Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Area of Science:

• Genetics
• Genomics
• Personalized Medicine

Background:

• Genome-wide association studies (GWAS) identify genetic loci linked to human traits.
• Current GWAS approaches often overlook complex environmental interactions and pleiotropy.
• There is a need for advanced models to predict disease risk using genomic and environmental data.

Purpose of the Study:

• To highlight the limitations of traditional GWAS.
• To emphasize the importance of gene-environment (G×E) and gene-gene (G×G) interactions.
• To advocate for improved predictive models for precision medicine.

Main Methods:

• Discusses the concept of pleiotropy (one locus affecting multiple traits).
• Explains the significance of gene-environment (G×E) interactions.
• Highlights the role of gene-gene (G×G) interactions in disease etiology.

Main Results:

• Pleiotropy, G×E, and G×G interactions reveal biological pathway impacts.
• These interactions identify genes involved in disease within specific environmental contexts.
• This information enhances personal risk assessment and medical intervention strategies.

Conclusions:

• Advanced models incorporating genetic interactions and environmental factors are essential for precision medicine.
• Understanding pleiotropy and G×E/G×G interactions improves disease risk prediction.
• This approach enables tailored medical interventions based on individual genotype and environment.