P.2.a.028 Gene × environment interaction in depressive disorders: which environment is of risk?

2012 ◽  
Vol 22 ◽  
pp. S240
Author(s):  
L. Mandelli ◽  
C. Petrelli ◽  
A. Serretti
Keyword(s):  
Depressive Disorders ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment

scholarly journals War exposure, 5-HTTLPR genotype and lifetime risk of depression

2011 ◽  
Vol 199 (1) ◽  
pp. 43-48 ◽  
Author(s):  
Sylvaine Artero ◽  
Jacques Touchon ◽  
Anne-Marie Dupuy ◽  
Alain Malafosse ◽  
Karen Ritchie
Keyword(s):  
Depressive Disorders ◽  
Stressful Life Event ◽  
Community Sample ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Gender Education ◽  
Life Threatening ◽  
Long Term Impact

BackgroundIn 1962 approximately 1.5 million French people living in Algeria were repatriated to France in very poor and often life-threatening conditions. These people constitute a cohort for the study of the long-term impact of gene–environment interaction on depression.AimsTo examine the interaction between a highly stressful life event and subsequent depression, and its modulation by a length polymorphism of the serotonin transporter gene (5–HTTLPR).MethodA community sample of people aged 65 years and over residing in the Montpellier region of the south of France was randomly recruited from electoral rolls. Genotyping was performed on 248 repatriated persons and 632 controls. Current and lifetime major and minor depressive disorders were assessed according to DSM-IV criteria.ResultsA significant relationship was observed between exposure to repatriation and subsequent depression (P<0.002), but there was no significant effect of gene alone (P = 0.62). After controlling for age, gender, education, disability, recent life events and cognitive function, the gene–environment interaction (repatriation×5-HTTLPR) was globally significant (P<0.002; OR = 3.21, 95% CI 2.48–5.12). Individuals carrying the two short (s) alleles of 5-HTTLPR were observed to be at higher risk (P<0.005; OR = 2.34, 95% CI 1.24–4.32), particularly when repatriation occurred before age 35 years (P<0.002; OR = 2.91, 95% CI 1.44–5.88), but this did not reach significance in those who were older at the time of the event (P = 0.067).ConclusionsThe association between depression and war repatriation was significantly modulated by 5-HTTLPR genotype but this appeared to occur only in people who were younger at the time of exposure.

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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