Gene-environment interactions in genome-wide association studies: current approaches and new directions

Genome-Wide Gene-Environment Interaction Analysis Identifies Novel Candidate Variants for Growth Traits in Beef Cattle

Complex traits are widely considered to be the result of a compound regulation of genes, environmental factors, and genotype-by-environment interaction (G × E). The inclusion of G × E in genome-wide association analyses is essential to understand animal environmental adaptations and improve the efficiency of breeding decisions. Here, we systematically investigated the G × E of growth traits (including weaning weight, yearling weight, 18-month body weight, and 24-month body weight) with environmental factors (farm and temperature) using genome-wide genotype-by-environment interaction association studies (GWEIS) with a dataset of 1350 cattle. We validated the robust estimator's effectiveness in GWEIS and detected 29 independent interacting SNPs with a significance threshold of 1.67 × 10-6, indicating that these SNPs, which do not show main effects in traditional genome-wide association studies (GWAS), may have non-additive effects across genotypes but are obliterated by environmental means. The gene-based analysis using MAGMA identified three genes that overlapped with the GEWIS results exhibiting G × E, namely SMAD2, PALMD, and MECOM. Further, the results of functional exploration in gene-set analysis revealed the bio-mechanisms of how cattle growth responds to environmental changes, such as mitotic or cytokinesis, fatty acid β-oxidation, neurotransmitter activity, gap junction, and keratan sulfate degradation. This study not only reveals novel genetic loci and underlying mechanisms influencing growth traits but also transforms our understanding of environmental adaptation in beef cattle, thereby paving the way for more targeted and efficient breeding strategies.

GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

BACKGROUND

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

RESULTS

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

CONCLUSIONS

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Leveraging GWAS: Path to Prevention?

Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7.

Escherichia coli triggers α-synuclein pathology in the LRRK2 transgenic mouse model of PD

Alpha-synuclein (α-syn) pathology is the hallmark of Parkinson's disease (PD). The leucine-rich repeat kinase 2 (LRRK2) gene is a major-effect risk gene for sporadic PD (sPD). However, what environmental factors may trigger the formation of α-syn pathology in carriers of LRRK2 risk variants are still unknown. Here, we report that a markedly increased abundance of Escherichia coli (E. coli) in the intestinal microbiota was detected in LRRK2 risk variant(R1628P or G2385R) carriers with sPD compared with carriers without sPD. Animal experiments showed that E. coli administration triggered pathological α-syn accumulation in the colon and spread to the brain via the gut-brain axis in Lrrk2 R1628P mice, due to the co-occurrence of Lrrk2 variant-induced inhibition of α-syn autophagic degradation and increased phosphorylation of α-syn caused by curli in E. coli-derived extracellular vesicles. Fecal microbiota transplantation (FMT) effectively ameliorated motor deficits and α-syn pathology in Lrrk2 R1628P mice. Our findings elaborate on the mechanism that E. coli triggers α-syn pathology in Lrrk2 R1628P mice, and highlight a novel gene-environment interaction pattern in LRRK2 risk variants. Even more importantly, the findings reveal the interplay between the specific risk gene and the matched environmental factors triggers the initiation of α-syn pathology in sPD.

Environmental pathways affecting gene expression (E.PAGE) as an R package to predict gene-environment associations

The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. The initial database search run on the Gene Expression Omnibus and the Molecular Signature Database retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 237 datasets were selected and 522 gene modules were manually annotated. We then curated a database containing four environmental factors, cigarette smoking, diet, infections and toxic chemicals, along with a total of 25,789 genes that had an association with one or more of gene modules. The database and statistical analysis package was then tested with the differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure and smoking. On testing, we uncovered statistically enriched biological processes, which revealed pathways associated with environmental factors and the genes. The curated database and enrichment tool are available as R packages at https://github.com/AhmedMehdiLab/E.PATH and https://github.com/AhmedMehdiLab/E.PAGE respectively.

Genome-microbiome interplay provides insight into the determinants of the human blood metabolome

Variation in the blood metabolome is intimately related to human health. However, few details are known about the interplay between genetics and the microbiome in explaining this variation on a metabolite-by-metabolite level. Here, we perform analyses of variance for each of 930 blood metabolites robustly detected across a cohort of 1,569 individuals with paired genomic and microbiome data while controlling for a number of relevant covariates. We find that 595 (64%) of these blood metabolites are significantly associated with either host genetics or the gut microbiome, with 69% of these associations driven solely by the microbiome, 15% driven solely by genetics and 16% under hybrid genome-microbiome control. Additionally, interaction effects, where a metabolite-microbe association is specific to a particular genetic background, are quite common, albeit with modest effect sizes. This knowledge will help to guide targeted interventions designed to alter the composition of the human blood metabolome.

The Impact of ACEs on BMI: An Investigation of the Genotype-Environment Effects of BMI

Adverse Childhood Experiences are stressful and traumatic events occurring before the age of eighteen shown to cause mental and physical health problems, including increased risk of obesity. Obesity remains an ongoing national challenge with no predicted solution. We examine a subset of the Healthy Nevada Project, focusing on a multi-ethnic cohort of 15,886 sequenced participants with recalled adverse childhood events, to study how ACEs and their genotype-environment interactions affect BMI. Specifically, the Healthy Nevada Project participants sequenced by the Helix Exome+ platform were cross-referenced to their electronic medical records and social health determinants questionnaire to identify: 1) the effect of ACEs on BMI in the absence of genetics; 2) the effect of genotype-environment interactions on BMI; 3) how these gene-environment interactions differ from standard genetic associations of BMI. The study found very strong significant associations between the number of adverse childhood experiences and adult obesity. Additionally, we identified fifty-five common and rare variants that exhibited gene-interaction effects including three variants in the CAMK1D gene and four variants in LHPP; both genes are linked to schizophrenia. Surprisingly, none of the variants identified with interactive effects were in canonical obesity-related genes. Here we show the delicate balance between genes and environment, and how the two strongly influence each other.

