Using rumen microbial predictors for genomic prediction of feed efficiency

Obtaining phenotypic measures of feed efficiency requires measuring intake levels and growth rates over a period of approximately 8 weeks (2 weeks of adaption and 6 of measurement), which is expensive and low-throughput. Rumen microbial community (RMC) profiles have shown to be associated with feed efficiency traits in ruminants and so may be a suitable proxy. Using a dataset of 1298 animals across 4 genetically linked flocks that were measured through a feed intake facility (FIF), we predicted feed efficiency from RMC profiles and obtained higher prediction accuracies compared to host genomic prediction. The genetic and phenotypic correlations between feed efficiency traits measured from the FIF and predicted from RMC profiles were estimated as 0.64 and 0.33 for mid-trial intake and 0.47 and 0.30 for residual feed intake (RFI). These results suggest RMC profiles have the potential to be used as a proxy for feed efficiency traits in ruminants.</p

Profiling the rumen microbiome in New Zealand for potential application in ruminants

The conversion of feed in ruminant animals is driven by microbial fermentation in the rumen. This produces a mix of volatile fatty acids (VFA) that are a major energy source for the animal. Changes in rumen microbial composition can affect the composition of the VFA mix, and therefore affect the overall performance and health of the animal. The relationship between rumen microbiome community (RMC) profiles and livestock traits have been previously investigated and associations with methane, performance and feed intake traits identified. However, the rumen microbial composition is highly variable across environments which may change the relationship of the RMC profiles with livestock traits. Here, we investigate the variation of RMC profiles of ruminants located across a diverse range of farms and flocks in New Zealand. Over 10,000 rumen samples from cattle, deer, goats, and sheep were collected from across New Zealand and sequenced using a restriction enzyme reduced representation sequencing approach to generate RMC profiles. Sequences were classified with the Genome Taxonomy Database (GTDB) using the GBS-TaFFE (https://github.com/BenjaminJPerry/GBSTaFFE) pipeline to determine microbial taxonomy. Results suggest that feed followed by species are the main sources of variation in the RMC, although the top 10 most abundant genera was consistent across these variables. Nevertheless, RMC profiles collected on the same type of ruminants grazing similar feeds were found to be similar, even though samples were collected at geographically distant farms and different seasons and years. Associations between direct measurements and a proxy trait predicted from RMC profiles for methane emissions and feed efficiency was also examined, where moderate-to-high genetic correlations and moderate phenotypic correlations were observed on a subset of the samples collected on sheep. More importantly, we found that RMC profiles could be used as a proxy trait for methane and feed efficiency in selective breeding programs.</p