Gene-environment Interaction Studies Research Articles

401. Gene-Environment Interaction Study on the Correlates for Psychotic Experiences in the UK Biobank

401. Gene-Environment Interaction Study on the Correlates for Psychotic Experiences in the UK Biobank

Lysosomal genes contribute to Parkinson’s disease near agriculture with high intensity pesticide use

Parkinson’s disease (PD), the second most common neurodegenerative disorder, develops sporadically, likely through a combination of polygenic and environmental factors. Previous studies associate pesticide exposure and genes involved in lysosomal function with PD risk. We evaluated the frequency of variants in lysosomal function genes among patients from the Parkinson’s, Environment, and Genes (PEG) study with ambient pesticide exposure from agricultural sources. 757 PD patients, primarily of White European/non-Hispanic ancestry (75%), were screened for variants in 85 genes using a custom amplicon panel. Variant enrichment was calculated against the Genome Aggregation Database (gnomAD). Enriched exonic variants were prioritized by exposure to a cluster of pesticides used on cotton and severity of disease progression in a subset of 386 patients subdivided by race/ethnicity. Gene enrichment analysis identified 36 variants in 26 genes in PEG PD patients. Twelve of the identified genes (12/26, 46%) had multiple enriched variants and/or a single enriched variant present in multiple individuals, representing 61% (22/36) of the observed variation in the cohort. The majority of enriched variants (26/36, 72%) were found in genes contributing to lysosomal function, particularly autophagy, and were bioinformatically deemed functionally deleterious (31/36, 86%). We conclude that, in this study, variants in genes associated with lysosomal function, notably autophagy, were enriched in PD patients exposed to agricultural pesticides suggesting that altered lysosomal function may generate an underlying susceptibility for developing PD with pesticide exposure. Further study of gene-environment interactions targeting lysosomal function may improve understanding of PD risk in individuals exposed to pesticides.

Abstract 6140: Gene-environment interactions for lifetime water arsenic exposure and bladder cancer risk in the New England Bladder Cancer study

Abstract Environmental arsenic and genetic susceptibility have both been shown to increase bladder cancer risk, yet little is known about their respective interaction. We conducted a gene-environment interaction (GxE) study to characterize lifetime arsenic exposure from drinking water, inherited susceptibility, and bladder cancer risk in the New England Bladder Cancer Study. Exposures were estimated from arsenic measured in water samples collected at home and workplace. We evaluated common single-nucleotide polymorphisms (SNPs) i) from the latest bladder cancer genome-wide association study (n= 28), ii) from candidate gene studies of arsenic or bladder cancer (n= 16 SNPs), and iii) SNPs from a large genome-wide association study of arsenic metabolism (n = 4 SNPs) as potential effect modifiers of the arsenic-bladder cancer relationship. We coded SNPs as the absence or presence of a risk/effect allele for bladder cancer or arsenic metabolism in 2,019 adults (928 bladder cancer cases and 1,091 controls) and arsenic exposures as tertiles. Odds ratios (OR) with 95% confidence intervals (CI) adjusted for age, sex, smoking status, high-risk occupation, and principal components of genetic ancestry were estimated using logistic regression models. P-values for multiplicative interaction were calculated. Out of 48 SNPs, we found evidence of interaction for 7 SNPs at the statistical significance level of P<0.05. Most SNPs involved in GxE interactions are mapped to genomic regions known or suspected to function in arsenic metabolism. For the association between cumulative lifetime water arsenic (mg) and bladder cancer, the strongest evidence of an interaction was with rs1046428 (near GSTZ1 on 14q23); among those with one or two copies of the T risk allele, ORT2vsT1: 1.52, CI: 1.11 - 2.07; ORT3 vsT1: 1.44, CI: 1.05 - 1.98, Pinteraction = 0.01; among those with no risk allele copy, ORT2vsT1: 0.91, CI: 0.62 - 1.35; ORT3 vsT1: 0.97, CI: 0.65 - 1.44. For average daily water arsenic (µg/day), we found evidence of interaction among those with risk allele for rs1801133 (A/A, A/G; MTHFR on 1p36) ORT2 vsT1: 1.70, CI: 1.18 - 2.44; ORT3 vsT1: 1.53, CI: 1.06 - 2.23, Pinteraction = 0.02, in those with no copies of risk allele ORT2vsT1: 0.85, CI: 0.61 - 1.17; ORT3 vsT1: 1.11, CI: 0.79 - 1.54; and rs1801131 (G/G; G/T; MTHFR ) ORT2 vsT1: 1.59, CI: 1.14 - 2.22; ORT3 vsT1: 1.63, CI: 1.16 - 2.29, Pinteraction = 0.01, whereas in those with no risk allele ORT2vsT1: 0.84, CI: 0.59 - 1.19; ORT3 vsT1: 1, CI: 0.69 - 1.44. For 40-year lagged cumulative lifetime water arsenic (mg) and average daily water arsenic (µg/day), we found suggestive interactions with rs2234636 (SLC39A2 on 14q11), and rs1495741 (NAT2 on 8p22). Our study provides evidence of interactions between lifetime arsenic exposure and genetic variants that function in xenobiotic metabolism on bladder cancer occurrence. Citation Format: Hugo Pomares-Millan, Stella Koutros, Nathaniel Rothman, Dalsu Baris, Molly Schwenn, Alison Johnson, Debra T. Silverman, Margaret R. Karagas, Michael N. Passarelli. Gene-environment interactions for lifetime water arsenic exposure and bladder cancer risk in the New England Bladder Cancer study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6140.

