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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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Longitudinal Studies01:26

Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Comparing the Survival Analysis of Two or More Groups01:20

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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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.
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Sparse group variable selection for gene-environment interactions in the longitudinal study.

Fei Zhou1, Xi Lu1, Jie Ren2

  • 1Department of Statistics, Kansas State University, Manhattan, Kansas, 66506, USA.

Genetic Epidemiology
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new penalized method for analyzing complex genetic and environmental interactions in longitudinal health studies. The approach improves the identification and prediction of disease risk factors from high-dimensional data.

Keywords:
gene-environment interactionlongitudinal datapenalizationquadratic inference functionsparse group selection

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Area of Science:

  • Biostatistics
  • Genomics
  • Epidemiology

Background:

  • Longitudinal data analysis requires methods that handle correlated repeated measurements.
  • High-dimensional data in genetics necessitates efficient variable selection techniques.
  • Structured sparsity in gene-environment interaction studies remains a challenge for existing penalization methods.

Purpose of the Study:

  • To develop a novel sparse group penalization method for bi-level gene-environment (GxE) interaction studies.
  • To simultaneously identify main and interaction effects at both group and individual levels in longitudinal data.
  • To improve prediction and identification performance in high-dimensional GxE analyses.

Main Methods:

  • Developed a sparse group penalization approach within the quadratic inference function framework.
  • Applied the method to high-dimensional single nucleotide polymorphism (SNP) data and longitudinal phenotype data.
  • Utilized simulation studies to compare the proposed method against existing approaches.

Main Results:

  • The proposed sparse group penalization method demonstrated superior performance compared to major competitors in simulation studies.
  • The method effectively identified significant main and interaction effects in both simulated and real-world data.
  • Achieved improved prediction accuracy for the longitudinal phenotype.

Conclusions:

  • The developed sparse group penalization method is effective for bi-level GxE interaction studies with high-dimensional longitudinal data.
  • This approach offers enhanced capabilities for identifying complex genetic and environmental risk factors.
  • The findings have significant implications for understanding disease etiology, such as asthma, and improving predictive modeling.