Early adversity as the prototype gene × environment interaction in mental disorders?

Childhood adversity (CA) as a significant stressor has consistently been associated with the development of mental disorders. The interaction between CA and genetic variants has been proposed to play a substantial role in disease etiology. In this review, we focus on the gene by environment (GxE) paradigm, its background and interpretation and stress the necessity of its implementation in psychiatric research. Further, we discuss the findings supporting GxCA interactions, ranging from candidate gene studies to polygenic and genome-wide approaches, their strengths and limitations. To illustrate potential underlying epigenetic mechanisms by which GxE effects are translated, we focus on results from FKBP5 × CA studies and discuss how molecular evidence can supplement previous GxE findings. In conclusion, while GxE studies constitute a valuable line of investigation, more harmonized GxE studies in large, deep-phenotyped, longitudinal cohorts, and across different developmental stages are necessary to further substantiate and understand reported GxE findings.

Genome-wide association of single nucleotide polymorphism loci and candidate genes for frogeye leaf spot (Cercospora sojina) resistance in soybean

BACKGROUND

Frogeye leaf spot (FLS) is a destructive fungal disease that affects soybean production. The most economical and effective strategy to control FLS is the use of resistant cultivars. However, the use of a limited number of resistant loci in FLS management will be countered by the emergence of new high-virulence Cercospora sojina races. Therefore, we identified quantitative trait loci (QTL) that control resistance to FLS and identified novel resistant genes using a genome-wide association study (GWAS) on 234 Chinese soybean cultivars.

RESULTS

A total of 30,890 single nucleotide polymorphism (SNP) markers were used to estimate linkage disequilibrium (LD) and population structure. The GWAS results showed four loci (p < 0.0001) distributed over chromosomes (Chr.) 5 and 20, that are significantly associated with FLS resistance. No previous studies have reported resistance loci in these regions. Subsequently, 45 genes in the two resistance-related haplotype blocks were annotated. Among them, Glyma20g31630 encoding pyruvate dehydrogenase (PDH), Glyma05g28980, which encodes mitogen-activated protein kinase 7 (MPK7), and Glyma20g31510, Glyma20g31520 encoding calcium-dependent protein kinase 4 (CDPK4) in the haplotype blocks deserves special attention.

CONCLUSIONS

This study showed that GWAS can be employed as an effective strategy for identifying disease resistance traits in soybean and narrowing SNPs and candidate genes. The prediction of candidate genes in the haplotype blocks identified by disease resistance loci can provide a useful reference to study systemic disease resistance.

Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review

Understanding the biological mechanisms underlying tick resistance in cattle holds the potential to facilitate genetic improvement through selective breeding. Genome wide association studies (GWAS) are popular in research on unraveling genetic determinants underlying complex traits such as tick resistance. To date, various studies have been published on single nucleotide polymorphisms (SNPs) associated with tick resistance in cattle. The discovery of SNPs related to tick resistance has led to the mapping of associated candidate genes. Despite the success of these studies, information on genetic determinants associated with tick resistance in cattle is still limited. This warrants the need for more studies to be conducted. In Africa, the cost of genotyping is still relatively expensive; thus, conducting GWAS is a challenge, as the minimum number of animals recommended cannot be genotyped. These population size and genotype cost challenges may be overcome through the establishment of collaborations. Thus, the current review discusses GWAS as a tool to uncover SNPs associated with tick resistance, by focusing on the study design, association analysis, factors influencing the success of GWAS, and the progress on cattle tick resistance studies.

A genetic sum score of effect alleles associated with serum lipid concentrations interacts with educational attainment

High-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC) levels are influenced by both genes and the environment. The aim was to investigate whether education and income as indicators of socioeconomic position (SEP) interact with lipid-increasing genetic effect allele scores (GES) in a population-based cohort. Using baseline data of 4516 study participants, age- and sex-adjusted linear regression models were fitted to investigate associations between GES and lipids stratified by SEP as well as including GES×SEP interaction terms. In the highest education group compared to the lowest stronger effects per GES standard deviation were observed for HDL-C (2.96 mg/dl [95%-CI: 2.19, 3.83] vs. 2.45 mg/dl [95%-CI: 1.12, 3.72]), LDL-C (6.57 mg/dl [95%-CI: 4.73, 8.37] vs. 2.66 mg/dl [95%-CI: −0.50, 5.76]) and TC (8.06 mg/dl [95%-CI: 6.14, 9.98] vs. 4.37 mg/dl [95%-CI: 0.94, 7.80]). Using the highest education group as reference, interaction terms showed indication of GES by low education interaction for LDL-C (ß_{GES×Education}: −3.87; 95%-CI: −7.47, −0.32), which was slightly attenuated after controlling for GES_LDL-C×Diabetes interaction (ß_{GES×Education}: −3.42; 95%-CI: −6.98, 0.18). The present study showed stronger genetic effects on LDL-C in higher SEP groups and gave indication for a GES_LDL-C×Education interaction, demonstrating the relevance of SEP for the expression of genetic health risks.