Associations between disturbed sleep and attenuated psychotic experiences in people at clinical high risk for psychosis.

Pre-diagnostic stages of psychotic illnesses, including 'clinical high risk' (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis. Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population. Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa. In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings.

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

The Promise and Potential of Brain Organoids.

Brain organoids are 3D in vitro culture systems derived from human pluripotent stem cells that self-organize to model features of the (developing) human brain. This review examines the techniques behind organoid generation, their current and potential applications, and future directions for the field. Brain organoids possess complex architecture containing various neural cell types, synapses, and myelination. They have been utilized for toxicology testing, disease modeling, infection studies, personalized medicine, and gene-environment interaction studies. An emerging concept termed Organoid Intelligence (OI) combines organoids with artificial intelligence systems to generate learning and memory, with the goals of modeling cognition and enabling biological computing applications. Brain organoids allow neuroscience studies not previously achievable with traditional techniques, and have the potential to transform disease modeling, drug development, and the understanding of human brain development and disorders. The aspirational vision of OI parallels the origins of artificial intelligence, and efforts are underway to map a roadmap toward its realization. In summary, brain organoids constitute a disruptive technology that is rapidly advancing and gaining traction across multiple disciplines.

A Kpna1-deficient psychotropic drug-induced schizophrenia model mouse for studying gene–environment interactions

KPNA1 is a mediator of nucleocytoplasmic transport that is abundantly expressed in the mammalian brain and regulates neuronal differentiation and synaptic function. De novo mutations in Kpna1 have been identified using genome-wide association studies in humans with schizophrenia; however, it remains unclear how KPNA1 contributes to schizophrenia pathogenesis. Recent studies have suggested a complex combination of genetic and environmental factors that are closely related to psychiatric disorders. Here, we found that subchronic administration of phencyclidine, a psychotropic drug, induced vulnerability and behavioral abnormalities consistent with the symptoms of schizophrenia in Kpna1-deficient mice. Microarray assessment revealed that the expression levels of dopamine d1/d2 receptors, an RNA editing enzyme, and a cytoplasmic dynein component were significantly altered in the nucleus accumbens brain region in a gene-environment (G × E) interaction-dependent manner. Our findings demonstrate that Kpna1-deficient mice may be useful as a G × E interaction mouse model for psychiatric disorders and for further investigation into the pathogenesis of such diseases and disorders.

Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies.

statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.

Genetics and Epigenetics of Nonsuicidal Self-Injury: a Narrative Review

Nonsuicidal self-injury (NSSI), or self-harm is widely spread, especially among the young people. However, despite the problem’s relevance, NSSI was investigated mainly from the psychological and social perspective, while a fairly small number of biological studies of NSSI have been published to date. The aim of this narrative review was to analyze all the currently available publications on the genetics and epigenetics of NSSI – one of the most promising areas in biological research. We discussed and analyzed all stages of genetic research of NSSI: from twin studies and studies of self-harm in the framework of hereditary diseases to candidate genes, genome-wide, epigenetic and gene-environment interactions studies. We demonstrated data indicating significant contribution of genetic and epigenetic factors to the development of NSSI, discussed advantages and limitations of analyzed studies, outlined prospects for further research in this area.

Association of polygenic scores for depression and neuroticism with perceived stress in daily life during a long-lasting stress period.

