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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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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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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Statistical methods for gene-environment interaction analysis.

Jiacheng Miao1, Yixuan Wu2, Qiongshi Lu1,3,4

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Wiley Interdisciplinary Reviews. Computational Statistics
|May 3, 2024
PubMed
Summary
This summary is machine-generated.

This review explores statistical methods for analyzing gene-environment interactions (G × E) in complex human traits. Understanding these interactions is key to advancing precision medicine and uncovering genetic architecture.

Keywords:
Applications of Computational Statistics > Genomics/Proteomics/GeneticsData: Types and Structure > Massive DataStatistical Models > Linear Modelsgene–environment interaction (G × E)precision medicinestatistical genetics

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Area of Science:

  • Genetics
  • Biostatistics
  • Genomics

Background:

  • Complex human phenotypes arise from multiple genetic and environmental factors.
  • Gene-environment interactions (G × E) are crucial for understanding trait development and disease risk.
  • Large population biobanks enable advanced statistical analysis of G × E.

Purpose of the Study:

  • To review state-of-the-art statistical methodologies for gene-environment interaction (G × E) analysis.
  • To provide an overview of current approaches for both single-variant and polygenic G × E mapping.
  • To discuss future directions and challenges in G × E research.

Main Methods:

  • Survey of statistical methods for single-variant G × E mapping.
  • Review of techniques for polygenic G × E analysis.
  • Discussion of emerging analytical strategies.

Main Results:

  • Comprehensive overview of existing statistical methodologies for G × E analysis.
  • Identification of various approaches for both targeted and genome-wide G × E studies.
  • Synthesis of current knowledge and future research avenues.

Conclusions:

  • Statistical methods for G × E analysis are rapidly evolving.
  • Accurate G × E analysis is vital for precision medicine applications.
  • Future research should address remaining challenges in G × E studies.