1. Grow-Finish Challenge (1.2)
Activity 1
Grow-Finish Challenge (1.2)

2. Natural Challenge (Pig & Sample Flow) CDPQ Test Station (16 weeks)
60-75 barrows/batch from
High Health Multiplier Farm
Weaned at 21 days old
Blood
Samples collected 1
HIR, DRAA, CBC, WBT, Serology 2
HIR, Serology, microbiome 3
CBC, Nab, IgG, WBT, microbiome 4
CBC, IgG, WBT, Serology, microbiome 5
Serology 6
WBT, Serology (sick & healthy)
transported
CRSAD Building
(3 weeks)
Nursery I
D5 – Blood 1
D19 – Blood 2 &
exit Nursery I
D5 denotes number of days spent in both
research buildings (CRSAD and CDPQ) * *
CDPQ Test Station
(4 weeks)
Nursery II
D33 – Blood 3
CDPQ Test Station (16 weeks)
Grower & Finisher
D61 – Blood 4
D102B1-8/D145B9-on – Blood 5
transferred

3. Natural Challenge (Pig & Sample Flow)
Severity of disease challenge
Clean
Mild challenge
Severe challenge
60-75 barrows/batch from
High Health Multiplier Farm
Weaned at 21 days old
transported
Types of challenge
Nursery II
Introduction of own seeders from grower & finisher pens
Grower & Finisher
Introduction of seeders from commercial farms
Pulse medication schedule
CRSAD Building
(3 weeks)
Nursery I
D5 – Blood 1
D19 – Blood 2 &
exit Nursery I
CDPQ Test Station
(4 weeks)
Nursery II
D33 – Blood 3
CDPQ Test Station (16 weeks)
Grower & Finisher
D61 – Blood 4
D102B1-8/D145B9-on – Blood 5
transferred

4. Integrated statistical and bioinformatic analyses
3500 YxLR weaner pigs from high-health herds
PREDICTIVE
PHENOTYPES collected on 960 young healthy pigs
80 K SNP genotypes
Immune Response
HIR
DRAA
Blood Stimulation
Phagocytosis
Blood Transcriptome
Blood Metabolome
Blood Proteome
TARGET RESILIENCE PHENOTYPES
RESILIENCE
collected on all 3500 pigs
Mortality/Morbidity
Growth performance
Feed intake
Water intake
Disease diagnostics
Environmental Factors
Trait** Genetics
Parameters
Architecture
Mechanisms
Genetic
Prediction
of Resilience
Phenotypic
PROJECT
DELIVE-
RABLES
Gut Microbiome
Integrated statistical and bioinformatic analyses
Blood transcriptome*
Gut Microbiome*
NATURAL CHALLENGE FACILITY
QUARANTINE NURSERY
Functional Annotation
FAANG
NIFA Extension
Figure 1. Schematic overview of phenomics to develop genetic and phenotypic predictors of resilience. Red underlined text indicates phenomic data and information that will be generated in the proposed work, building on phenotypic and genotypic data that will be generated as part of the existing Genome Alberta and Genome Canada project, as well as in the FAANG project, in which a total of 3,500 healthy weaner pigs are first evaluated in a quarantine nursery and then entered in a natural challenge nursery-grow-finish research facility. Project data, analyses and results will both utilize and contribute to the FAANG project.
Data collected only on a limited number of case-control animal
Resilience, indicator, and intermediate –omics traits and their interrelationships

5. Immune Response Measures
Activity 1.1
Immune Response Measures

6. Immune Response (IR) Measures
IR measures that predict resilience phenotypes will greatly enhance genetic selection programs.
Several IR measures are being evaluated for this purpose:
Complete Blood Counts (CBC)
High Immune Response Technologies (HIR)
Disease Resistance Assay for Animal (DRAA)
Other Assays (e.g. cytokines, Natural Ab, IgG)

7. Complete Blood Counts (CBC)

8. Complete Blood Counts (CBC)

9. Exploring CBC and Growth

10. Exploring APP and Growth
APP= Acute Phase Proteins
All N=31

11. High Immune Response technology:
A broad based approach to disease resistance in swine
Professor Dr. Bonnie Mallard and Dr. Julie Schmied
Ontario Veterinary College
University of Guelph, Ontario, Canada

12. The Immune System Provides Defence Against Disease

13. Experimental Protocol for Immune Response Phenotyping of Pigs
Day 0
Bleed pigs to measure pre-immunization serum antibody to AMIR Ag by ELISA
AMIR and CMIR test Antigens (combined, IM)
Treatments
Measurements
Day 14
Skin thickness measurements & bleed for post-immunization serum antibody
Day 16 (48hrs)
Skin thickness measurements
CMIR test Ag (ID)
PBS Control (ID)

