Correcting systematic inflation in genetic association tests that consider interaction effects: application to a genome-wide association study of posttraumatic stress disorder

Gene-Environment Analyses in a UK Biobank Skin Cancer Cohort Identifies Important SNPs in DNA Repair Genes That May Help Prognosticate Disease Risk

BACKGROUND

Despite well-established relationships between sun exposure and skin cancer pathogenesis/progression, specific gene-environment interactions in at-risk individuals remain poorly-understood.

METHODS

We leveraged a UK Biobank cohort of basal cell carcinoma (BCC, n = 17,221), cutaneous squamous cell carcinoma (cSCC, n = 2,331), melanoma in situ (M-is, n = 1,158), invasive melanoma (M-inv, n = 3,798), and healthy controls (n = 448,164) to quantify the synergistic involvement of genetic and environmental factors influencing disease risk. We surveyed 8,798 SNPs from 190 DNA repair genes, and 11 demographic/behavioral risk factors.

RESULTS

Clinical analysis identified darker skin (RR = 0.01-0.65) and hair (RR = 0.27-0.63) colors as protective factors. Eleven SNPs were significantly associated with BCC, three of which were also associated with M-inv. Gene-environment analysis yielded 201 SNP-environment interactions across 90 genes (FDR-adjusted q < 0.05). SNPs from the FANCA gene showed interactions with at least one clinical factor in all cancer groups, of which three (rs9926296, rs3743860, rs2376883) showed interaction with nearly every factor in BCC and M-inv.

CONCLUSIONS

We identified novel risk factors for keratinocyte carcinomas and melanoma, highlighted the prognostic value of several FANCA alleles among individuals with a history of sunlamp use and childhood sunburns, and demonstrated the importance of combining genetic and clinical data in disease risk stratification.

IMPACT

This study revealed genome-wide associations with important implications for understanding skin cancer risk in the context of the rapidly-evolving field of precision medicine. Major individual factors (including sex, hair and skin color, and sun protection use) were significant mediators for all skin cancers, interacting with >200 SNPs across four skin cancer types.

A genome-wide search for pleiotropy in more than 100,000 harmonized longitudinal cognitive domain scores

BACKGROUND

More than 75 common variant loci account for only a portion of the heritability for Alzheimer's disease (AD). A more complete understanding of the genetic basis of AD can be deduced by exploring associations with AD-related endophenotypes.

METHODS

We conducted genome-wide scans for cognitive domain performance using harmonized and co-calibrated scores derived by confirmatory factor analyses for executive function, language, and memory. We analyzed 103,796 longitudinal observations from 23,066 members of community-based (FHS, ACT, and ROSMAP) and clinic-based (ADRCs and ADNI) cohorts using generalized linear mixed models including terms for SNP, age, SNP × age interaction, sex, education, and five ancestry principal components. Significance was determined based on a joint test of the SNP's main effect and interaction with age. Results across datasets were combined using inverse-variance meta-analysis. Genome-wide tests of pleiotropy for each domain pair as the outcome were performed using PLACO software.

RESULTS

Individual domain and pleiotropy analyses revealed genome-wide significant (GWS) associations with five established loci for AD and AD-related disorders (BIN1, CR1, GRN, MS4A6A, and APOE) and eight novel loci. ULK2 was associated with executive function in the community-based cohorts (rs157405, P = 2.19 × 10-9). GWS associations for language were identified with CDK14 in the clinic-based cohorts (rs705353, P = 1.73 × 10-8) and LINC02712 in the total sample (rs145012974, P = 3.66 × 10-8). GRN (rs5848, P = 4.21 × 10-8) and PURG (rs117523305, P = 1.73 × 10-8) were associated with memory in the total and community-based cohorts, respectively. GWS pleiotropy was observed for language and memory with LOC107984373 (rs73005629, P = 3.12 × 10-8) in the clinic-based cohorts, and with NCALD (rs56162098, P = 1.23 × 10-9) and PTPRD (rs145989094, P = 8.34 × 10-9) in the community-based cohorts. GWS pleiotropy was also found for executive function and memory with OSGIN1 (rs12447050, P = 4.09 × 10-8) and PTPRD (rs145989094, P = 3.85 × 10-8) in the community-based cohorts. Functional studies have previously linked AD to ULK2, NCALD, and PTPRD.

CONCLUSION

Our results provide some insight into biological pathways underlying processes leading to domain-specific cognitive impairment and AD, as well as a conduit toward a syndrome-specific precision medicine approach to AD. Increasing the number of participants with harmonized cognitive domain scores will enhance the discovery of additional genetic factors of cognitive decline leading to AD and related dementias.