Long-term antibiotic use during early life and risks to mental traits: an observational study and gene-environment-wide interaction study in UK Biobank cohort

The relationships between long-term antibiotic use during early life and mental traits remain elusive now. A total of 158,444 subjects from UK Biobank were used in this study. Linear regression analyses were first conducted to assess the correlations between long-term antibiotic use during early life and mental traits. Gene-environment-wide interaction study (GEWIS) was then performed by PLINK2.0 to detect the interaction effects between long-term antibiotic use during early life and genes on the risks of mental traits. Finally, DAVID tool was used to conduct gene ontology (GO) analysis of the identified genes interacting with long-term antibiotic use during early life. We found negative associations of long-term antibiotic use during early life with remembrance (p value=1.74 × 10^-6, b = -0.10) and intelligence (p value=2.64 × 10^-26, b = -0.13), and positive associations of long-term antibiotic use during early life with anxiety (p value = 2.75 × 10^-47, b = 0.12) and depression (p value=2.01 × 10^-195, b = 0.25). GEWIS identified multiple significant genes-long-term antibiotic use during early life interaction effects, such as ANK3 (rs773585997, p value = 1.78 × 10^-8) for anxiety and STRN (rs140049205, p value = 1.88 × 10^-8) for depression. GO enrichment analysis detected six GO terms enriched in the identified genes interacting with long-term antibiotic use during early life for anxiety, such as GO:0030425~dendrite (p value = 3.41 × 10^-2) and GO:0005886~plasma membrane (p value = 3.64 × 10^-3). Our study results suggest the impact of long-term antibiotic use during early life on the development of mental traits.

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.

Gene-Environment Interaction: A Variable Selection Perspective

Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.

Polygenic approaches to detect gene-environment interactions when external information is unavailable

The exploration of 'gene-environment interactions' (G × E) is important for disease prediction and prevention. The scientific community usually uses external information to construct a genetic risk score (GRS), and then tests the interaction between this GRS and an environmental factor (E). However, external genome-wide association studies (GWAS) are not always available, especially for non-Caucasian ethnicity. Although GRS is an analysis tool to detect G × E in GWAS, its performance remains unclear when there is no external information. Our 'adaptive combination of Bayes factors method' (ADABF) can aggregate G × E signals and test the significance of G × E by a polygenic test. We here explore a powerful polygenic approach for G × E when external information is unavailable, by comparing our ADABF with the GRS based on marginal effects of SNPs (GRS-M) and GRS based on SNP × E interactions (GRS-I). ADABF is the most powerful method in the absence of SNP main effects, whereas GRS-M is generally the best test when single-nucleotide polymorphisms main effects exist. GRS-I is the least powerful test due to its data-splitting strategy. Furthermore, we apply these methods to Taiwan Biobank data. ADABF and GRS-M identified gene × alcohol and gene × smoking interactions on blood pressure (BP). BP-increasing alleles elevate more BP in drinkers (smokers) than in nondrinkers (nonsmokers). This work provides guidance to choose a polygenic approach to detect G × E when external information is unavailable.

Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions

The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Background

Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification.

Results

Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation.

Conclusion

PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

Opinion: Is gene mapping in wild populations useful for understanding and predicting adaptation to global change?

Changing environmental conditions will inevitably alter selection pressures. Over the long term, populations have to adapt to these altered conditions by evolutionary change to avoid extinction. Quantifying the 'evolutionary potential' of populations to predict whether they will be able to adapt fast enough to forecasted changes is crucial to fully assess the threat for biodiversity posed by climate change. Technological advances in sequencing and high-throughput genotyping have now made genomic studies possible in a wide range of species. Such studies, in theory, allow an unprecedented understanding of the genomics of ecologically relevant traits and thereby a detailed assessment of the population's evolutionary potential. Aimed at a wider audience than only evolutionary geneticists, this paper gives an overview of how gene-mapping studies have contributed to our understanding and prediction of evolutionary adaptations to climate change, identifies potential reasons why their contribution to understanding adaptation to climate change may remain limited, and highlights approaches to study and predict climate change adaptation that may be more promising, at least in the medium term.

The potential role of clock genes in children attention-deficit/hyperactivity disorder

BACKGROUND

Attention deficit/ hyperactivity disorder (ADHD) is a chronic neurodevelopmental disorder and is thought to be associated with circadian system.

METHODS

We performed a pathway-based study to test individual single nucleotide polymorphisms (SNPs) and the overall evidence of genetic polymorphisms involved in the circadian pathway in association with children ADHD susceptibility among a Chinese population. A community-based case-control study was conducted among Chinese children, and 168 ADHD patients and 233 controls were recruited using a combination diagnosis based on the diagnostic and statistical manual of mental disorders iv (DSM-IV) ADHD rating scale, Swanson, Nolan, and Pelham rating scale (SNAP-IV) rating scale, and semi-structured clinical interview.

RESULTS

The results of single-loci analyses identified that PER1 rs2518023 and ARNTL2 rs2306074 were nominally association with ADHD susceptibility (P < 0.05). Next, we applied multifactor dimensionality reduction (MDR), and classification and regression tree (CART) analyses to explore high-order gene-gene interactions among the functional SNPs to ADHD risks. The results indicated that interactions among the PER1 rs2518023, ARNTL2 rs2306074 and NR1D1 rs939347 were associated with the risk of ADHD in children. Individuals carrying the combination genotypes of the PER1 rs2518023 GG or GT, ARNTL2 rs2306074 TC or TT and NR1D1 rs939347 GA or AA displayed a significantly higher risk for ADHD than who carry the PER1 rs2518023 TT and CRY2 rs2292910 CA/CC genotypes (adjusted OR = 4.37, 95% CI = 2.16-8.85, P < 0.001).

CONCLUSIONS

These findings revealed the importance of genetic variations related to the circadian clock system to the susceptibility of children ADHD.

Gene expression in peripheral blood in treatment-free major depression

BACKGROUND

Peripheral gene expression of several molecular pathways has been studied in major depressive disorder (MDD) with promising results. We sought to investigate some of these genes in a treatment-free Latino sample of Mexican descent.

MATERIAL AND METHODS

The sample consisted of 50 MDD treatment-free cases and 50 sex and age-matched controls. Gene expression of candidate genes of neuroplasticity (BDNF, p11, and VGF), inflammation (IL1A, IL1B, IL4, IL6, IL7, IL8, IL10, MIF, and TNFA), the canonical Wnt signaling pathway (TCF7L2, APC, and GSK3B), and mTOR, was compared in cases and controls. RNA was obtained from blood samples. We used bivariate analyses to compare subjects versus control mean mRNA quantification of target genes and lineal regression modelling to test for effects of age and body mass index on gene expression.

