Genotype × Environment Interaction in Psychiatric Genetics: Deep Truth or Thin Ice?

Background:There continues to be significant investment in the detection of genotype × environment interaction (G × E) in psychiatric genetics. The implications of the method of assessment for the genetic analysis of psychiatric disorders are examined for simulated twin data on symptom scores and environmental covariates.Methods: Additive and independent genetic and environmental risks were simulated for 10,000 monozygotic (MZ) and 10,000 dizygotic (DZ) twin pairs and the ‘subjects’ administered typical simulated checklists of clinical symptoms and environmental factors. A variety of standard tests for G × E were applied to the simulated additive risk scores, sum scores derived from the checklists and transformed sum scores.Results:All analyses revealed no evidence for G × E for latent risk but marked evidence for G × E and other effects of modulation in the sum scores. These effects were all removed by transformation. An integrated genetic and psychometric model, accounting for both the causes of latent liability and a theory of measurement, was fitted to a sample of the simulated sum-score data and showed that there was no significant modulation of the parameters of the genetic model by environmental covariates (i.e., no G × E).Conclusions:Claims to detect G × E based on analytical methods that ignore the theory of measurement must be subjected to greater scrutiny prior to publication.

Download Full-text

Diallel Analysis for Inbred Lines Involving Genotype × Environment Interaction Effects on Additive-Dominance Genetic Model

Journal of Biological Sciences ◽

10.3923/jbs.2001.704.707 ◽

2001 ◽

Vol 1 (8) ◽

pp. 704-707 ◽

Cited By ~ 1

Author(s):

Abderrahmane Achouch ◽

Zhu Jun .

Keyword(s):

Genetic Model ◽

Diallel Analysis ◽

Interaction Effects ◽

Inbred Lines ◽

Environment Interaction ◽

Genotype Environment Interaction

Download Full-text

A genetic model of interpopulation variation and covariation of quantitative characters

Genetics Research ◽

10.1017/s0016672300028196 ◽

1989 ◽

Vol 53 (3) ◽

pp. 215-221 ◽

Cited By ~ 2

Author(s):

Zhao-Bang Zeng

Keyword(s):

Natural Selection ◽

Genetic Model ◽

Environment Interaction ◽

Effective Population ◽

Genotype Environment Interaction ◽

Quantitative Characters ◽

Special Cases ◽

Population Sizes ◽

Interpopulation Variation ◽

Evolutionary Consequences

SummaryEvolutionary consequences of natural selection, migration, genotype–environment interaction, and random genetic drift on interpopulation variation and covariation of quantitative characters are analysed in terms of a selection model that partitions natural selection into directional and stabilizing components. Without migration, interpopulation variation and covariation depend mainly on the pattern and intensities of selection among populations and the harmonic mean of effective population sizes. Both transient and equilibrium covariance structures are formulated with suitable approximations. Migration reduces the differentiation among populations, but its effect is less with genotype–environment interaction. In some special cases of genotype–environment interaction, the equilibrium interpopulation variation and covariation is independent of migration.

Download Full-text

Climate change has increased genotype-environment interactions in wheat breeding

10.21203/rs.3.rs-69475/v1 ◽

2020 ◽

Author(s):

Wei Xiong ◽

Matthew Reynolds ◽

Jose Crossa ◽

Thomas Payne ◽

Urs Schulthess ◽

...

Keyword(s):

Climate Change ◽

Genetic Model ◽

Climatic Factors ◽

Wheat Breeding ◽

Environment Interaction ◽

Genotype Environment Interaction ◽

Quantitative Genetic ◽

Quantitative Genetic Model ◽

Yield Responses ◽

Wheat Lines

Abstract The International Maize and Wheat Improvement Center (CIMMYT) develops and distributes annually elite wheat lines as international trials worldwide to assess their performance in different environments and utilization by partners for use in breeding or release as varieties. However, as elsewhere, the collaborator test sites where trials are evaluated have experienced climate change, with implications for how adapted wheat genotypes are bred. Using a standard quantitative genetic model and archived datasets for four global spring wheat trials, we show that the genotype-environment-interaction (GEI) has increased by up to 500% over recent decades. Notably crossover has increased over time, a critical indicator of changes in the ranking of cultivar performance in different environments. Climatic factors explain over 70% of the year-to-year variability in GEI and crossover interactions for yield. Examining yield responses of all genotypes in all trial environments from 1985 to 2017 reveals that climate change has increased GEI by ~ 49% and ranking change by ~38%. Genetic improvement of wheat targeted to high-yielding environments has exacerbated this increase, but the performance of new wheat germplasm developed to withstand heat and drought stress is more adapted and stable, offsetting the increase in ranking changes due to the warmer climate.

Download Full-text

Determining environmental covariates which explain genotype environment interaction in winter wheat through probe genotypes and biadditive factorial regression

Theoretical and Applied Genetics ◽

10.1007/s001220050038 ◽

2000 ◽

Vol 100 (2) ◽

pp. 285-298 ◽

Cited By ~ 17

Author(s):

M. Brancourt-Hulmel ◽

J. -B. Denis ◽

C. Lecomte

Keyword(s):

Winter Wheat ◽

Environment Interaction ◽

Environmental Covariates ◽

Factorial Regression ◽

Genotype Environment Interaction ◽

Probe Genotypes

Download Full-text

Mathematical Ability and Socio-Economic Background: IRT Modeling to Estimate Genotype by Environment Interaction

Twin Research and Human Genetics ◽

10.1017/thg.2017.59 ◽

2017 ◽

Vol 20 (6) ◽

pp. 511-520 ◽

Cited By ~ 3

Author(s):

Inga Schwabe ◽

Dorret I. Boomsma ◽

Stéphanie M. van den Berg

Keyword(s):

Individual Differences ◽

Genetic Model ◽

Environmental Influences ◽

Genotype By Environment Interaction ◽

Mathematical Ability ◽

Environment Interaction ◽

Environmental Variance ◽

Behavioral Traits ◽

Genotype By Environment ◽

Sum Scores

Genotype by environment interaction in behavioral traits may be assessed by estimating the proportion of variance that is explained by genetic and environmental influences conditional on a measured moderating variable, such as a known environmental exposure. Behavioral traits of interest are often measured by questionnaires and analyzed as sum scores on the items. However, statistical results on genotype by environment interaction based on sum scores can be biased due to the properties of a scale. This article presents a method that makes it possible to analyze the actually observed (phenotypic) item data rather than a sum score by simultaneously estimating the genetic model and an item response theory (IRT) model. In the proposed model, the estimation of genotype by environment interaction is based on an alternative parametrization that is uniquely identified and therefore to be preferred over standard parametrizations. A simulation study shows good performance of our method compared to analyzing sum scores in terms of bias. Next, we analyzed data of 2,110 12-year-old Dutch twin pairs on mathematical ability. Genetic models were evaluated and genetic and environmental variance components estimated as a function of a family's socio-economic status (SES). Results suggested that common environmental influences are less important in creating individual differences in mathematical ability in families with a high SES than in creating individual differences in mathematical ability in twin pairs with a low or average SES.

Download Full-text