A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS

Genotype-by-environment interaction (GEI or GxE) plays an important role in understanding complex human traits. However, it is usually challenging to detect GEI signals efficiently and accurately while adjusting for population stratification and sample relatedness in large-scale genome-wide association studies (GWAS). Here we propose a fast and powerful linear mixed model-based approach, fastGWA-GE, to test for GEI effect and G + GxE joint effect. Our extensive simulations show that fastGWA-GE outperforms other existing GEI test methods by controlling genomic inflation better, providing larger power and running hundreds to thousands of times faster. We performed a fastGWA-GE analysis of ~7.27 million variants on 452 249 individuals of European ancestry for 13 quantitative traits and five environment variables in the UK Biobank GWAS data and identified 96 significant signals (72 variants across 57 loci) with GEI test P-values < 1 × 10-9, including 27 novel GEI associations, which highlights the effectiveness of fastGWA-GE in GEI signal discovery in large-scale GWAS.

Index of multiple deprivation contributed to common psychiatric disorders: A systematic review and comprehensive analysis

BACKGROUND

Limited studies have been conducted to explore the interaction effects of social environmental and genetic factors on the risks of common psychiatric disorders.

METHODS

56,613-106,695 individuals were collected from the UK Biobank cohort. Logistic or liner regression models were first used to evaluate the associations of index of multiple deprivation (IMD) with bipolar disorder (BD), depression and anxiety in UK Biobank cohort. Then, for the significant IMD associated with BD, depression and anxiety, genome-wide gene-environment interaction study (GWEIS) was performed by PLINK 2.0.

RESULT

Totally, the higher levels of IMD were significantly associated with higher risks of BD, depression and anxiety. For BD, GWEIS identified multiple significant SNPs interacting with IMD, such as rs75182167 for income and rs111841503 for education. For depression and anxiety, GWEIS found significant SNPs interacting with income and education, such as rs147013419 for income and rs142366753 for education.

CONCLUSION

Social environmental deprivations contributed to the risks of psychiatric disorders. Besides, we reported multiple candidate genetic loci interacting with IMD, providing novel insights into the biological mechanism.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Genome-wide gene and serum ferritin interaction in the development of type 2 diabetes in adults aged 40 years or older

BACKGROUND AND AIMS

Elevated serum ferritin is associated with incident Type 2 diabetes (T2D), but the interactions between serum ferritin and genetic factors which may improve understanding underlying mechanism in the development of T2D are still unclear. We determined the gene-ferritin interactions on the development of T2D by genome-wide gene-ferritin interaction analyses.

METHODS AND RESULTS

A total of 3405 participants from two prospective cohorts of community living residents were included, and the median follow-time was 3.99 years. Genome-wide gene-ferritin interactions were analyzed using the joint test with two degrees of freedom and the interaction test with one degree of freedom. There were 18 SNPs selected in the joint test. Finally, four independent variants [rs355140 (LINC00312), rs4075576 (nearby PDGFA), rs1332202 (PTPRD), and rs713157 (nearby LINC00900)] with low pairwise linkage disequilibrium (r2<0.2) and located at least 1000 kb from the index SNP showed interactions with serum ferritin level. In the association analyses between serum ferritin levels (tertiles of ferritin and ferritin status) and the incidence of T2D according to genotype, the Incidence Rate Ratios (IRRs) in the highest tertile of ferritin level (vs. the lowest tertile) were greater for participants with heterozygotes of risk alleles of each of the four SNP than IRRs for those with wild type. Compared with the normal group, the elevated ferritin group also had a higher risk of T2D for all genetic variants of risk alleles, particularly its homozygotes.

CONCLUSION

Serum ferritin level interacts with genetic variants (rs355140, rs4075576, rs1332202, and rs713157) in the development of T2D.

Smooth-threshold multivariate genetic prediction incorporating gene–environment interactions

We propose a genetic prediction modeling approach for genome-wide association study (GWAS) data that can include not only marginal gene effects but also gene–environment (GxE) interaction effects—i.e., multiplicative effects of environmental factors with genes rather than merely additive effects of each. The proposed approach is a straightforward extension of our previous multiple regression-based method, STMGP (smooth-threshold multivariate genetic prediction), with the new feature being that genome-wide test statistics from a GxE interaction analysis are used to weight the corresponding variants. We develop a simple univariate regression approximation to the GxE interaction effect that allows a direct fit of the STMGP framework without modification. The sparse nature of our model automatically removes irrelevant predictors (including variants and GxE combinations), and the model is able to simultaneously incorporate multiple environmental variables. Simulation studies to evaluate the proposed method in comparison with other modeling approaches demonstrate its superior performance under the presence of GxE interaction effects. We illustrate the usefulness of our prediction model through application to real GWAS data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Rare and low-frequency exonic variants and gene-by-smoking interactions in pulmonary function