14. Two Tests are Performed to Capture Broad-based
Disease Resistance
ANTIBODY- MEDIATED IMMUNE RESPONSE
(AMIR)
CELL-MEDIATED IMMUNE RESPONSE (CMIR)

15. Progress to date: Quarter 1 - 4 HIR Phenotyping
December 2015 – Staff at Deschambault trained in HIR test protocol by J. Schmied and S. Cartwright (Q1)
Cycle 1 : Batch 1-7
On farm (Q2 & Q3)
Laboratory analysis (Q3)
HIR phenotypes have been sent to Dr. Jack Dekkers and Austin Putz for comparative analysis with production and health traits
Cycle 2 : Batch 8-14
On farm (Q3 & Q4)
Laboratory analysis underway (to be completed by Q5)
Batch 8 was not tested due to pre-mix feed issue at CRSAD
Batch 11 not tested due to schedule interruption instigated at multiplier
Cycle 3 : Batch 15-21
Currently underway
Batch 15 – 17 completed on farm

16. Troubleshooting: HIR Phenotyping ELISA
High background readings in pre-immunization sera observed across batches in Cycle 1
Surveys sent to multipliers 1-7 in an attempt to identify possible management related causes
ELISA troubleshooting was concurrently conducted by J. Schmied
Background was found to be caused by non-specific binding of antibodies in un-immunized pig sera
Assay blocking buffer and serum diluent adjusted to compensate
Maternal antibody exists and is an occasional issue, but manageable for the purposes of the test
ELISA now running optimally

17. Preliminary Data: Immune Response Phenotype Rankings for Cycle 1
Data analyzed using SAS general linear model : Y= Mi + Tj + eij
Where Y = Immune Response Phenotype , M = Multiplier (i = 1, 2, 3, 4, 5, 6 & 7) and T = Technician (j )

18. Disease resistance Assay for Animals (DRAA)
PBMCs A B C D E
mitogens
(immune stimulants)
Blastogenic
index
cell numbers
time
Mitogens: ConA, PHA, PKW, LPS, PMA/Ion
reflect different immunologic pathways
Multivariate
analyses
PCA:
PC1 – mainly TCR/CD3 dependent signaling
PC2 – mainly TLR4 specific responses
PC3 – protein kinase C dependent pathways

19. Natural antibodies (Nab)
Objective: to determine if Nab are predictive predictive of clinical performance (health, growth, etc) in natural challenge
Naturally occurring, non-inducible, present at birth
Described in dairy, never in swine
Development work completed on archived and selected ALGP sera from batches 1-3 (Harding group, 2016)
KLH natural antibody levels by pig age showing exemplifier IgG and IgM profiles
(Merial dataset, Harding group 2016 unpublished)

20. Gilt Acclimatization Model
Activity 1.3
Gilt Acclimatization Model

21. Gilt Acclimation Project
RP, S RP
BW, S BW, S
~ 40 d
Entry
Post-acclimation Parities:
Serology data:
1) PRRS
2) PCV2
3) Influenza (SIV)
4) MH (Mycoplasma hyopneumoniae)
5-13) Actinobacillus pleuropneumoniae (app):
1, 2, 3, 5, 7, 10, 12, and 13
BW: Body Weight
S: Serum
RP: Reproductive Performance
Acute Phase Proteins
F1 Gilts i.e. “sibs” of the barrows in the Natural Challenge

22. Number of phenotypic data across serology traits and time points
Overview of the data
Phenotype Data (Serology; Total)
Genotype Data
~ 3,000 gilts were genotyped
Porcine SNP panel by Illumina (60K v1, 60K v2, and 80K)
~42K SNPs in common were used
Number of phenotypic data across serology traits and time points
Time point
app1
app2
app3
app5
app7
app10
app12
app13
PRRS MH
SIV
PCV2
Entry
2,619
2,521
Post-Acc.
2,455
2,426
Parity 1
2,193
2,135
Parity 2
1,455
1,429
app, Actinobacillus pleuropneumoniae; MH, Mycoplasma hyopneumoniae; SIV, Swine Influenza Virus

23. Gilt Acclimation
Genomic prediction accuracies ranged from low to moderate. Average accuracies were highest when using only the 269 SNPs in both QTL regions (SNPSSC7, with accuracies of 0.39 and 0.31 for outbreak and GA validation datasets, respectively.
Analysis continuing for other traits in dataset including relationship with sow lifetime productivity.

24. Ongoing and Future Analyses
ELISA data
Estimation of genetic parameters across diseases and time points
Reproduction and longevity data (once complete):
Estimation of genetic parameters
GWAS for reproduction and longevity traits
Genomic prediction of reproduction and longevity traits
Relationship of reproductive performance with S/P ratio for PRRS and other diseases
Phenotypic and genetic parameters
Genomic prediction of reproduction and longevity based on S/P ratios
Identification of CG with disease outbreak
Conduct separate analyses for these CG