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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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Behavioral Genetics and Its Designs01:23

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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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Criteria for Causality: Bradford Hill Criteria - II01:28

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Epistasis Analysis01:09

Epistasis Analysis

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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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Incomplete Dominance01:43

Incomplete Dominance

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Related Experiment Video

Updated: Oct 18, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Causal Inference with Genetic Data: Past, Present, and Future.

Jean-Baptiste Pingault1, Rebecca Richmond2, George Davey Smith2

  • 1Division of Psychology and Language Sciences, University College London, London WC1H 0AP United Kingdom.

Cold Spring Harbor Perspectives in Medicine
|September 28, 2021
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Summary

Genetics and causal inference methods are converging, leveraging natural experiments for insights into human disease and development. This synergy promises significant advancements in understanding and translational applications.

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Area of Science:

  • Biomedical research
  • Genetics
  • Causal inference

Background:

  • Genetics and causal inference are distinct fields.
  • Genetics provides natural experiments.
  • Convergence offers new analytical approaches.

Purpose of the Study:

  • Introduce genetically informed causal inference methods.
  • Explain the synergy between genetics and causal inference.
  • Highlight the potential of this interdisciplinary field.

Main Methods:

  • Review of genetic principles.
  • Introduction to causal inference concepts.
  • Discussion of genetically informed methodologies.

Main Results:

  • Demonstration of how genetic data facilitates causal inference.
  • Identification of key concepts for researchers.
  • Overview of current and future applications.

Conclusions:

  • The convergence of genetics and causal inference is a rapidly developing field.
  • This interdisciplinary approach enhances understanding of human disease and development.
  • Anticipated translational applications offer significant clinical benefits.