Genome-wide association studies have identified numerous common genetic variants associated with spirometric measures of pulmonary function, including forced expiratory volume in one second (FEV₁), forced vital capacity, and their ratio. However, variants with lower minor allele frequencies are less explored. We conducted a large-scale gene-smoking interaction meta-analysis on exonic rare and low-frequency variants involving 44,429 individuals of European ancestry in the discovery stage and sought replication in the UK BiLEVE study with 45,133 European ancestry samples and UK Biobank study with 59,478 samples. We leveraged data on cigarette smoking, the major environmental risk factor for reduced lung function, by testing gene-by-smoking interaction effects only and simultaneously testing the genetic main effects and interaction effects. The most statistically significant signal that replicated was a previously reported low-frequency signal in GPR126, distinct from common variant associations in this gene. Although only nominal replication was obtained for a top rare variant signal rs142935352 in one of the two studies, interaction and joint tests for current smoking and PDE3B were significantly associated with FEV₁. This study investigates the utility of assessing gene-by-smoking interactions and underscores their effects on potential pulmonary function.

Genome-wide gene-environment interactions in neuroticism: an exploratory study across 25 environments

Gene-environment interactions (GxE) are often suggested to play an important role in the aetiology of psychiatric phenotypes, yet so far, only a handful of genome-wide environment interaction studies (GWEIS) of psychiatric phenotypes have been conducted. Representing the most comprehensive effort of its kind to date, we used data from the UK Biobank to perform a series of GWEIS for neuroticism across 25 broadly conceptualised environmental risk factors (trauma, social support, drug use, physical health). We investigated interactions on the level of SNPs, genes, and gene-sets, and computed interaction-based polygenic risk scores (PRS) to predict neuroticism in an independent sample subset (N = 10,000). We found that the predictive ability of the interaction-based PRSs did not significantly improve beyond that of a traditional PRS based on SNP main effects from GWAS, but detected one variant and two gene-sets showing significant interaction signal after correction for the number of analysed environments. This study illustrates the possibilities and limitations of a comprehensive GWEIS in currently available sample sizes.

Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model

The contribution of gene-by-environment (GxE) interactions for many human traits and diseases is poorly characterized. We propose a Bayesian whole-genome regression model for joint modeling of main genetic effects and GxE interactions in large-scale datasets, such as the UK Biobank, where many environmental variables have been measured. The method is called LEMMA (Linear Environment Mixed Model Analysis) and estimates a linear combination of environmental variables, called an environmental score (ES), that interacts with genetic markers throughout the genome. The ES provides a readily interpretable way to examine the combined effect of many environmental variables. The ES can be used both to estimate the proportion of phenotypic variance attributable to GxE effects and to test for GxE effects at genetic variants across the genome. GxE effects can induce heteroskedasticity in quantitative traits, and LEMMA accounts for this by using robust standard error estimates when testing for GxE effects. When applied to body mass index, systolic blood pressure, diastolic blood pressure, and pulse pressure in the UK Biobank, we estimate that 9.3%, 3.9%, 1.6%, and 12.5%, respectively, of phenotypic variance is explained by GxE interactions and that low-frequency variants explain most of this variance. We also identify three loci that interact with the estimated environmental scores (−log10p>7.3).

Genome-wide interaction study of single-nucleotide polymorphisms and alcohol consumption on blood pressure: The Ansan and Ansung study of the Korean Genome and Epidemiology Study (KoGES)

Hypertension is a common disease worldwide. Alcohol consumption is one of the risk factors for hypertension, however, it is unclear how alcohol consumption elevates blood pressure. Blood pressure could be affected by interactions between genetic variations and alcohol consumption. Thus, we performed a genome-wide interaction study (GWIS) to assess the effect of gene-alcohol consumption interaction on blood pressure among adults aged ≥40 years from the Ansan and Ansung cohort study (n = 6,176), a part of the Korean Genome Epidemiology Study (KoGES). As a result, rs1297184, single-nucleotide polymorphism (SNP) in locus LGR5 was significant (P_GWIS  = 8.78 × 10^-9 ) in GWIS analysis on diastolic blood pressure, but not on systolic blood pressure. However, there was a heteroscedasticity of alcohol consumption. In the GWIS analysis, applying the inverse-variance weighting to correct the systematic inflation slightly attenuated the strength of interaction (P_{GWIS_IVW}  = 7.14 × 10^-8 ). This interaction was replicated in the Health Examinees cohort (p = .026), a large-scale community-based cohort (n = 18,708). In conclusion, we identified a possible novel interaction between an SNP (rs1297184) and alcohol consumption on blood pressure.

