110 – No confirmation of gene–environment interaction between COMT Val158Met and adolescent-onset cannabis use in a large sample of patients with schizophrenia

IntroductionEarlier studies have shown that candidate gene risk polymorphisms and psychoactive substance abuse influence the frequency and severity of psychosis.ObjectivesIn this study we examined whether the most studied schizophrenia risk polymorphisms and psychoactive substance abuse interact in their influence on symptom severity and neurocognition.MethodsWe analyzed the clinical data of 280 schizophrenia patients, including genotyping data of the candidate genes NRG1, DTNBP1, RGS4, G72/G30 and PIP5K2A. Patients were assessed clinically by the Positive and Negative Symptom Scale (PANSS) and information about substance abuse was based on self-report and reviewing patient charts. We tested for possible interactional effects using the General Linear Model (GLM) analysis.Results15,8% of patients reported episodic or regular substance abuse, the vast majority (92%) used cannabis or the combination of cannabis and another drug. Substance abuse was associated with higher scores of the PANSS hostility/excitement factor, independent of sex, age, or genetic results (F = 4,02;p = 0,04). We found significant interactional effects of the DTNBP1 gene risk polymorphisms and substance abuse on different PANSS factors: rs2619528 and positive substance abuse interaction were associated with higher scores on the PANSS negative factor (F = 4,6;p = 0,03), and the PANSS depression factor (F = 4,75;p = 0,03). Moreover the rs3213207 - substance abuse interaction was associated with higher scores on the PANSS cognitive factor (F = 7,55;p = 0,006). Carriers of the Val allele of the COMT Val158Met polymorphism demonstrated significantly higher scores on the PANSS depression factor (F = 5,53;p = 0,02).ConclusionsOur results underscore the importance of gene-environment interactions in the phenotypic heterogeneity of schizophrenia.

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SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data

Frontiers in Genetics ◽

10.3389/fgene.2021.710055 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jocelyn T. Chi ◽

Ilse C. F. Ipsen ◽

Tzu-Hung Hsiao ◽

Ching-Heng Lin ◽

Li-San Wang ◽

...

Keyword(s):

Large Scale ◽

Exact Algorithm ◽

Large Sample Size ◽

Environment Interaction ◽

Sample Sizes ◽

Computationally Efficient ◽

Test Statistic ◽

Large Sample ◽

Gene Environment Interaction ◽

Gene Environment

The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index.

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