Leveraging microbiome information for animal genetic improvement

Area of Science:

• Animal genetics and microbiology
• Host-microbiome interactions
• Genomic prediction modeling

Background:

• The microbiome plays a crucial role in host phenotypic variation.
• Genomic prediction models are vital for genetic improvements in livestock.
• Understanding the holobiont is key to unlocking full genetic potential.

Purpose of the Study:

• To explore the integration of microbiome data into genomic prediction models for farmed animals.
• To investigate how host genome influences microbiome-mediated phenotypes.
• To enhance genetic gain by accounting for host-microbiome interactions.

Main Methods:

• Developing genomic prediction models that incorporate microbiome information.
• Analyzing the indirect genetic effects of the host on microbiome composition and function.
• Evaluating the impact of holobiont-informed models on predicting animal performance.

Main Results:

• Genomic models incorporating holobiont data show potential for accelerated genetic gains.
• Host genetics significantly influence microbiome-associated traits.
• Accounting for indirect genetic effects is crucial for accurate prediction.

Conclusions:

• Integrating microbiome data into genomic prediction models offers a promising avenue for livestock breeding.
• Holobiont genomics can lead to more effective genetic selection strategies.
• Future research should focus on refining models to capture complex host-microbiome interactions.