Quick assessment for systematic test statistic inflation/deflation due to null model misspecifications in genome-wide environment interaction studies

Gene-environment (GxE) interaction is one potential explanation for the missing heritability problem. A popular approach to genome-wide environment interaction studies (GWEIS) is based on regression models involving interactions between genetic variants and environment variables. Unfortunately, GWEIS encounters systematically inflated (or deflated) test statistics more frequently than a marginal association study. The problematic behavior may occur due to poor specification of the null model (i.e. the model without genetic effect) in GWEIS. Improved null model specification may resolve the problem, but the investigation requires many time-consuming analyses of genome-wide scans, e.g. by trying out several transformations of the phenotype. It is therefore helpful if we can predict such problematic behavior beforehand. We present a simple closed-form formula to assess problematic behavior of GWEIS under the null hypothesis of no genetic effects. It requires only phenotype, environment variables, and covariates, enabling quick identification of systematic test statistic inflation or deflation. Applied to real data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), our formula identified problematic studies from among hundreds GWEIS considering each metabolite as the environment variable in GxE interaction. Our formula is useful to quickly identify problematic GWEIS without requiring a genome-wide scan.

Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies

Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension.

Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in UK Biobank and Generation Scotland

Stress is associated with poorer physical and mental health. To improve our understanding of this link, we performed genome-wide association studies (GWAS) of depressive symptoms and genome-wide by environment interaction studies (GWEIS) of depressive symptoms and stressful life events (SLE) in two UK population-based cohorts (Generation Scotland and UK Biobank). No SNP was individually significant in either GWAS, but gene-based tests identified six genes associated with depressive symptoms in UK Biobank (DCC, ACSS3, DRD2, STAG1, FOXP2 and KYNU; p < 2.77 × 10-6). Two SNPs with genome-wide significant GxE effects were identified by GWEIS in Generation Scotland: rs12789145 (53-kb downstream PIWIL4; p = 4.95 × 10-9; total SLE) and rs17070072 (intronic to ZCCHC2; p = 1.46 × 10-8; dependent SLE). A third locus upstream CYLC2 (rs12000047 and rs12005200, p < 2.00 × 10-8; dependent SLE) when the joint effect of the SNP main and GxE effects was considered. GWEIS gene-based tests identified: MTNR1B with GxE effect with dependent SLE in Generation Scotland; and PHF2 with the joint effect in UK Biobank (p < 2.77 × 10-6). Polygenic risk scores (PRSs) analyses incorporating GxE effects improved the prediction of depressive symptom scores, when using weights derived from either the UK Biobank GWAS of depressive symptoms (p = 0.01) or the PGC GWAS of major depressive disorder (p = 5.91 × 10-3). Using an independent sample, PRS derived using GWEIS GxE effects provided evidence of shared aetiologies between depressive symptoms and schizotypal personality, heart disease and COPD. Further such studies are required and may result in improved treatments for depression and other stress-related conditions.

Testing for gene-environment interaction under exposure misspecification

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties.

Gene-environment interaction and psychiatric disorders: Review and future directions

Empirical studies suggest that psychiatric disorders result from a complex interplay between genetic and environmental factors. Most evidence for such gene-environment interaction (GxE) is based on single candidate gene studies conducted from a Diathesis-Stress perspective. Recognizing the short-comings of candidate gene studies, GxE research has begun to focus on genome-wide and polygenic approaches as well as drawing on different theoretical concepts underlying GxE, such as Differential Susceptibility. After reviewing evidence from candidate GxE studies and presenting alternative theoretical frameworks underpinning GxE research, more recent approaches and findings from whole genome approaches are presented. Finally, we suggest how future GxE studies may unpick the complex interplay between genes and environments in psychiatric disorders.

Single-nucleotide polymorphisms in genes related to the hypothalamic-pituitary-adrenal axis as risk factors for posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a common psychiatric disorder. The etiology of PTSD is multifactorial, depending on many environmental and genetic risk factors, and the exposure to life or physical integrity-threatening events. Several studies have shown significant correlations of many neurobiological findings with PTSD. Hypothalamic-pituitary-adrenal (HPA) axis dysfunction is strongly correlated with this disorder. One hypothesis is that HPA axis dysfunction may precede the traumatic event, suggesting that genes expressed in the HPA axis may be involved in the development of PTSD. This article reviews molecular genetic studies related to PTSD collected through a literature search performed in PubMed, MEDLINE, ScienceDirect, and Scientific Electronic Library Online (SciELO). The results of these studies suggest that several polymorphisms in the HPA axis genes, including FKBP5, NR3C1, CRHR1, and CRHR2, may be risk factors for PTSD development or may be associated with the severity of PTSD symptoms.

Environment-Dependent Genotype-Phenotype Associations in Avian Breeding Time

Understanding how genes shape phenotypes is essential to assess the evolutionary potential of a trait. Identifying the genes underlying quantitative behavioral or life-history traits has, however, proven to be a major challenge. The majority of these traits are phenotypically plastic and different parts of the genome can be involved in shaping the trait under different environmental conditions. These variable genotype-phenotype associations could be one explanation for the limited success of genome-wide association studies in such traits. We here use avian seasonal timing of breeding, a trait that is highly plastic in response to spring temperature, to explore effects of such genotype-by-environment interactions in genome-wide association studies. We genotyped 2045 great tit females for 384081 single nucleotide polymorphisms (SNPs) and recorded their egg-laying dates in the wild. When testing for associations between SNPs and egg-laying dates, no SNP reached genome-wide significance. We then explored whether SNP effects were modified by annual spring temperature by formally testing for an interaction between SNP effect and temperature. The models including the SNP^∗temperature interaction performed consistently better although no SNP reached genome-wide significance. Our results suggest that the effects of genes shaping seasonal timing depended on annual spring temperature. Such environment-dependent effects are expected for any phenotypically plastic trait. Taking these effects into account will thus improve the success of detecting genes involved in phenotypically plastic traits, thereby leading to a better understanding of their evolutionary potential.