Genetic factors contribute significantly to interindividual differences in the susceptibility to stress-related disorders. As stress can also be conceptualized as environmental exposure, controlled gene-environment interaction (GxE) studies with an in-depth phenotyping may help to unravel mechanisms underlying the interplay between genetic factors and stress. In a prospective-longitudinal quasi-experimental study, we investigated whether polygenic scores (PGS) for depression (DEP-PGS) and neuroticism (NEU-PGS), respectively, were associated with responses to chronic stress in daily life. We examined law students (n = 432) over 13 months. Participants in the stress group experienced a long-lasting stress phase, namely the preparation for the first state examination for law students. The control group consisted of law students without particular stress exposure. In the present manuscript, we analyzed perceived stress levels assessed at high frequency and in an ecologically valid manner by ambulatory assessments as well as depression symptoms and two parameters of the cortisol awakening response. The latter was only assessed in a subsample (n = 196). No associations between the DEP-PGS and stress-related variables were found. However, for the NEU-PGS we found a significant GxE effect. Only in individuals experiencing academic stress a higher PGS for neuroticism predicted stronger increases of perceived stress levels until the exam. At baseline, a higher NEU-PGS was associated with higher perceived stress levels in both groups. Despite the small sample size, we provide preliminary evidence that the genetic disposition for neuroticism is associated with stress level increases in daily life during a long-lasting stress period.

Precision pharmacological reversal of strain-specific diet-induced metabolic syndrome in mice informed by epigenetic and transcriptional regulation.

Diet-related metabolic syndrome is the largest contributor to adverse health in the United States. However, the study of gene-environment interactions and their epigenomic and transcriptomic integration is complicated by the lack of environmental and genetic control in humans that is possible in mouse models. Here we exposed three mouse strains, C57BL/6J (BL6), A/J, and NOD/ShiLtJ (NOD), to a high-fat, high-carbohydrate diet, leading to varying degrees of metabolic syndrome. We then performed transcriptomic and genome-wide DNA methylation analyses for each strain and found overlapping but also highly divergent changes in gene expression and methylation upstream of the discordant metabolic phenotypes. Strain-specific pathway analysis of dietary effects revealed a dysregulation of cholesterol biosynthesis common to all three strains but distinct regulatory networks driving this dysregulation. This suggests a strategy for strain-specific targeted pharmacologic intervention of these upstream regulators informed by epigenetic and transcriptional regulation. As a pilot study, we administered the drug GW4064 to target one of these genotype-dependent networks, the farnesoid X receptor pathway, and found that GW4064 exerts strain-specific protection against dietary effects in BL6, as predicted by our transcriptomic analysis. Furthermore, GW4064 treatment induced inflammatory-related gene expression changes in NOD, indicating a strain-specific effect in its associated toxicities as well as its therapeutic efficacy. This pilot study demonstrates the potential efficacy of precision therapeutics for genotype-informed dietary metabolic intervention and a mouse platform for guiding this approach.

A Case-Only Genome-Wide Interaction Study of Smoking and Bladder Cancer Risk: Results from the COBLAnCE Cohort.

Bladder cancer (BC) is the 6th most common cancer worldwide, with tobacco smoking considered as its main risk factor. Accumulating evidence has found associations between genetic variants and the risk of BC. Candidate gene-environment interaction studies have suggested interactions between cigarette smoking and NAT2/GSTM1 gene variants. Our objective was to perform a genome-wide association case-only study using the French national prospective COBLAnCE cohort (COhort to study BLAdder CancEr), focusing on smoking behavior. The COBLAnCE cohort comprises 1800 BC patients enrolled between 2012 and 2018. Peripheral blood samples collected at enrolment were genotyped using the Illumina Global Screening Array with a Multi-Disease drop-in panel. Genotyping data (9,719,614 single nucleotide polymorphisms (SNP)) of 1674, 1283, and 1342 patients were analyzed for smoking status, average tobacco consumption, and age at smoking initiation, respectively. A genome-wide association study (GWAS) was conducted adjusting for gender, age, and genetic principal components. The results suggest new candidate loci (4q22.1, 12p13.1, 16p13.3) interacting with smoking behavior for the risk of BC. Our results need to be validated in other case-control or cohort studies.

Socioeconomic status and risk for child psychopathology: exploring gene-environment interaction in the presence of gene-environment correlation using extended families in the Norwegian Mother, Father and Child Birth Cohort Study.