RESULTS

Most subjects were female (66%) with a mean age of 26.7 (SD 7.9) years. Only GSK3B was differentially expressed between cases and controls at a statistically significant level (p = 0.048). TCF7L-2 showed the highest number of correlations with MDD-related traits, yet these were modest in size.

DISCUSSION

GSK3B encodes a moderator of the canonical Wnt signaling pathway. It has a role in neuroplasticity, neuroprotection, depression, and other psychiatric phenotypes. We found that adding population diversity has the potential to elicit distinct peripheral gene expression markers in MDD and MDD-related traits. However, our results should only be considered as hypothesis-generating research that merits further replication in larger cohorts of similar ancestry.

Isfahan Twins Registry (ITR): An Invaluable Platform for Epidemiological and Epigenetic Studies: Design and Methodology of ITR

Twin studies are one of the main tools for studying the interaction between genes and the environment in the development of complex diseases such as cancers, cardiovascular diseases and diabetes. The Isfahan Twin Registry (ITR) was launched in Isfahan in 2017 as a pilot study to establish a nationwide twin registry in Iran and aims to obtain comprehensive information about complex diseases and their risk factors from twins and multiples living in Isfahan. ITR will continue to recruit twins and multiples until all twins residing in Isfahan are registered in the registry. Twins are identified from welfare agencies, public health homes, maternity hospitals, Persian Twins Association and the local media. Demographic information, twin similarities, lifestyle, family history of diseases and past medical history are collected using validated questionnaires. Anthropometric measurements and blood pressure are measured by health professionals. Hematology panel, fasting blood sugar, total cholesterol, low-density lipoprotein, high-density lipoprotein, aspartate aminotransferase, alanine aminotransferase and quantitative C-reactive protein are measured by an automated analyzer. Extra samples are obtained for future studies. For twins aged under 6 years, parents complete the questionnaires for their children and a brief questionnaire for themselves. Currently, 998 persons (395 pairs and 67 multiples) are registered in the ITR and have provided their data. Results of preliminary data analysis are discussed in this article. We plan to carry out longitudinal assessments. ITR can play an important role in future epigenetic, biomarkers and omics studies using the biobank materials.

A Fast and Accurate Method for Genome-wide Scale Phenome-wide G × E Analysis and Its Application to UK Biobank

The etiology of most complex diseases involves genetic variants, environmental factors, and gene-environment interaction (G × E) effects. Compared with marginal genetic association studies, G × E analysis requires more samples and detailed measure of environmental exposures, and this limits the possible discoveries. Large-scale population-based biobanks with detailed phenotypic and environmental information, such as UK-Biobank, can be ideal resources for identifying G × E effects. However, due to the large computation cost and the presence of case-control imbalance, existing methods often fail. Here we propose a scalable and accurate method, SPAGE (SaddlePoint Approximation implementation of G × E analysis), that is applicable for genome-wide scale phenome-wide G × E studies. SPAGE fits a genotype-independent logistic model only once across the genome-wide analysis in order to reduce computation cost, and SPAGE uses a saddlepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanced case-control ratios. Simulation studies show that SPAGE is 33-79 times faster than the Wald test and 72-439 times faster than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level even when case-control ratios are extremely unbalanced. Through the analysis of UK-Biobank data of 344,341 white British European-ancestry samples, we show that SPAGE can efficiently analyze large samples while controlling for unbalanced case-control ratios.

A genetic sum score of risk alleles associated with body mass index interacts with socioeconomic position in the Heinz Nixdorf Recall Study

Body mass index (BMI) is influenced by genetic, behavioral and environmental factors, while interactions between genetic and socioeconomic factors have been suggested. Aim of the study was to investigate whether socioeconomic position (SEP) interacts with a BMI-related genetic sum score (GRS_BMI) to affect BMI in a population-based cohort. SEP-related health behaviors and a GRS associated with educational attainment (GRS_Edu) were included in the analysis to explore potential interactions underlying the GRS_BMIxSEP effect. Baseline information on SEP indicators (education, income), BMI, smoking, physical activity, alcohol consumption and genetic risk factors were available for 4,493 participants of the Heinz Nixdorf Recall Study. Interaction analysis was based on linear regression as well as on stratified analyses. In SEP-stratified analyses, the highest genetic effects were observed in the lowest educational group with a 0.24 kg/m² higher BMI (95%CI: 0.16; 0.31) and in the lowest income quartile with a 0.14 kg/m² higher BMI (95%CI: 0.09; 0.18) per additional risk allele. Indication for a GRS_BMIxSEP interaction was observed for education (ß_{GRSbmixeducation} = -0.02 [95%CI:-0.03; -0.01]) and income (ß_{GRSbmixincome} = -0.05 [95%CI: -0.08; -0.02]). When adjusting for interactions with the GRS_Edu and SEP-related health behaviors, effect size estimates of the GRS_BMIxSEP interaction remained virtually unchanged. Results gave indication for an interaction of BMI-related genetic risk factors with SEP indicators, showing substantially stronger genetic effects in low SEP groups. This supports the hypothesis that expression of genetic risks is higher in socioeconomically disadvantaged environments. No indication was observed that the GRS_BMIxSEP interaction was affected by other SEP-related factors included in the analysis.

