Streamlining Methane Emission Data Management

Introduction to Portable Accumulation Chambers (PAC)

PACs represent a breakthrough in measuring methane emissions from sheep.
Accurate measurement in natural grazing settings.
Valuable in environmental research.

Challenges in PAC Data Management

Managing PAC data poses significant challenges.
Issues include processing large datasets, data cleaning, and integration into breeding programs.
Highlights the need for efficient data management solutions.

R Shiny Application for PAC Data Management

Features of the Developed R Shiny Application

Intuitively designeed for researchers and breeders.
User-friendly interface with capabilities for downloading cleaned data.
Robust quality control mechanism ensuring data accuracy and integrity.

Replacing Traditional Methods with R Shiny Application

R Shiny application replaces traditional, semi-manual PAC data management methods.
Automated process enhances data processing efficiency and accuracy.
Revolutionizes environmental data management in sheep breeding.

Future Development Plan for the R Shiny Application

Planned integration with statistical genetics software like ASReml, BLUPf90, and JWAS.
Enables handling sophisticated data sets, enhancing utility in genomic selection.

Investigating Genomics Models through the App

Current implementation includes GBLUP and microbiability analysis.
Focus on expanding to multiomics models for comprehensive genetic analysis.

Limitations of Traditional Microbiome Analysis

Traditional focus on microbiome data analysis and microbiability.
Limited in scope for breeding due to not fully accounting for genetic interactions.

Advantages of the Multiomics Model

Comprehensive approach leveraging microbiome data alongside other genetic information.
More effective for breeding programs, providing deeper insights into genetic traits.

Two-Step Process in Multiomics Modeling

First step: microbiome features as response variables with genotype as explanatory variable.
Second step: methane emission as response variable, microbiomes as explanatory variables.
Quantifies heritable components of microbiomes, enhancing genomic selection accuracy.

Computational Challenges and PCA Application

Challenge in handling 24,000 microbiome features.
Employed PCA for dimensionality reduction, testing various component counts.

Results and Implications of Multiomics Model Analysis

Analysis reveals trade-off between accuracy and bias with different PCA components.
Results show improvement in correlation and bias adjustments with varying PCA components.

Future Directions and Conclusion

Refining the multiomics model for optimized accuracy and bias balance.
Integration with advanced software for enhanced utility in genomic selection.
Aim: informed breeding decisions for sustainable sheep farming.