Low socioeconomic status (SES) is associated with increased risk for emotional and behavioural problems among children. Evidence from twin studies has shown that family SES moderates genetic and environmental influences on child mental health. However, it is also known that SES is itself under genetic influence and previous gene-environment interaction (G×E) studies have not incorporated the potential genetic overlap between child mental health and family SES into G×E analyses. We applied a novel approach using extended family data to investigate the moderation of aetiological influences on child emotional and behavioural problems by parental socioeconomic status in the presence of modelled gene-environment correlation. The sample comprised >28,100 children in extended-family units drawn from the Norwegian Mother, Father and Child Cohort Study (MoBa). Mothers reported children's emotional and behavioural symptoms. Parents' income and educational attainment were obtained through linkage to administrative register data. Bivariate moderation Multiple-Children-of-Twins-and-Siblings (MCoTS) models were used to analyse relationships between offspring outcomes (emotional and behavioural symptom scores) and parental socioeconomic moderators (income rank and educational attainment). The aetiology of child emotional symptoms was moderated by maternal and paternal educational attainment. Shared environmental influences on child emotional symptoms were greater at lower levels of parents' education. The aetiology of child behavioural symptoms was moderated by maternal, but not paternal, socioeconomic factors. Genetic factors shared between maternal income and child behavioural symptoms were greater in families with lower levels maternal income. Nonshared environmental influences on child behavioural symptoms were greater in families with higher maternal income and education. Parental socioeconomic indicators moderated familial influences and nonshared environmental influences on child emotional and behavioural outcomes. Maternal SES and child mental health share aetiological overlap such that shared genetic influence was greater at the lower end of the socioeconomic distribution. Our findings collectively highlight the role that family socioeconomic factors play in shaping the origins of child emotional and behavioural problems.

The Relationship Between Antisocial Personality Disorder and Criminology: Acquired Factors and Genetic Influences

This study looks at the interaction of internal and external factors in the process of antisocial personality disorder (ASPD) and criminal conduct. It reveals the major contributions of both genetic and acquired factors using data from longitudinal twin studies, neuroimaging research, and gene-environment interaction studies. Environmental variables, such as bad childhood experiences, community hardship, and exposure to violence, also play important roles in antisocial behavior variance. The complicated interaction between genetic predispositions and environmental conditions emphasizes the need of understanding these components' dynamic character throughout an individual's development. With further knowledge, it will be feasible to build complete, individualized therapies that target both genetic and environmental aspects in the emergence and maintenance of antisocial behavior. Furthermore, this review emphasizes the importance of early intervention and prevention strategies that take an individual's unique genetic and environmental risk factors into account, ultimately contributing to a more effective approach in reducing the prevalence of ASPD and criminal behavior in society.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry

BackgroundGenome-wide studies of gene–environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer.MethodsAnalyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene–environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs.ResultsAssuming a 1 × 10–5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92–0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88–0.94).ConclusionsOverall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

The Bayesian regularized quantile varying coefficient model

The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of regression coefficients. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable, and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. Although extensive studies have been conducted to examine variable selection for the high-dimensional quantile varying coefficient models, the Bayesian analysis has been rarely developed. The Bayesian regularized quantile varying coefficient model has been proposed to incorporate robustness against data heterogeneity while accommodating the non-linear interactions between the effect modifier and predictors. Selecting important varying coefficients can be achieved through Bayesian variable selection. Incorporating the multivariate spike-and-slab priors further improves performance by inducing exact sparsity. The Gibbs sampler has been derived to conduct efficient posterior inference of the sparse Bayesian quantile VC model through Markov chain Monte Carlo (MCMC). The merit of the proposed model in selection and estimation accuracy over the alternatives has been systematically investigated in simulation under specific quantile levels and multiple heavy-tailed model errors. In the case study, the proposed model leads to identification of biologically sensible markers in a non-linear gene-environment interaction study using the NHS data.