Clinical phenotype and genetic risk factors for bipolar disorder with binge eating: an update

Introduction: Clinical and genetic study of psychiatric conditions has underscored the co-occurrence of complex phenotypes and the need to refine them. Bipolar Disorder (BD) and Binge Eating (BE) behavior are common psychiatric conditions that have high heritability and high co-occurrence, such that at least one quarter of BD patients have BE (BD + BE). Genetic studies of BD alone and of BE alone suggest complex polygenic risk models, with many genetic risk loci yet to be identified. Areas covered: We review studies of the epidemiology of BD+BE, its clinical features (cognitive traits, psychiatric comorbidity, and role of obesity), genomic studies (of BD, eating disorders (ED) defined by BE, and BD + BE), and therapeutic implications of BD + BE. Expert opinion: Subphenotyping of complex psychiatric disorders reduces heterogeneity and increases statistical power and effect size; thus, it enhances our capacity to find missing genetic (and other) risk factors. BD + BE has a severe clinical picture and genetic studies suggests a distinct genetic architecture. Differential therapeutic interventions may be needed for patients with BD + BE compared with BD patients without BE. Recognizing the BD + BE subphenotype is an example of moving towards more precise clinical and genetic entities.

The UKB envirome of depression: from interactions to synergistic effects

Major depressive disorder is a result of the complex interplay between a large number of environmental and genetic factors but the comprehensive analysis of contributing environmental factors is still an open challenge. The primary aim of this work was to create a Bayesian dependency map of environmental factors of depression, including life stress, social and lifestyle factors, using the UK Biobank data to determine direct dependencies and to characterize mediating or interacting effects of other mental health, metabolic or pain conditions. As a complementary approach, we also investigated the non-linear, synergistic multi-factorial risk of the UKB envirome on depression using deep neural network architectures. Our results showed that a surprisingly small number of core factors mediate the effects of the envirome on lifetime depression: neuroticism, current depressive symptoms, parental depression, body fat, while life stress and household income have weak direct effects. Current depressive symptom showed strong or moderate direct relationships with life stress, pain conditions, falls, age, insomnia, weight change, satisfaction, confiding in someone, exercise, sports and Townsend index. In conclusion, the majority of envirome exerts their effects in a dynamic network via transitive, interactive and synergistic relationships explaining why environmental effects may be obscured in studies which consider them individually.

Improving pharmacogenetic prediction of extrapyramidal symptoms induced by antipsychotics

In previous work we developed a pharmacogenetic predictor of antipsychotic (AP) induced extrapyramidal symptoms (EPS) based on four genes involved in mTOR regulation. The main objective is to improve this predictor by increasing its biological plausibility and replication. We re-sequence the four genes using next-generation sequencing. We predict functionality "in silico" of all identified SNPs and test it using gene reporter assays. Using functional SNPs, we develop a new predictor utilizing machine learning algorithms (Discovery Cohort, N = 131) and replicate it in two independent cohorts (Replication Cohort 1, N = 113; Replication Cohort 2, N = 113). After prioritization, four SNPs were used to develop the pharmacogenetic predictor of AP-induced EPS. The model constructed using the Naive Bayes algorithm achieved a 66% of accuracy in the Discovery Cohort, and similar performances in the replication cohorts. The result is an improved pharmacogenetic predictor of AP-induced EPS, which is more robust and generalizable than the original.

Keeping Pace with the Red Queen: Identifying the Genetic Basis of Susceptibility to Infectious Disease

The results of genome-wide association studies are known to be affected by epistasis and gene-by-environment interactions. Using a statistical model....

Genome-wide association studies are widely used to identify “disease genes” conferring resistance/susceptibility to infectious diseases. Using a combination of mathematical models and simulations, we demonstrate that genetic interactions between hosts and parasites [genotype-by-genotype (G × G) interactions] can drastically affect the results of these association scans and hamper our ability to detect genetic variation in susceptibility. When hosts and parasites coevolve, these G × G interactions often make genome-wide association studies unrepeatable over time or across host populations. Reanalyzing previously published data on Daphnia magna susceptibility to infection by Pasteuria ramosa, we identify genomic regions consistent with G × G interactions. We conclude by outlining possible avenues for designing more powerful and more repeatable association studies.

Gene-based interaction analysis shows GABAergic genes interacting with parenting in adolescent depressive symptoms

BACKGROUND

Most gene-environment interaction studies (G × E) have focused on single candidate genes. This approach is criticized for its expectations of large effect sizes and occurrence of spurious results. We describe an approach that accounts for the polygenic nature of most psychiatric phenotypes and reduces the risk of false-positive findings. We apply this method focusing on the role of perceived parental support, psychological control, and harsh punishment in depressive symptoms in adolescence.

METHODS

Analyses were conducted on 982 adolescents of Caucasian origin (M_age (SD) = 13.78 (.94) years) genotyped for 4,947 SNPs in 263 genes, selected based on a literature survey. The Leuven Adolescent Perceived Parenting Scale (LAPPS) and the Parental Behavior Scale (PBS) were used to assess perceived parental psychological control, harsh punishment, and support. The Center for Epidemiologic Studies Depression Scale (CES-D) was the outcome. We used gene-based testing taking into account linkage disequilibrium to identify genes containing SNPs exhibiting an interaction with environmental factors yielding a p-value per single gene. Significant results at the corrected p-value of p < 1.90 × 10^-4 were examined in an independent replication sample of Dutch adolescents (N = 1354).

RESULTS

Two genes showed evidence for interaction with perceived support: GABRR1 (p = 4.62 × 10^-5 ) and GABRR2 (p = 9.05 × 10^-6 ). No genes interacted significantly with psychological control or harsh punishment. Gene-based analysis was unable to confirm the interaction of GABRR1 or GABRR2 with support in the replication sample. However, for GABRR2, but not GABRR1, the correlation of the estimates between the two datasets was significant (r (46) = .32; p = .027) and a gene-based analysis of the combined datasets supported GABRR2 × support interaction (p = 1.63 × 10^-4 ).

CONCLUSIONS

We present a gene-based method for gene-environment interactions in a polygenic context and show that genes interact differently with particular aspects of parenting. This accentuates the importance of polygenic approaches and the need to accurately assess environmental exposure in G × E.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

BACKGROUND

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data.