Powerful Genetic Association Analysis for Common or Rare Variants with High-Dimensional Structured Traits

Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical P-value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.

Maternal prenatal anxiety and child COMT genotype predict working memory and symptoms of ADHD

Maternal prenatal anxiety is an important risk factor for altered child neurodevelopment but there is uncertainty concerning the biological mechanisms involved and sources of individual differences in children's responses. We sought to determine the role of functional genetic variation in COMT, which encodes catechol-O-methyltransferase, in the association between maternal prenatal anxiety and child symptoms of ADHD and working memory. We used the prospectively-designed ALSPAC cohort (n = 6,969) for our primary data analyses followed by replication analyses in the PREDO cohort (n = 425). Maternal prenatal anxiety was based on self-report measures; child symptoms of ADHD were collected from 4-15 years of age; working memory was assessed from in-person testing at age 8 years; and genetic variation in COMT at rs4680 was determined in both mothers and children. The association between maternal prenatal anxiety and child attention/hyperactivity symptoms and working memory was moderated by the child's rs4680 genotype, with stronger effects obtained for the val/val (G:G) genotype relative to val/met (A:G) (all p<0.01) and met/met (A:A) groups (all p<0.05). Similar findings were observed in the PREDO cohort where maternal prenatal anxiety interacted with child rs4680 to predict symptoms of ADHD at 3.5 years of age. The findings, from two cohorts, show a robust gene-environment interaction, which may contribute to inter-individual differences in the effects of maternal prenatal anxiety on developmental outcomes from childhood to mid-adolescence.

Early Life Stress, FKBP5 Polymorphisms, and Quantitative Glycemic Traits

OBJECTIVE

Early life stress (ELS) has been shown to influence health later in life. Functioning of the hypothalamic-pituitary-adrenal axis, regulated partly by FKBP5 gene, may moderate these effects. We examined whether FKBP5 single-nucleotide polymorphisms (SNPs) interact with ELS on Type 2 diabetes, cardiovascular disease, and quantitative glycemic traits.

METHODS

A total of 1728 Helsinki Birth Cohort Study participants born from 1934 to 1944 were genotyped for FKBP5 SNPs (rs1360780, rs9394309, rs9470080) and were administered a 2-hour (75 g) oral glucose tolerance test and a questionnaire on physician-diagnosed and medication use for chronic diseases at a mean age of 61.5 years. Of the participants, 273 had been exposed to ELS, operationalized as separation from their parents, at a mean age of 4.7 years due to evacuations during World War II.

RESULTS

ELS interacted with FKBP5 SNPs in the analyses of fasting (rs1360780, p = .015), 30-minute (rs1360780, p = .031; rs9394309, p = .041) and incremental insulin (rs1360780, p = .032; rs9394309, p = .028; rs9470080, p = .043), insulin area under the curve (rs1360780, p = .044), and impaired fasting glucose (rs9470080, p = .049); among carriers of at least one copy of minor allele, but not among major allele homozygotes, insulin values were higher, as were the odds for impaired fasting glucose if they had been separated compared with if they had not. Corresponding associations were found with a haplotype formed by minor alleles in all three SNPs for fasting, 30-minute, and incremental insulin (p < .05).

CONCLUSIONS

FKBP5 polymorphisms in combination with ELS exposure predict higher insulin and glucose values in midlife. Our findings support the role for hypothalamic-pituitary-adrenal axis dysregulation in health-related metabolic outcomes.

Birth weight interacts with a functional variant of the oxytocin receptor gene (OXTR) to predict executive functioning in children

Genetic variation in the oxytocin receptor gene (OXTR) is associated with several psychiatric conditions characterized by deficits in executive functioning (EF). A specific OXTR variant, rs2254298, has previously been associated with brain functioning in regions implicated in EF. Moreover, birth weight variation across the entire range is associated with individual differences in cortical structure and function that underlie EF. This is the first study to examine the main and interactive effect between rs2254298 and birth weight on EF in children. The sample consisted of 310 children from an ongoing longitudinal study. EF was measured at age 4.5 using observational tasks indexing working memory, cognitive flexibility, and inhibitory control. A family-based design that controlled for population admixture, stratification, and nongenomic confounds was employed. A significant genetic association between rs2254298 and EF was observed, with more copies of the major allele (G) associated with higher EF. There was also a significant interaction between rs2254298 and birth weight, such that more copies of the major allele in combination with higher birth weight predicted better EF. Findings suggest that OXTR may be associated with discrete neurocognitive abilities in childhood, and these effects may be modulated by intrauterine conditions related to fetal growth and development.