Cannabis use as a potential mediator between childhood adversity and first-episode psychosis: results from the EU-GEI case–control study

AbstractBackgroundChildhood adversity and cannabis use are considered independent risk factors for psychosis, but whether different patterns of cannabis use may be acting as mediator between adversity and psychotic disorders has not yet been explored. The aim of this study is to examine whether cannabis use mediates the relationship between childhood adversity and psychosis.MethodsData were utilised on 881 first-episode psychosis patients and 1231 controls from the European network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study. Detailed history of cannabis use was collected with the Cannabis Experience Questionnaire. The Childhood Experience of Care and Abuse Questionnaire was used to assess exposure to household discord, sexual, physical or emotional abuse and bullying in two periods: early (0–11 years), and late (12–17 years). A path decomposition method was used to analyse whether the association between childhood adversity and psychosis was mediated by (1) lifetime cannabis use, (2) cannabis potency and (3) frequency of use.ResultsThe association between household discord and psychosis was partially mediated by lifetime use of cannabis (indirect effect coef. 0.078, s.e. 0.022, 17%), its potency (indirect effect coef. 0.059, s.e. 0.018, 14%) and by frequency (indirect effect coef. 0.117, s.e. 0.038, 29%). Similar findings were obtained when analyses were restricted to early exposure to household discord.ConclusionsHarmful patterns of cannabis use mediated the association between specific childhood adversities, like household discord, with later psychosis. Children exposed to particularly challenging environments in their household could benefit from psychosocial interventions aimed at preventing cannabis misuse.

Tobacco use in first-episode psychosis, a multinational EU-GEI study.

Tobacco is a highly prevalent substance of abuse in patients with psychosis. Previous studies have reported an association between tobacco use and schizophrenia. The aim of this study was to analyze the relationship between tobacco use and first-episode psychosis (FEP), age at onset of psychosis, and specific diagnosis of psychosis. The sample consisted of 1105 FEP patients and 1355 controls from the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) study. We assessed substance use with the Tobacco and Alcohol Questionnaire and performed a series of regression analyses using case-control status, age of onset of psychosis, and diagnosis as outcomes and tobacco use and frequency of tobacco use as predictors. Analyses were adjusted for sociodemographic characteristics, alcohol, and cannabis use. After controlling for cannabis use, FEP patients were 2.6 times more likely to use tobacco [p ⩽ 0.001; adjusted odds ratio (AOR) 2.6; 95% confidence interval (CI) [2.1-3.2]] and 1.7 times more likely to smoke 20 or more cigarettes a day (p = 0.003; AOR 1.7; 95% CI [1.2-2.4]) than controls. Tobacco use was associated with an earlier age at psychosis onset (β = -2.3; p ⩽ 0.001; 95% CI [-3.7 to -0.9]) and was 1.3 times more frequent in FEP patients with a diagnosis of schizophrenia than in other diagnoses of psychosis (AOR 1.3; 95% CI [1.0-1.8]); however, these results were no longer significant after controlling for cannabis use. Tobacco and heavy-tobacco use are associated with increased odds of FEP. These findings further support the relevance of tobacco prevention in young populations.

Association between birth by caesarian section and anxiety, self-harm: a gene-environment interaction study using UK Biobank data

BackgroundLimited efforts have been paid to explore the underlying genetic mechanisms of birth by caesarian section (CS) affecting the risks of adult anxiety and self-harm.MethodsUsing UK Biobank cohort, the logistic regression model was first applied to evaluate the associations of adult anxiety and self-harm with birth by CS. Using birth by CS as exposure variables, genome-wide by environment interaction study (GWEIS) was then applied by PLINK2.0 to identify associated genes interacting with birth by CS for anxiety and self-harm.ResultsIn the observational study, significant associations were observed between birth by CS and anxiety (odds ratio (OR) = 1.24; 95% confidence interval (CI), 1.12–1.38; P = 4.86 × 10− 5), and self-harm (OR = 1.12; 95% CI, 1.01–1.24; P = 2.90 × 10− 2). GWEIS revealed multiple suggestive genes interacted with birth by CS for anxiety, such as DKK2 (rs13137764, P = 1.24 × 10− 9, adjusted P = 2.68 × 10− 7) and ATXN1 (rs62389045, P = 4.38 × 10− 8, adjusted P = 3.55 × 10− 6). For self-harm, significant gene-environment interactions of birth by CS on self-harm were detected, such as ALDH1A2 (rs77828167, P = 1.62 × 10− 8; rs116899929, P = 1.92 × 10− 8) and DAB1 (rs116124269, P = 3.20 × 10− 8; rs191070006, P = 3.63 × 10− 8).ConclusionsOur results suggested that birth by CS was associated with the risk of adult anxiety and self-harm. We also discovered some genes interacted with birth by CS might influence the risk of anxiety and self-harm, which may provide novel clues for the pathogenesis of those mental disorders.

Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene-environment interaction studies. This study introduces "springer," an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package "springer" has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the "springer" package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package "springer" is available at https://cran.r-project.org/package=springer.