GENETIC EPIDEMIOLOGY

Scope & Guideline

Transforming Health Outcomes with Genetic Insights

Introduction

Immerse yourself in the scholarly insights of GENETIC EPIDEMIOLOGY with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageEnglish
ISSN0741-0395
PublisherWILEY
Support Open AccessNo
CountryUnited States
TypeJournal
Convergefrom 1984 to 2024
AbbreviationGENET EPIDEMIOL / Genet. Epidemiol.
Frequency8 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
Address111 RIVER ST, HOBOKEN 07030-5774, NJ

Aims and Scopes

The journal 'Genetic Epidemiology' aims to bridge the gap between genetics and epidemiology, focusing on the genetic determinants of health and disease across populations. It emphasizes the integration of genetic data with epidemiological methods to enhance our understanding of disease etiology, risk factors, and the interplay between genetic and environmental influences.
  1. Genetic Association Studies:
    The journal publishes research on genetic association studies that identify genetic variants associated with various diseases and traits, often utilizing genome-wide association studies (GWAS) to explore genetic risk factors.
  2. Mendelian Randomization:
    A significant focus is placed on Mendelian randomization techniques, which use genetic variants as instrumental variables to infer causal relationships between exposures (like lifestyle factors) and outcomes (such as diseases).
  3. Genetic Epidemiology of Complex Traits:
    Research in this area investigates the genetic architecture of complex traits, including polygenic risk scores, gene-environment interactions, and the heritability of traits across diverse populations.
  4. Integration of Multi-Omics Data:
    The journal emphasizes studies that integrate genetic data with other omics data (e.g., transcriptomics, proteomics) to gain a comprehensive understanding of biological pathways influencing health.
  5. Population Diversity and Genetics:
    Research that addresses genetic diversity among populations, including studies on admixed populations and transancestry analyses, is a core area, reflecting the journal's commitment to inclusivity in genetic research.
  6. Ethical, Legal, and Social Implications (ELSI):
    The journal also explores the ethical, legal, and social implications of genetic research, particularly in the context of gene-environment interactions and public health.
The journal has observed several emerging themes reflecting the evolving landscape of genetic epidemiology. These trends highlight the integration of advanced methodologies and interdisciplinary approaches to address complex health issues.
  1. Machine Learning and AI in Genetic Research:
    There is a growing trend towards utilizing machine learning and artificial intelligence techniques to analyze genetic data, enhance predictive models, and identify novel genetic associations.
  2. Gene-Environment Interactions:
    Research on gene-environment interactions is gaining prominence, reflecting an increased interest in understanding how environmental factors and genetic predispositions jointly influence health outcomes.
  3. Polygenic Risk Scores and Predictive Modeling:
    The development and application of polygenic risk scores for predicting disease risk across diverse populations are trending, enabling personalized medicine approaches.
  4. Integration of Genomic and Clinical Data:
    There is an emerging focus on integrating genomic data with electronic health records and biobanking resources to enhance the understanding of genetic underpinnings of diseases in real-world settings.
  5. Ethical Considerations in Genetic Research:
    Research addressing the ethical implications of genetic studies, particularly in diverse populations and regarding data privacy, is becoming increasingly relevant.
  6. Longitudinal and Multi-Omics Studies:
    There is a trend towards longitudinal studies that incorporate multi-omics data to uncover the dynamics of genetic influence over time, providing deeper insights into disease progression and risk.

Declining or Waning

While 'Genetic Epidemiology' continues to thrive in various research areas, certain themes appear to be declining in prominence. These waning scopes may reflect shifts in research focus or advancements in methodologies that render previous approaches less relevant.
  1. Traditional Statistical Methods:
    There seems to be a decline in the use of traditional statistical methods for genetic association analysis, as more sophisticated machine learning and Bayesian approaches gain traction.
  2. Single Phenotype Studies:
    Research focusing exclusively on single phenotypes is becoming less common, with a notable shift towards multi-phenotype analyses that capture the complexity of genetic interactions and their impact on health.
  3. Basic Genetic Mapping Techniques:
    Basic genetic mapping techniques are waning in favor of more advanced methods that incorporate multi-omics data and sophisticated computational frameworks, leading to a decrease in publications centered on conventional mapping.
  4. Clinical Trials Focused Solely on Genetic Factors:
    Studies that focus exclusively on the genetic factors of diseases without considering environmental or social determinants are less frequently published, indicating a broader understanding of health determinants.

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