SCOPE OF REVIEW

1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data.

MAJOR CONCLUSIONS

To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions.

GENERAL SIGNIFICANCE

Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.

Genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson's disease

INTRODUCTION

Genetic factors and environmental exposures, including pesticides, contribute to the risk of Parkinson's disease (PD). There have been few studies of gene and pesticide exposure interactions in PD, and all of the prior studies used a candidate gene approach.

METHODS

We performed the first genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson's disease. Analyses were performed using data on >700,000 single nucleotide polymorphisms (SNPs) in 364 discordant sibling pairs. In addition to testing for SNP-pesticide interaction effects, we also performed exploratory analyses of gene-pesticide interactions at the gene level.

RESULTS

None of the gene-environment interaction results were significant after genome-wide correction for multiple testing (α = 1.5E-07 for SNP-level tests; α = 2.1E-06 for gene-level tests). Top results in the SNP-level tests provided suggestive evidence (P < 5.0E-06) that the effect of pesticide exposure on PD risk may be modified by SNPs in the ERCC6L2 gene (P = 2.4E-06), which was also supported by suggestive evidence in the gene-level analysis (P = 4.7E-05). None of the candidate genes assessed in prior studies of gene-pesticide interactions reached statistical support in this genome-wide screen.

CONCLUSION

Although no significant interactions were identified, several of the genes with suggestive evidence of gene-environment interaction effects have biological plausibility for PD risk. Further investigation of the role of those genes in PD risk, particularly in the context of pesticide exposure, in large and carefully recruited samples is warranted.

Beyond main effects of gene-sets: harsh parenting moderates the association between a dopamine gene-set and child externalizing behavior

BACKGROUND

In a longitudinal cohort study, we investigated the interplay of harsh parenting and genetic variation across a set of functionally related dopamine genes, in association with children's externalizing behavior. This is one of the first studies to employ gene-based and gene-set approaches in tests of Gene by Environment (G × E) effects on complex behavior. This approach can offer an important alternative or complement to candidate gene and genome-wide environmental interaction (GWEI) studies in the search for genetic variation underlying individual differences in behavior.

METHODS

Genetic variants in 12 autosomal dopaminergic genes were available in an ethnically homogenous part of a population-based cohort. Harsh parenting was assessed with maternal (n = 1881) and paternal (n = 1710) reports at age 3. Externalizing behavior was assessed with the Child Behavior Checklist (CBCL) at age 5 (71 ± 3.7 months). We conducted gene-set analyses of the association between variation in dopaminergic genes and externalizing behavior, stratified for harsh parenting.

RESULTS

The association was statistically significant or approached significance for children without harsh parenting experiences, but was absent in the group with harsh parenting. Similarly, significant associations between single genes and externalizing behavior were only found in the group without harsh parenting. Effect sizes in the groups with and without harsh parenting did not differ significantly. Gene-environment interaction tests were conducted for individual genetic variants, resulting in two significant interaction effects (rs1497023 and rs4922132) after correction for multiple testing.

CONCLUSION

Our findings are suggestive of G × E interplay, with associations between dopamine genes and externalizing behavior present in children without harsh parenting, but not in children with harsh parenting experiences. Harsh parenting may overrule the role of genetic factors in externalizing behavior. Gene-based and gene-set analyses offer promising new alternatives to analyses focusing on single candidate polymorphisms when examining the interplay between genetic and environmental factors.

Dose-dependent cannabis use, depressive symptoms, and FAAH genotype predict sleep quality in emerging adults: a pilot study

BACKGROUND

Cannabis has been shown to affect sleep in humans. Findings from animal studies indicate that higher endocannabinoid levels promote sleep, suggesting that chronic use of cannabis, which downregulates endocannabinoid activity, may disrupt sleep.

OBJECTIVES

This study sought to determine if past-year cannabis use and genes that regulate endocannabinoid signaling, FAAH rs324420 and CNR1 rs2180619, predicted sleep quality. As depression has been previously associated with both cannabis and sleep, the secondary aim was to determine if depressive symptoms moderated or mediated these relationships.

METHODS

Data were collected from 41 emerging adult (ages 18-25) cannabis users. Exclusion criteria included Axis I disorders (besides SUD) and medical and neurologic disorders. Relationships were tested using multiple regressions, controlling for demographic variables, past-year substance use, and length of cannabis abstinence.

RESULTS

Greater past-year cannabis use and FAAH C/C genotype were associated with poorer sleep quality. CNR1 genotype did not significantly predict sleep quality. Depressive symptoms moderated the relationship between cannabis use and sleep at a nonsignificant trend level, such that participants with the higher cannabis use and depressive symptoms reported the more impaired sleep. Depressive symptoms mediated the relationship between FAAH genotype and sleep quality.

CONCLUSIONS

This study demonstrates a dose-dependent relationship between chronic cannabis use and reported sleep quality, independent of abstinence length. Furthermore, it provides novel evidence that depressive symptoms mediate the relationship between FAAH genotype and sleep quality in humans. These findings suggest potential targets to impact sleep disruptions in cannabis users.

Genomic Bayesian Prediction Model for Count Data with Genotype × Environment Interaction

Genomic tools allow the study of the whole genome, and facilitate the study of genotype-environment combinations and their relationship with phenotype. However, most genomic prediction models developed so far are appropriate for Gaussian phenotypes. For this reason, appropriate genomic prediction models are needed for count data, since the conventional regression models used on count data with a large sample size (nT) and a small number of parameters (p) cannot be used for genomic-enabled prediction where the number of parameters (p) is larger than the sample size (nT). Here, we propose a Bayesian mixed-negative binomial (BMNB) genomic regression model for counts that takes into account genotype by environment (G×E) interaction. We also provide all the full conditional distributions to implement a Gibbs sampler. We evaluated the proposed model using a simulated data set, and a real wheat data set from the International Maize and Wheat Improvement Center (CIMMYT) and collaborators. Results indicate that our BMNB model provides a viable option for analyzing count data.

