Tempero-Spatial Sequence Analysis, Bayesian Phylodynamic Methods on 1-7-4 PRRSv - Dr. Andres Perez, from the 2015 Allen D. Leman Swine Conference, September 19-22, 2015, St. Paul, Minnesota, USA. More presentations at http://www.swinecast.com/2015-leman-swine-conference-material

1. Applications of Bayesian phylodynamic methods to the
investigation of the recent 1-7-4 PRRSv outbreak
Andres Perez, DVM, PhD
Endowed Chair of Global Animal Health and Food Safety
College of Veterinary Medicine
University of Minnesota
aperez@umn.edu

2. Swine Group, University of Minnesota
Moh Alkhamis
Andres Perez
Mike Murtaugh
Xiong Wang
Bob Morrison
Team work
Swine Health Monitoring Program
Four anonymous participants

3. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Introduction
Suppose that we have sequences from 8 farms (1 per farm)
Each square represents one nucleotide of the sequence

4. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Introduction
Alignment

5. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Matrix of (nt) differences
1 2 3 4 5 6 7
2 2
3 1 2
4 5 4 4
5 2 1 1 3
6 4 3 3 2 2
7 3 1 3 4 2 4
8 6 5 5 1 5 3 5

6. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
(nt) Genetic distance
1 2 3 4 5 6 7
2 0.333
3 0.167 0.333
4 0.833 0.667 0.667
5 0.333 0.167 0.167
6 0.667 0.5 0.5 0.333 0.333
7 0.5 0.167 0.5 0.667 0.333 0.667
8 1 0.833 0.833 0.167 0.833 0.5 0.833
Genetic distance (GD)
GD = (nt, aa) differences / (nt, aa) compared
Probability of finding one (nt, aa) difference when two strains ar

7. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
1) Because sequences theoretically split into two descendant
sequences, phylogenetic trees are typically assumed to be
bifurcating
2) Topology: branching pattern of a tree
3) Taxa: any kind of taxonomic units (eg.: virus sequences)
Unrooted tree Rooted tree

8. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
If 4 taxa are being compared, there are 15 and 3
possible rooted and unrooted tree topologies,
respectively
From Nei and Kumar, 2000

9. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Phylogenetic trees
When 10 taxa are being compared, the number of possible
rooted topologies is 34,459,425 and the number of possible
bifurcating unrooted topologies is 2,027,025
Only one of them is the true topology
Reconstructed or inferred trees: trees built from observed
sequences
Traditional methods to infer a tree (distance, parsimony,
likelihood) ignore the associated epidemiological information
(space, time of identification, associated data)

10. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Bayesian phylogenetic methods
http://www.nbcnews.com/science/such-
deep-roots-you-have-how-little-red-riding-
hoods-2D11591047
http://www.plosone.org/article/in
fo%3Adoi%2F10.1371%2Fjourn
al.pone.0078871

11. Paton et al. 2005. Selection of foot and mouth disease vaccine strains- a rev
Bayesian phylogenetic methods

12. Correlation of alternative metrics of PRRSv relatedness, namely, difference (%) in
the number of nucleotides (X axis) and considering phylogenetic evolution (Y axis).
Correlation is quite bad

13. Phylogenetic methods
• Similarly, we can use phylogenetic methods
to infer:
– Most likely time of emergence of the strain;
– Past probability of spread between systems and
between type of farms
– Population (viral) size

14. Data
• 6,774 ORF 5 sequences
• January 1998 – April 2015
• 5 independent systems (coded A…E)
• Type of farm (sow, growing pig farms)
• Selected 1-7-4 PRRSv using a maximum
likelihood algorithm

15. Results
Some (n= 288) related
sequences (based on
ML) collected between
September 2003-March
2015
Most (n=241) are 1-7-4
sequences obtained
between Jan 2014 and
Mar 2015

16. Results
Increase in
population size in
2014

17. Results
History of spread
between systems

18. Results
History of spread
between types of
farms

19. Dynamic representation

20. Future plans
• Continue enrollment of systems
• Standardization and optimization of protocols
for data collection and sharing
• Implementation of analytical tools into IT
system designated by the SHIC
• Provide near real time interpretation of results
• Contribute to PRRS control at a national
scale

21. Thank you