Screening for interaction effects in gene expression data

Expression quantitative trait (eQTL) studies are a powerful tool for identifying genetic variants that affect levels of messenger RNA. Since gene expression is controlled by a complex network of gene-regulating factors, one way to identify these factors is to search for interaction effects between genetic variants and mRNA levels of transcription factors (TFs) and their respective target genes. However, identification of interaction effects in gene expression data pose a variety of methodological challenges, and it has become clear that such analyses should be conducted and interpreted with caution. Investigating the validity and interpretability of several interaction tests when screening for eQTL SNPs whose effect on the target gene expression is modified by the expression level of a transcription factor, we characterized two important methodological issues. First, we stress the scale-dependency of interaction effects and highlight that commonly applied transformation of gene expression data can induce or remove interactions, making interpretation of results more challenging. We then demonstrate that, in the setting of moderate to strong interaction effects on the order of what may be reasonably expected for eQTL studies, standard interaction screening can be biased due to heteroscedasticity induced by true interactions. Using simulation and real data analysis, we outline a set of reasonable minimum conditions and sample size requirements for reliable detection of variant-by-environment and variant-by-TF interactions using the heteroscedasticity consistent covariance-based approach.

Oxytocin Receptor Genetic and Epigenetic Variations: Association With Child Abuse and Adult Psychiatric Symptoms

Childhood abuse can alter biological systems and increase risk for adult psychopathology. Epigenetic mechanisms, alterations in DNA structure that regulate the gene expression, are a potential mechanism underlying this risk. While abuse associates with methylation of certain genes, particularly those in the stress response system, no study to date has evaluated abuse and methylation of the oxytocin receptor (OXTR). However, studies support a role for OXTR in the link between abuse and adverse adult outcomes, showing that abuse can confer greater risk for psychiatric symptoms in those with specific OXTR genotypes. This study therefore sought to (a) assess the role of epigenetics in the link between abuse and psychopathology and (b) begin to integrate the genetic and epigenetic literature by exploring associations between OXTR genotypes and DNA CpG methylation. Data on 18 OXTR CpG sites, 44 single nucleotide polymorphisms, childhood abuse, and adult depression and anxiety symptoms were assessed in 393 African American adults (age = 41 ± 12.8 years). Overall, 68% of genotypes were associated with methylation of nearby CpG sites, with a subset surviving multiple test correction. Child abuse associated with higher methylation of two CpG sites yet did not survive correction or serve as a mediator of psychopathology. However, abuse interacted with CpG methylation to predict psychopathology. These findings suggest a role for OXTR in understanding the influence of early environments on adult psychiatric symptoms.

GENOME-WIDE ASSOCIATION STUDY (GWAS) AND GENOME-WIDE BY ENVIRONMENT INTERACTION STUDY (GWEIS) OF DEPRESSIVE SYMPTOMS IN AFRICAN AMERICAN AND HISPANIC/LATINA WOMEN

BACKGROUND

Genome-wide association studies (GWAS) have made little progress in identifying variants linked to depression. We hypothesized that examining depressive symptoms and considering gene-environment interaction (GxE) might improve efficiency for gene discovery. We therefore conducted a GWAS and genome-wide by environment interaction study (GWEIS) of depressive symptoms.

METHODS

Using data from the SHARe cohort of the Women's Health Initiative, comprising African Americans (n = 7,179) and Hispanics/Latinas (n = 3,138), we examined genetic main effects and GxE with stressful life events and social support. We also conducted a heritability analysis using genome-wide complex trait analysis (GCTA). Replication was attempted in four independent cohorts.

RESULTS

No SNPs achieved genome-wide significance for main effects in either discovery sample. The top signals in African Americans were rs73531535 (located 20 kb from GPR139, P = 5.75 × 10(-8) ) and rs75407252 (intronic to CACNA2D3, P = 6.99 × 10(-7) ). In Hispanics/Latinas, the top signals were rs2532087 (located 27 kb from CD38, P = 2.44 × 10(-7) ) and rs4542757 (intronic to DCC, P = 7.31 × 10(-7) ). In the GEWIS with stressful life events, one interaction signal was genome-wide significant in African Americans (rs4652467; P = 4.10 × 10(-10) ; located 14 kb from CEP350). This interaction was not observed in a smaller replication cohort. Although heritability estimates for depressive symptoms and stressful life events were each less than 10%, they were strongly genetically correlated (rG = 0.95), suggesting that common variation underlying self-reported depressive symptoms and stressful life event exposure, though modest on their own, were highly overlapping in this sample.

