Combining genomic and metagenomic predictions increases accuracy

Dr Elizabeth Ross from QAAFI's Centre for Animal Science has pioneered the use of a new type of genomics research in animals, by analysing microbial information from the animal’s microbiome to develop accurate predictions of the heritability of high value traits like methane emissions and feed conversion efficiency. In this seminar, Dr Ross explains how the technology can be used with other genomic data to better predict methane emissions in sheep.

Seminar abstract

Methane production from rumen methanogenesis contributes approximately 71 per cent of greenhouse gas emissions from the agricultural sector.

This study performed genomic predictions for methane production from 99 sheep across 3 years using a residual methane phenotype that is log methane yield corrected for live weight, rumen volume, and feed intake.

Using genomic relationships, the prediction accuracies (as determined by the correlation between predicted and observed residual methane production) ranged from 0.058 to 0.220 depending on the time point being predicted.

The best linear unbiased prediction algorithm was then applied to relationships between animals that were built on the rumen metabolome and microbiome. Prediction accuracies for the metabolome-based relationships for the two available time points were 0.254 and 0.132; the prediction accuracy for the first microbiome time point was 0.142.

The second microbiome time point could not successfully predict residual methane production. When the metabolomic relationships were added to the genomic relationships, the accuracy of predictions increased to 0.274 (from 0.201 when only the genomic relationship was used) and 0.158 (from 0.081 when only the genomic relationship was used) for the two-time points, respectively.

When the microbiome relationships from the first time point were added to the genomic relationships, the maximum prediction accuracy increased to 0.247 (from 0.216 when only the genomic relationship was used), which was achieved by giving the genomic relationships ten times more weighting than the microbiome relationships.

These accuracies were higher than the genomic, metabolomic, and microbiome relationship matrixes achieved alone when identical sets of animals were used. 

Read full article Genomic predictions for enteric methane production are improved by metabolome and microbiome data in sheep (Ovis arise) Dr Elizabeth M Ross, Ben J Hayes, David Tucker, Jude Bond, Stuart E Denman, and Victor Hutton Oddy published in the Journal of Animal Science, Volume 98, Issue 10, October 2020, https://doi.org/10.1093/jas/skaa262

Dr Elizabeth Ross,

Research Fellow, Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation

Dr Elizabeth Ross undertook her PhD through La Trobe University in Melbourne hosted within the Department of Primary Industries, and supervised by Professor Ben Hayes and Professor Ben Cocks. This work involved developing new approaches for characterising the rumen microbiome using untargeted “shotgun” high throughput sequencing. Her research involved developing the method of Metagenomic predictions, which uses whole metagenome wide markers to predict the host phenotype. After a short Post Doc on RNA sequencing in chickpea at the University of Melbourne, Elizabeth moved out of academia for two years before moving to Queensland to join the Queensland Alliance for Agriculture and Food Innovation at the University of Queensland. Currently Dr Ross is involved in research projects including the assembly of a platinum quality reference genome, the transcriptional analysis of Brahman datasets, developing new tools and approaches for long read sequencing and many others.

Presented by: Dr Elizabeth Ross, Centre for Animal Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, T. 07 33462162 E. e.ross@uq.edu.au