Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Causality in Epidemiology

Causality in Epidemiology

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...

Correlation and Causation

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...

Behavioral Genetics and Its Designs

Behavioral Genetics and Its Designs

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.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...

Criteria for Causality: Bradford Hill Criteria - II

Criteria for Causality: Bradford Hill Criteria - II

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:

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cytomegalovirus-encoded immediate early 1 protein perturbs neural progenitor proliferation via interfering with host PML-DISC1 interaction.

The Journal of biological chemistry·2026

Same author

Analysis of a deeply-phenotyped familial hypercholesterolemia cohort from Mexico shows a role for both rare and common alleles across known dyslipidemia genes and reveals structural variation in a novel locus.

Human genomics·2025

Same author

DISC1-PML protein interaction for congenital CMV infection-induced cortical neural progenitor deficit: perturbance of host signaling via viral IE1.

bioRxiv : the preprint server for biology·2025

Same author

Diverse ancestral representation improves genetic intolerance metrics.

Nature communications·2025

Same author

Polygenic risk score portability for common diseases across genetically diverse populations.

Human genomics·2024

Same author

An atypical form of 60S ribosomal subunit in Diamond-Blackfan anemia linked to RPL17 variants.

JCI insight·2024

Search research articles

Related Experiment Video

Updated: Jun 24, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

From association to causality: the new frontier for complex traits.

Nicholas Katsanis¹

¹McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Genome Medicine

|April 4, 2009

Summary

Genomic studies have identified many disease-associated regions, but pinpointing specific genes and understanding disease mechanisms remains challenging. A new approach combining genomic and functional studies is needed for complex traits.

Related Experiment Videos

Last Updated: Jun 24, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Genomics
Human Genetics
Disease Mechanisms

Background:

Advances in technology have cataloged numerous genomic regions linked to human phenotypes.
However, the genetic causes of most diseases remain unknown.
Current genome-wide association studies often lack the resolution to identify specific genes or create functional disease models.

Purpose of the Study:

To discuss the limitations of current genomic approaches in identifying disease-causing genes.
To highlight the need for a paradigm shift in studying complex traits.
To emphasize the importance of integrating functional studies with genomic data.

Main Methods:

Review of current genomic and association study methodologies.
Discussion of the limitations in resolving genetic underpinnings of complex traits.
Conceptual framework for integrating genomic and functional studies.

Main Results:

Genome-wide association studies provide limited resolution for identifying specific genes.
Developing mechanistic disease models from genomic data is challenging.
Increasing cohort sizes without functional interpretation may not improve genetic dissection of complex traits.

Conclusions:

A paradigm shift is necessary for studying complex traits.
Combinatorial application of genomic and functional studies is crucial.
Functional interpretation is essential for understanding the genetic basis of disease.