CONCLUSIONS

Our results underscore the need for larger samples, more GEWIS, and greater investigation into genetic and environmental determinants of depressive symptoms in minorities.

RDoC and translational perspectives on the genetics of trauma-related psychiatric disorders

Individuals with a history of child abuse are at high risk for depression, anxiety disorders, aggressive behavior, and substance use problems. The goal of this paper is to review studies of the genetics of these stress-related psychiatric disorders. An informative subset of studies that examined candidate gene by environment (GxE) predictors of these psychiatric problems in individuals maltreated as children is reviewed, together with extant genome wide association studies (GWAS). Emerging findings on epigenetic changes associated with adverse early experiences are also reviewed. Meta-analytic support and replicated findings are evident for several genetic risk factors; however, extant research suggests the effects are pleiotropic. Genetic factors are not associated with distinct psychiatric disorders, but rather diverse clinical phenotypes. Research also suggests adverse early life experiences are associated with changes in gene expression of multiple known candidate genes, genes involved in DNA transcription and translation, and genes necessary for brain circuitry development, with changes in gene expression reported in key brain structures implicated in the pathophysiology of psychiatric and substance use disorders. The finding of pleiotropy highlights the value of using the Research Domain Criteria (RDoC) framework in future studies of the genetics of stress-related psychiatric disorders, and not trying simply to link genes to multifaceted clinical syndromes, but to more limited phenotypes that map onto distinct neural circuits. Emerging work in the field of epigenetics also suggests that translational studies that integrate numerous unbiased genome-wide approaches will help to further unravel the genetics of stress-related psychiatric disorders.

Gene-Stress-Epigenetic Regulation of FKBP5: Clinical and Translational Implications

Stress responses and related outcomes vary markedly across individuals. Elucidating the molecular underpinnings of this variability is of great relevance for developing individualized prevention strategies and treatments for stress-related disorders. An important modulator of stress responses is the FK506-binding protein 51 (FKBP5/FKBP51). FKBP5 acts as a co-chaperone that modulates not only glucocorticoid receptor activity in response to stressors but also a multitude of other cellular processes in both the brain and periphery. Notably, the FKBP5 gene is regulated via complex interactions among environmental stressors, FKBP5 genetic variants, and epigenetic modifications of glucocorticoid-responsive genomic sites. These interactions can result in FKBP5 disinhibition that has been shown to contribute to a number of aberrant phenotypes in both rodents and humans. Consequently, FKBP5 blockade may hold promise as treatment intervention for stress-related disorders, and recently developed selective FKBP5 blockers show encouraging results in vitro and in rodent models. Although risk for stress-related disorders is conferred by multiple environmental and genetic factors, the findings related to FKBP5 illustrate how a deeper understanding of the molecular and systemic mechanisms underlying specific gene-environment interactions may provide insights into the pathogenesis of stress-related disorders.

A Framework for Interpreting Type I Error Rates from a Product-Term Model of Interaction Applied to Quantitative Traits

Adequate control of type I error rates will be necessary in the increasing genome-wide search for interactive effects on complex traits. After observing unexpected variability in type I error rates from SNP-by-genome interaction scans, we sought to characterize this variability and test the ability of heteroskedasticity-consistent standard errors to correct it. We performed 81 SNP-by-genome interaction scans using a product-term model on quantitative traits in a sample of 1,053 unrelated European Americans from the NHLBI Family Heart Study, and additional scans on five simulated datasets. We found that the interaction-term genomic inflation factor (lambda) showed inflation and deflation that varied with sample size and allele frequency; that similar lambda variation occurred in the absence of population substructure; and that lambda was strongly related to heteroskedasticity but not to minor non-normality of phenotypes. Heteroskedasticity-consistent standard errors narrowed the range of lambda, with HC3 outperforming HC0, but in individual scans tended to create new P-value outliers related to sparse two-locus genotype classes. We explain the lambda variation as a result of non-independence of test statistics coupled with stochastic biases in test statistics due to a failure of the test to reach asymptotic properties. We propose that one way to interpret lambda is by comparison to an empirical distribution generated from data simulated under the null hypothesis and without population substructure. We further conclude that the interaction-term lambda should not be used to adjust test statistics and that heteroskedasticity-consistent standard errors come with limitations that may outweigh their benefits in this setting.

Gene × Environment Determinants of Stress- and Anxiety-Related Disorders

The burgeoning field of gene-by-environment (G×E) interactions has revealed fascinating biological insights, particularly in the realm of stress-, anxiety-, and depression-related disorders. In this review we present an integrated view of the study of G×E interactions in stress and anxiety disorders, including the evolution of genetic association studies from genetic epidemiology to contemporary large-scale genome-wide association studies and G×E studies. We convey the importance of consortia efforts and collaboration to gain the large sample sizes needed to move the field forward. Finally, we discuss several robust and well-reproduced G×E interactions and demonstrate how epidemiological identification of G×E interactions has naturally led to a plethora of basic research elucidating the mechanisms of high-impact genetic variants.

