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Related Concept Videos

Gene-Environment Interactions01:20

Gene-Environment Interactions

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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Background and Environment Affect Phenotype02:27

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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Epistasis Analysis01:09

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Polygenic Traits01:18

Polygenic Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
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Generalized partial linear varying multi-index coefficient model for gene-environment interactions.

Xu Liu, Bin Gao, Yuehua Cui

    Statistical Applications in Genetics and Molecular Biology
    |December 19, 2016
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    Summary
    This summary is machine-generated.

    This study introduces a new statistical model to understand how combined environmental exposures and genetic factors influence disease risk. The findings offer novel insights into complex disease etiology by analyzing gene-environment interactions.

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    Area of Science:

    • Environmental Health
    • Genetic Epidemiology
    • Biostatistics

    Background:

    • Epidemiological studies suggest simultaneous environmental exposures impact disease risk.
    • The joint effect of environmental mixtures on genetic predisposition to disease remains unclear.
    • Understanding gene-environment (G×E) interactions is crucial for complex diseases.

    Purpose of the Study:

    • To develop a statistical model for assessing the joint effect of environmental mixtures on genetic risk.
    • To rigorously evaluate gene-environment interactions within complex environmental exposures.
    • To provide novel insights into the etiology of complex diseases.

    Main Methods:

    • Proposed a generalized partial linear varying multi-index coefficient model (GPLVMICM).
    • Employed profile procedures for parametric parameter estimation.
    • Utilized B-spline backfitted kernel methods for nonparametric index function estimation.
    • Developed generalized likelihood ratio (GLR) and parametric likelihood tests for interaction effects.

    Main Results:

    • The proposed GPLVMICM can capture genetic effects modulated by multiple environmental exposures.
    • Parametric and nonparametric estimators demonstrated consistency and asymptotic normality.
    • The GLR test effectively assesses the linearity of multi-environment gene-interaction effects.
    • Simulation studies and real data analysis validated the method's performance.

    Conclusions:

    • The developed statistical framework rigorously assesses gene-environment interactions in the context of environmental mixtures.
    • This approach enhances our understanding of how combined environmental and genetic factors contribute to complex diseases.
    • The method offers a valuable tool for future epidemiological and etiological research.