Integrating Genetic, Neuropsychological and Neuroimaging Data to Model Early-Onset Obsessive Compulsive Disorder Severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the training set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our understanding of the neurobiological basis of the disorder.

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.

Associations of prodynorphin sequence variation with alcohol dependence and related traits are phenotype-specific and sex-dependent

We previously demonstrated that prodynorphin (PDYN) haplotypes and single nucleotide polymorphism (SNP) rs2281285 are associated with alcohol dependence and the propensity to drink in negative emotional states, and recent studies suggest that PDYN gene effects on substance dependence risk may be sex-related. We examined sex-dependent associations of PDYN variation with alcohol dependence and related phenotypes, including negative craving, time until relapse after treatment and the length of sobriety episodes before seeking treatment, in discovery and validation cohorts of European ancestry. We found a significant haplotype-by-sex interaction (p  =  0.03), suggesting association with alcohol dependence in males (p = 1E-4) but not females. The rs2281285 G allele increased risk for alcohol dependence in males in the discovery cohort (OR = 1.49, p = 0.002), with a similar trend in the validation cohort (OR = 1.35, p = 0.086). However, rs2281285 showed a trend towards association with increased negative craving in females in both the discovery (beta = 10.16, p = 0.045) and validation samples (OR = 7.11, p = 0.066). In the discovery cohort, rs2281285 was associated with time until relapse after treatment in females (HR = 1.72, p = 0.037); in the validation cohort, it was associated with increased length of sobriety episodes before treatment in males (beta = 13.49, p = 0.001). Our findings suggest that sex-dependent effects of PDYN variants in alcohol dependence are phenotype-specific.

The Roles of Genes in the Neuronal Migration and Neurite Outgrowth Network in Developmental Dyslexia: Single- and Multiple-Risk Genetic Variants

Abnormal regulation of neural migration and neurite growth is thought to be an important feature of developmental dyslexia (DD). We investigated 16 genetic variants, selected by bioinformatics analyses, in six key genes in the neuronal migration and neurite outgrowth network in a Chinese population. We first observed that KIAA0319L rs28366021, KIAA0319 rs4504469, and DOCK4 rs2074130 were significantly associated with DD risk after false discovery rate (FDR) adjustment for multiple comparisons (odds ratio (OR) = 0.672, 95 % confidence interval (CI) = 0.505-0.894, P = 0.006; OR = 1.608, 95 % CI = 1.174-2.203, P = 0.003; OR = 1.681, 95 % CI = 1.203-2.348, P = 0.002). The following classification and regression tree (CART) analysis revealed a prediction value of gene-gene interactions among DOCK4 rs2074130, KIAA0319 rs4504469, DCDC2 rs2274305, and KIAA0319L rs28366021 variants. Compared with the lowest risk carriers of the combination of rs2074130 CC, rs4504469 CC, and rs2274305 GG genotype, individuals carrying the combined genotypes of rs2074130 CC, rs4504469 CT or TT, and rs28366021 GG had a significantly increased risk for DD (OR = 2.492, 95 % CI = 1.447-4.290, P = 0.001); individuals with the combination of rs2074130 CT or TT and rs28366021 GG genotype exhibited the highest risk for DD (OR = 2.770, 95 % CI = 2.265-6.276, P = 0.000). A significant dose effect was observed among these four variants (P for trend = 0.000). In summary, this study supports the importance of single- and multiple-risk variants in this network in DD susceptibility in China.

Association study of monoamine oxidase-A gene promoter polymorphism (MAOA-uVNTR) with self-reported anxiety and other psychopathological symptoms in a community sample of early adolescents

The polymorphism upstream of the gene for monoamine oxidase A (MAOA-uVNTR) is reported to be an important enzyme involved in human physiology and behavior. With a sample of 228 early-adolescents from a community sample (143 girls) and adjusting for environmental variables, we examined the influence of MAOA-uVNTR alleles on the scores obtained in the Screen for Childhood Anxiety and Related Emotional Disorders and in the Child Symptom Inventory-4. Our results showed that girls with the high-activity MAOA allele had higher scores for generalized and total anxiety than their low-activity peers, whereas boys with the low-activity allele had higher social phobia scores than boys with the high-activity allele. Results for conduct disorder symptoms did not show a significant relationship between the MAOA alleles and the presence of these symptoms. Our findings support a possible association, depending on gender, between the MAOA-uVNTR polymorphism and psychopathological disorders such as anxiety, which affects high rates of children and adolescents.

Genetics of cardiovascular disease: Importance of sex and ethnicity

Sex differences in incidence and prevalence of and morbidity and mortality from cardiovascular disease are well documented. However, many studies examining the genetic basis for cardiovascular disease fail to consider sex as a variable in the study design, in part, because there is an inherent difficulty in studying the contribution of the sex chromosomes in women due to X chromosome inactivation. This paper will provide general background on the X and Y chromosomes (including gene content, the pseudoautosomal regions, and X chromosome inactivation), discuss how sex chromosomes have been ignored in Genome-wide Association Studies (GWAS) of cardiovascular diseases, and discuss genetics influencing development of cardiovascular risk factors and atherosclerosis with particular attention to carotid intima-medial thickness, and coronary arterial calcification based on sex-specific studies. In addition, a brief discussion of how ethnicity and hormonal status act as confounding variables in sex-based analysis will be considered along with methods for statistical analysis to account for sex in cardiovascular disease.

Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression

Accounting for gene-environment (G×E) interactions in complex trait association studies can facilitate our understanding of genetic heterogeneity under different environmental exposures, improve the ability to discover susceptible genes that exhibit little marginal effect, provide insight into the biological mechanisms of complex diseases, help to identify high-risk subgroups in the population, and uncover hidden heritability. However, significant G×E interactions can be difficult to find. The sample sizes required for sufficient power to detect association are much larger than those needed for genetic main effects, and interactions are sensitive to misspecification of the main-effects model. These issues are exacerbated when working with binary phenotypes and rare variants, which bear less information on association. In this work, we present a similarity-based regression method for evaluating G×E interactions for rare variants with binary traits. The proposed model aggregates the genetic and G×E information across markers, using genetic similarity, thus increasing the ability to detect G×E signals. The model has a random effects interpretation, which leads to robustness against main-effect misspecifications when evaluating G×E interactions. We construct score tests to examine G×E interactions and a computationally efficient EM algorithm to estimate the nuisance variance components. Using simulations and data applications, we show that the proposed method is a flexible and powerful tool to study the G×E effect in common or rare variant studies with binary traits.

Is there a "metabolic-mood syndrome"? A review of the relationship between obesity and mood disorders

Obesity and mood disorders are highly prevalent and co-morbid. Epidemiological studies have highlighted the public health relevance of this association, insofar as both conditions and its co-occurrence are associated with a staggering illness-associated burden. Accumulating evidence indicates that obesity and mood disorders are intrinsically linked and share a series of clinical, neurobiological, genetic and environmental factors. The relationship of these conditions has been described as convergent and bidirectional; and some authors have attempted to describe a specific subtype of mood disorders characterized by a higher incidence of obesity and metabolic problems. However, the nature of this association remains poorly understood. There are significant inconsistencies in the studies evaluating metabolic and mood disorders; and, as a result, several questions persist about the validity and the generalizability of the findings. An important limitation in this area of research is the noteworthy phenotypic and pathophysiological heterogeneity of metabolic and mood disorders. Although clinically useful, categorical classifications in both conditions have limited heuristic value and its use hinders a more comprehensive understanding of the association between metabolic and mood disorders. A recent trend in psychiatry is to move toward a domain specific approach, wherein psychopathology constructs are agnostic to DSM-defined diagnostic categories and, instead, there is an effort to categorize domains based on pathogenic substrates, as proposed by the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC). Moreover, the substrates subserving psychopathology seems to be unspecific and extend into other medical illnesses that share in common brain consequences, which includes metabolic disorders. Overall, accumulating evidence indicates that there is a consistent association of multiple abnormalities in neuropsychological constructs, as well as correspondent brain abnormalities, with broad-based metabolic dysfunction, suggesting, therefore, that the existence of a "metabolic-mood syndrome" is possible. Nonetheless, empirical evidence is necessary to support and develop this concept. Future research should focus on dimensional constructs and employ integrative, multidisciplinary and multimodal approaches.

Genetic determinants of depression: recent findings and future directions

LEARNING OBJECTIVES

After participating in this activity, learners should be better able to: 1. Evaluate current evidence regarding the genetic determinants of depression 2. Assess findings from studies of gene-environment interaction 3. Identify challenges to gene discovery in depression Depression is one of the most prevalent, disabling, and costly mental health conditions in the United States and also worldwide. One promising avenue for preventing depression and informing its clinical treatment lies in uncovering the genetic and environmental determinants of the disorder as well as their interaction (G × E). The overarching goal of this review article is to translate recent findings from studies of genetic association and G × E related to depression, particularly for readers without in-depth knowledge of genetics or genetic methods. The review is organized into three major sections. In the first, we summarize what is currently known about the genetic determinants of depression, focusing on findings from genome-wide association studies (GWAS). In the second section, we review findings from studies of G × E, which seek to simultaneously examine the role of genes and exposure to specific environments or experiences in the etiology of depression. In the third section, we describe the challenges to genetic discovery in depression and promising strategies for future progress.

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

IMPORTANCE

Genetic association studies of psychiatric outcomes often consider interactions with environmental exposures and, in particular, apply tests that jointly consider gene and gene-environment interaction effects for analysis. Using a genome-wide association study (GWAS) of posttraumatic stress disorder (PTSD), we report that heteroscedasticity (defined as variability in outcome that differs by the value of the environmental exposure) can invalidate traditional joint tests of gene and gene-environment interaction.

OBJECTIVES

To identify the cause of bias in traditional joint tests of gene and gene-environment interaction in a PTSD GWAS and determine whether proposed robust joint tests are insensitive to this problem.

DESIGN, SETTING, AND PARTICIPANTS

The PTSD GWAS data set consisted of 3359 individuals (978 men and 2381 women) from the Grady Trauma Project (GTP), a cohort study from Atlanta, Georgia. The GTP performed genome-wide genotyping of participants and collected environmental exposures using the Childhood Trauma Questionnaire and Trauma Experiences Inventory.

MAIN OUTCOMES AND MEASURES

We performed joint interaction testing of the Beck Depression Inventory and modified PTSD Symptom Scale in the GTP GWAS. We assessed systematic bias in our interaction analyses using quantile-quantile plots and genome-wide inflation factors.

RESULTS

Application of the traditional joint interaction test to the GTP GWAS yielded systematic inflation across different outcomes and environmental exposures (inflation-factor estimates ranging from 1.07 to 1.21), whereas application of the robust joint test to the same data set yielded no such inflation (inflation-factor estimates ranging from 1.01 to 1.02). Simulated data further revealed that the robust joint test is valid in different heteroscedasticity models, whereas the traditional joint test is invalid. The robust joint test also has power similar to the traditional joint test when heteroscedasticity is not an issue.

CONCLUSIONS AND RELEVANCE

We believe the robust joint test should be used in candidate-gene studies and GWASs of psychiatric outcomes that consider environmental interactions. To make the procedure useful for applied investigators, we created a software tool that can be called from the popular PLINK package for analysis.