Degree Type

Dissertation

Date of Award

2021

Degree Name

Doctor of Philosophy

Department

Animal Science

Major

Animal Breeding and Genetics (Quantitative Genetics)

First Advisor

Jack Dekkers

Abstract

The overall objective of this thesis was to characterize the genetic basis of disease resilience traits in wean-to-finish pigs from a natural disease challenge model, including growth and carcass performance, clinical disease traits, and feed and water intake and behavior traits under a severe natural polymicrobial disease challenge. This thesis includes five manuscripts that have or will be submitted to be published in scientific journals. The first study aimed to estimate genetic parameters of growth and carcass performance and clinical disease traits under this disease challenge, to develop methods of including data on pigs that died before slaughter into genetic analysis of disease resilience, and to evaluate and validate the usefulness of traits derived from feed intake data and subjective health scores as genetic indicator traits for disease resilience. The second study continued to estimate genetic parameters for feed and water intake and behavior traits, to estimate the genetic correlations of these traits with growth rate and clinical disease traits, to evaluate the usefulness of day-to-day variation and proportion of off days derived from drinking behavior traits as indicators of disease resilience, and to develop other water and feed intake behavior indicator traits to select for disease resilience. These first two studies found that disease resilience traits, including treatment rates, mortality rates, and subjective health scores were lowly heritable, while growth and carcass performance, and feed and water intake and behavior traits under the disease challenge were moderate to highly heritable. Results also showed that resilience indicator traits derived from the feed and water intake patterns can be used for selection of more resilient pigs. Especially promising traits were day-to-day variation in feed intake duration and average daily number of water intake visits. The third study explored the opportunities of applying reaction norm models to identify disease resilient pigs. The first objective was to quantify the disease challenge load that pigs within a pen were exposed to, using growth rate and clinical disease phenotypes. The second objective was to quantify genetic variation in disease resilience using reaction norm models based on the developed challenge loads. The best challenge load for growth rate in the challenge nursery was the challenge load derived based on early growth rate in the finisher, while the challenge load derived from the clinical disease traits across the challenge nursery and finisher was the best challenge load for growth rate in the finisher and treatment rate in the challenge nursery and across challenge nursery and finisher. Reaction norm models were found to be able to identify the genetic variation across different disease challenge loads. The results of this study can be implemented to select more resilient animals across a range of challenge loads, or high-performing animals at a given challenge load, or a combination of these. The fourth study aimed to identify genomic regions that are associated with disease resilience using genome-wide association studies (GWAS) and fine mapping tools. The major histocompatibility complex (MHC) and several other quantitative trait loci (QTL) were identified. Four QTL were identified in the MHC region for growth rate in the challenge nursery by fine mapping, including one single nucleotide polymorphism (SNP) with major effects. These QTL play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience. The final study developed an efficient leave-one-out cross-validation method for prediction of breeding values under a general mixed linear model with multiple random effects, which can be used to predict the accuracy of genomic prediction of disease resilience traits. In conclusions, disease resilience traits obtained under the natural disease challenge model were heritable and can, therefore, be selected for. Multiple important QTL, including MHC, were identified for disease resilience traits. Genetic indicators derived from feed and water intake patterns, reaction norm models using the developed challenge load, and QTL and genetic markers identified, along with genomic prediction, can be used to facilitate the genetic progress of disease resilience.

DOI

https://doi.org/10.31274/etd-20210609-33

Copyright Owner

Jian Cheng

Language

en

File Format

application/pdf

File Size

325 pages

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