Metagenomic Predictions: A Review 10 years on

Abstract

Metagenomic predictions use variation in the metagenome (microbiome profile) to predict the unknown phenotype of the associated host. Metagenomic predictions were first developed 10 years ago, where they were used to predict which cattle would produce high or low levels of enteric methane. Since then, the approach has been applied to several traits and species including residual feed intake in cattle, and carcass traits, body mass index and disease state in pigs. Additionally, the method has been extended to include predictions based on other multi-dimensional data such as the metabolome, as well to combine genomic and metagenomic information. While there is still substantial optimisation required, the use of metagenomic predictions is expanding as DNA sequencing costs continue to fall and shows great promise particularly for traits heavily influenced by the microbiome such as feed efficiency and methane emissions.

Large-scale analysis of sheep rumen metagenome profiles captured by reduced representation sequencing reveals individual profiles are influenced by the environment and genetics of the host

“…Many of these studies have generated a microbial relationship matrix (i.e., MRM), calculated in a variety of ways, to represent the relationships between samples, followed by a microbial BLUP (i.e., MBLUP) approach (5,6). As discussed by Ross and Hayes (37), these studies have shown promise for using microbial pro les to predict individual performance. While not addressed directly in our study, clustering of individuals by methane selection line suggests an ability to predict environmentally important traits in sheep using this information.…”

Combining host and rumen metagenome profiling for selection in sheep: prediction of methane, feed efficiency, production, and health traits

“…Metagenome Relationship Matrices (MRMs) were rst developed by Ross et al [27] and have become the standard method for integrating metagenome pro les into prediction equations in livestock [15]. In our study, MRMs were generated separately for each Group.…”

“…Furthermore, these microbes and pro les have been shown to be heritable [12][13][14], suggesting sustained progress can be achieved through selection practices. However, previous studies have typically been on a small scale and used technologies that either do not scale well when considering implementation in industry (e.g., whole genome sequencing) or do not capture the breadth of diversity in the rumen (i.e., prokaryotic 16S rRNA gene sequencing) [15]. Recently, Hess et al [16] developed a Restriction Enzyme-Reduced Representation Sequencing (RE-RRS) approach that overcomes the shortcomings of other technologies to harness metagenomic information that can be used at the production level, due to its low cost and potential for high throughput.…”

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data

“…The host associated microbiome is known to influence many traits. A number of studies have reported that combining microbiome and genomic information could improve the prediction accuracy compared with only genomic data [ 23 ]. The makeM() function can easily normalize operational taxonomic units (OTU) as well as calculate the kinship matrix based on microbiome data.…”