Development of Prediction Models of Methane Production by Sheep and Cows Using Rumen Microbiota Data Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Boyang Zhang Graduate Program in Animal Sciences The Ohio State University 2018 Master's Examination Committee: Dr. Zhongtang Yu, Advisor Dr. Moraes, Co-advisor Dr. Firkins 1 Copyrighted by Boyang Zhang 2018 2 Abstract Methane emission from the rumen leads to approximately 10 % loss of the ingested energy, at the conversion of digestible to metabolizable energy. Besides research on mitigation of methane emission, much research interests have also been gravitated towards development of prediction models of methane emissions from livestock because the global warming is reducing agriculture productivity (Johnson and Johnson, 1995). An accurate prediction of enteric methane production from cattle and sheep can assist in balancing the increased livestock production with subsequent environmental impacts. Methane is an inevitable byproduct of the microbial fermentation processes in the rumen, and certain ruminal microbes have direct impacts to methane production (Morgavi et al., 2010). Thus, we hypothesized that the inclusion of individual microbial groups as predictor variables could improve the robustness and accuracy of prediction models. However, inclusion of microbial variables into prediction models can result in overfitting. Machine-learning algorithms can automatically select the key explanatory predictors, and Linear Mixed Models can provide a framework to predict random effects describing between animal variations. We proposed three novel frameworks for subset selections of microbial variables (MV) and one framework for generalized linear mixed models (GLMM) using L1-penalization (GLMMLASSO) selection with cross-validations (CV) ii to address the overfitting problems and to develop parsimonious prediction models of methane production. Methane emission can be expressed as unit methane per animal per d, per unit of dry matter intake (DMI), or per unit of metabolic body weight (MBW) per d. Thus, we developed prediction models based on g CH4/per animal/d (Animal-based models), g CH4/kg DMI (DMI-based models), and g CH4/kg metabolic bodyweight/d (MBW-based models). Two datasets were used: one dataset from a study that compared the rumen microbiota and methane production among sheep in New Zealand, while the other dataset that was collated from two datasets (one generated using dairy cows in Finland, and the other generated using steers in Australia). The cattle datasets were generated from studies that used different anti-methane feed additives to mitigate methane emission. Each dataset contained both animal data and relative sequence abundance (RSA) of genera of rumen microbes including bacteria, methanogens, protozoa, and fungi. Relative abundance of a genus was expressed as % of the sequences assigned to that genus over the total sequences of a marker gene, 16S rRNA gene for bacteria and methanogens, 18S rRNA gene for protozoa, and ITS1 for fungi). Subset and GLMMLASSO selections of MV combined with CV were based on minimal Bayesian information criterion (BIC). Linear mixed effects models were built based on the MV selected. The cross-validation was used to identify the best subset of MV that resulted in the lowest mean square prediction error (MSPE) to include in the prediction models. We also compared our new models with traditional models that only contained DMI, acetate: propionate ratio, and BW. iii From the sheep dataset, we developed 3 parsimonious models when the size of the pool of potential variables was limited to ≤132, but parsimonious models were not developed when the size of the pool of potential variables was over 300. Most importantly, GLMMLASSO successfully converged when the penalty parameter that controls the shrinkage, Lambda, was set between 0 to 1,000, selecting about 10 variables for all the models (animal-, DMI-, or MBW-based) when the size of pool of potential predictor variables was limited to be between 132 and 310. The GLMMLASSO approach combined with CV identified the important variables to explain the effects of the 8 feed additives in the combined cow dataset. The random effect associated with the between-animal variance component was small and of similar magnitude to the random error variance component. The linear mixed effects models based on GLMMLASSO selection of MV have root mean square error as the percentage of the mean of methane emission (RMSPE %) reduced by 2.3 percent points and performed better than those based on forward-stepwise MV selection and the traditional models based only on animal variables. Log transformation of the microbial data generally improved model performance, probably due to a better monotonicity of the MV. This thesis research indicates that individual groups of rumen microbes can be included in methane prediction models to improve prediction of methane production from sheep and cows. In conclusion, we built frameworks for model selections using MV, and GLMMLASSO combined with CV and forward-stepwise selection allowed identification of significant MV that can be included in methane prediction models that solved the overfitting problem and improved the prediction accuracy. GLMMLASSO selection coupled with CV is iv a useful method to extract a parsimonious and significant subset of MV from hundreds of MV that can improve the accuracy of methane prediction models. This is the first research to develop methane prediction models that contain rumen microbes as predictor variables. Future research can focus on the exploration of the common microbial predictor variables in the cow and the sheep datasets to understand the contribution of the microbes to methane production in the rumen and to develop models with a more mechanistic nature. v Acknowledgments I would like to thank Dr. Yu, Dr. Moraes and Dr. Firkins for their time and patience working with me on my thesis project. I would also like to thank the entire lab of my advisor Dr. Yu and my co-advisor Dr. Moraes for all their helps. Finally, would like to thank Dr. Peter Janssen for providing the data of sheep, Dr. Bayat Alireza for providing the dairy cow data, and Chris McSweeny for providing the steer data. vi Vita 2016................................................................Bachelor of Agriculture, Northeast Agricultural University, China 2016 to present ..............................................Master of Science, Department of Animal Sciences, The Ohio State University Fields of Study Major Field: Animal Sciences vii Table of Contents Abstract ............................................................................................................................... ii Acknowledgments.............................................................................................................. vi Vita .................................................................................................................................... vii List of Tables ...................................................................................................................... x List of Figures .................................................................................................................... xi Chapter 1. Introduction ....................................................................................................... 1 Chapter 2. Literature Review .............................................................................................. 4 The Rumen Microbial Ecosystem and Methane Production from Ruminants ............... 4 The Rumen Microbiota, Feed Digestion and Fermentation: ...................................... 4 Quantitative analysis of the rumen microbiota by qPCR and NGS: ........................... 6 Methane production in the rumen and hindgut and mitigation of emission: .............. 8 Prediction Models of Methane Emission from Ruminants ........................................... 10 Statistical models: ..................................................................................................... 12 Dynamic models: ...................................................................................................... 14 Chapter 3. Exploratory Data analysis and Statistical Models ........................................... 18 Abstract ......................................................................................................................... 18 Introduction ................................................................................................................... 19 Methods and Materials .................................................................................................. 21 Sheep dataset:............................................................................................................ 22 Cow data: .................................................................................................................. 23 Results and Discussion ................................................................................................. 24 EDA and PCA of Sheep Dataset:.............................................................................. 24 EDA and PCA of Cow Dataset: ................................................................................ 25 Chapter 4. Variable Selections Using the Forward Stepwise Selection ........................... 41 Abstract ........................................................................................................................