontogenetic trends over three years. Models including the year of growth, branching order, environment, genotype effects, and their interactions were built with variance function and covariance structure of residuals when necessary. After selection of a model, broad sense heritabilities were estimated. Despite strong environmental effect on most traits, no GxE was found. Moreover, the internal structure of traits co-variation was similar in both sites. Ontogenetic growth variation, related to (i) the overall tree form and (ii) the growth and branching habit at growth unit scale, was not altered by the environment. Finally, a moderate to strong genetic control was identified for traits at the whole tree scale and at internode scale. Among all studied traits, the maximal internode length exhibited the highest heritability (H² = 0.74). Considering the determinant role of this trait in tree architecture and its stability across environments, this study consolidates its relevance for breeding.