Age at onset of psychotic disorder: Cannabis, BDNF Val66Met, and sex-specific models of gene-environment interaction

2011 ◽  
Vol 156 (3) ◽  
pp. 363-369 ◽  
Author(s):  
Jeroen Decoster ◽  
Jim van Os ◽  
Gunter Kenis ◽  
Cecile Henquet ◽  
Joseph Peuskens ◽  
...  
Keyword(s):  
Psychotic Disorder ◽  
Age At Onset ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment

scholarly journals Replicated evidence that endophenotypic expression of schizophrenia polygenic risk is greater in healthy siblings of patients compared to controls, suggesting gene–environment interaction. The EUGEI study

2019 ◽  
Vol 50 (11) ◽  
pp. 1884-1897 ◽  
Author(s):  
Jim van Os ◽  
Lotta-Katrin Pries ◽  
Philippe Delespaul ◽  
Gunter Kenis ◽  
Jurjen J. Luykx ◽  
...  
Keyword(s):  
Cognitive Ability ◽  
Psychotic Disorder ◽  
Genetic Risk ◽  
Environment Interaction ◽  
Replication Sample ◽  
Polygenic Risk ◽  
Gene Environment Interaction ◽  
Intermediate Phenotypes ◽  
Gene Environment ◽  
Healthy Siblings

AbstractBackgroundFirst-degree relatives of patients with psychotic disorder have higher levels of polygenic risk (PRS) for schizophrenia and higher levels of intermediate phenotypes.MethodsWe conducted, using two different samples for discovery (n = 336 controls and 649 siblings of patients with psychotic disorder) and replication (n = 1208 controls and 1106 siblings), an analysis of association between PRS on the one hand and psychopathological and cognitive intermediate phenotypes of schizophrenia on the other in a sample at average genetic risk (healthy controls) and a sample at higher than average risk (healthy siblings of patients). Two subthreshold psychosis phenotypes, as well as a standardised measure of cognitive ability, based on a short version of the WAIS-III short form, were used. In addition, a measure of jumping to conclusion bias (replication sample only) was tested for association with PRS.ResultsIn both discovery and replication sample, evidence for an association between PRS and subthreshold psychosis phenotypes was observed in the relatives of patients, whereas in the controls no association was observed. Jumping to conclusion bias was similarly only associated with PRS in the sibling group. Cognitive ability was weakly negatively and non-significantly associated with PRS in both the sibling and the control group.ConclusionsThe degree of endophenotypic expression of schizophrenia polygenic risk depends on having a sibling with psychotic disorder, suggestive of underlying gene–environment interaction. Cognitive biases may better index genetic risk of disorder than traditional measures of neurocognition, which instead may reflect the population distribution of cognitive ability impacting the prognosis of psychotic disorder.

Gene-Environment Interaction in the Determination of Levels of Haemostatic Variables Involved in Thrombosis and Fibrinolysis

1997 ◽  
Vol 78 (01) ◽  
pp. 457-461 ◽  
Author(s):  
S E Humphries ◽  
A Panahloo ◽  
H E Montgomery ◽  
F Green ◽  
J Yudkin
Keyword(s):  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment

Gene-environment Interaction on the Risk of Type 2 Diabetes Among Ethnic Minority Populations Living in Europe and North America: A Systematic Review

2020 ◽  
Vol 16 (5) ◽  
pp. 457-470 ◽  
Author(s):  
Mohammad H. Zafarmand ◽  
Parvin Tajik ◽  
René Spijker ◽  
Charles Agyemang
Keyword(s):  
Type 2 Diabetes ◽  
North America ◽  
Ethnic Minority ◽  
Ethnic Minorities ◽  
Minority Groups ◽  
Environment Interaction ◽  
Ethnic Minority Groups ◽  
Gene Environment Interaction ◽  
Gene Environment

Background: The body of evidence on gene-environment interaction (GEI) related to type 2 diabetes (T2D) has grown in the recent years. However, most studies on GEI have sought to explain variation within individuals of European ancestry and results among ethnic minority groups are inconclusive. Objective: To investigate any interaction between a gene and an environmental factor in relation to T2D among ethnic minority groups living in Europe and North America. Methods: We systematically searched Medline and EMBASE databases for the published literature in English up to 25th March 2019. The screening, data extraction and quality assessment were performed by reviewers independently. Results: 1068 studies identified through our search, of which nine cohorts of six studies evaluating several different GEIs were included. The mean follow-up time in the included studies ranged from 5 to 25.7 years. Most studies were relatively small scale and few provided replication data. All studies included in the review included ethnic minorities from North America (Native-Americans, African- Americans, and Aboriginal Canadian), none of the studies in Europe assessed GEI in relation to T2D incident in ethnic minorities. The only significant GEI among ethnic minorities was HNF1A rs137853240 and smoking on T2D incident among Native-Canadians (Pinteraction = 0.006). Conclusion: There is a need for more studies on GEI among ethnicities, broadening the spectrum of ethnic minority groups being investigated, performing more discovery using genome-wide approaches, larger sample sizes for these studies by collaborating efforts such as the InterConnect approach, and developing a more standardized method of reporting GEI studies are discussed.

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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