Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

Lay Summary Gut microbiota has received significant research attention in farm animals because of its close relationship with host performance. We chose eight approaches to create a square covariance matrix that characterizes the relationship among animals based on their gut microbiota composition. Then, we fitted this information with linear models to evaluate the proportion of phenotypic variance explained by gut microbiota composition and predict host growth and body composition traits in three pig breeds. We found that different matrices had varying performance in predicting host phenotypes, but the results highly depended on the trait and breed considered in the prediction. Our findings highlight possible alternative approaches to incorporate gut microbiome data in regression models and emphasize the value of gut microbiome data in better understanding complex traits in pigs with diverse genetic backgrounds. The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 +/- 2.5 d of age). Rectal swabs were taken from each animal at 158 +/- 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine. We assessed alternative ways to incorporate microbiome data into regression models to estimate variance components and predict host phenotypes in three pig breeds.