The Psychiatric Genomics Consortium Posttraumatic Stress Disorder Workgroup: Posttraumatic Stress Disorder Enters the Age of Large-Scale Genomic Collaboration

The development of posttraumatic stress disorder (PTSD) is influenced by genetic factors. Although there have been some replicated candidates, the identification of risk variants for PTSD has lagged behind genetic research of other psychiatric disorders such as schizophrenia, autism, and bipolar disorder. Psychiatric genetics has moved beyond examination of specific candidate genes in favor of the genome-wide association study (GWAS) strategy of very large numbers of samples, which allows for the discovery of previously unsuspected genes and molecular pathways. The successes of genetic studies of schizophrenia and bipolar disorder have been aided by the formation of a large-scale GWAS consortium: the Psychiatric Genomics Consortium (PGC). In contrast, only a handful of GWAS of PTSD have appeared in the literature to date. Here we describe the formation of a group dedicated to large-scale study of PTSD genetics: the PGC-PTSD. The PGC-PTSD faces challenges related to the contingency on trauma exposure and the large degree of ancestral genetic diversity within and across participating studies. Using the PGC analysis pipeline supplemented by analyses tailored to address these challenges, we anticipate that our first large-scale GWAS of PTSD will comprise over 10 000 cases and 30 000 trauma-exposed controls. Following in the footsteps of our PGC forerunners, this collaboration-of a scope that is unprecedented in the field of traumatic stress-will lead the search for replicable genetic associations and new insights into the biological underpinnings of PTSD.

Epigenetics of Stress-Related Psychiatric Disorders and Gene × Environment Interactions

A deeper understanding of the pathomechanisms leading to stress-related psychiatric disorders is important for the development of more efficient preventive and therapeutic strategies. Epidemiological studies indicate a combined contribution of genetic and environmental factors in the risk for disease. The environment, particularly early life severe stress or trauma, can lead to lifelong molecular changes in the form of epigenetic modifications that can set the organism off on trajectories to health or disease. Epigenetic modifications are capable of shaping and storing the molecular response of a cell to its environment as a function of genetic predisposition. This provides a potential mechanism for gene-environment interactions. Here, we review epigenetic mechanisms associated with the response to stress and trauma exposure and the development of stress-related psychiatric disorders. We also look at how they may contribute to our understanding of the combined effects of genetic and environmental factors in shaping disease risk.

Current advances in biosampling for therapeutic drug monitoring of psychiatric CNS drugs

Many CNS drugs are effective for the treatment of psychiatric disorders. Psychotropic drugs work differently, thus clinical outcomes for many patients may be insufficient. For this reason it could be useful the measurement of drug levels for clinical decision-making. Analytical goals in therapeutic drug monitoring (TDM) should be established by selecting the appropriate biological matrix. The aim of this review is to highlight the usefulness of TDM for antiepileptics, antidepressants and antipsychotics, with a focus on current advances in biosampling. The literature on TDM was reviewed up to March 2015. An overview on the use of alternative biological matrices is provided to address the current issues and advances in the field of biosampling for psychiatric CNS drug TDM.

A genome-wide identified risk variant for PTSD is a methylation quantitative trait locus and confers decreased cortical activation to fearful faces

Genetic factors appear to be highly relevant to predicting differential risk for the development of post-traumatic stress disorder (PTSD). In a discovery sample, we conducted a genome-wide association study (GWAS) for PTSD using a small military cohort (Systems Biology PTSD Biomarkers Consortium; SBPBC, N = 147) that was designed as a case-controlled sample of highly exposed, recently returning veterans with and without combat-related PTSD. A genome-wide significant single nucleotide polymorphism (SNP), rs717947, at chromosome 4p15 (N = 147, β = 31.34, P = 1.28 × 10(-8) ) was found to associate with the gold-standard diagnostic measure for PTSD (the Clinician Administered PTSD Scale). We conducted replication and follow-up studies in an external sample, a larger urban community cohort (Grady Trauma Project, GTP, N = 2006), to determine the robustness and putative functionality of this risk variant. In the GTP replication sample, SNP rs717947 associated with PTSD diagnosis in females (N = 2006, P = 0.005), but not males. SNP rs717947 was also found to be a methylation quantitative trait locus (meQTL) in the GTP replication sample (N = 157, P = 0.002). Further, the risk allele of rs717947 was associated with decreased medial and dorsolateral cortical activation to fearful faces (N = 53, P < 0.05) in the GTP replication sample. These data identify a genome-wide significant polymorphism conferring risk for PTSD, which was associated with differential epigenetic regulation and with differential cortical responses to fear in a replication sample. These results may provide new insight into understanding genetic and epigenetic regulation of PTSD and intermediate phenotypes that contribute to this disorder.