Making Epidemiological Sense Out of Large Datasets of PRRS Sequences - Dr. Igor Paploski, from the 2018 Allen D. Leman Swine Conference, September 15-18, 2018, St. Paul, Minnesota, USA. More presentations at http://www.swinecast.com/2018-leman-swine-conference-material

1. Making epidemiological sense out of
large datasets of PRRS sequences
Igor Paploski / Department of Veterinary Population Medicine, UMN
Co:authors: Cesar Corzo, Albert Rovira, Michael Murtaugh, Juan Sanhueza,
Emily Smith, Kimberly VanderWaal
September 17th, 2018

2. Nelson et al., PLoS Path, 2007
• PRRS – Porcine Reproductive and Respiratory
Syndrome
• Estimated annual impact in the US swine industry:
$664 million
• Collection of PRRS sequences via MSHMP: important
tool in investigating epidemiologic patterns
– Can be used to relate occurence
of PRRS groups to systems, type of
farms, animal movement
Problem introduction
2
1. Holtkamp et al., JSHP, 2013
• Similar approaches used to
investigate the spread of
seasonal Influenza A, for
example

3. Data introduction
• 1901 PRRS sequences from systems participating
MSHMP, from 2015-17
• Info on farm, system, production type, location and
date of the sequence
• Farms tested based on animals clinical suspicion
– No data on negative farms
• Exploratory analysis - investigate genetic similarities
between sequences in different points in space and
time could provide insights for understanding PRRS
transmission
3

4. Objectives
• Stratify MSHMP sequences into genetic groups
• Describe temporally and spatially the occurrence of
PRRS genetic groups
• Evaluate if the frequency in which genetic groups
occur is similar between years, systems and
production types
• Evaluate if movement of animals contributes to the
spread of specific PRRS genetic groups
4

5. • 1901 sequences
– 747 unique farms, different production types
– 3 systems:
• A – 1,229 (64.7%)
• B – 502 (26.4%)
• C – 170 (8.9%)
– Relative contribution of systems over years was stable
5
– 3 years:
• 2015 – 770 (40.5%)
• 2016 – 643 (33.8%)
• 2017 – 488 (25.7%)
0
50
100150
countofid
2015 2016 2017
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Numberofsequences

6. Clustering sequences into genetic groups
• Removal of duplicates (different sequences with similar
nucleotides) and recombinants
• Extra sequences were obtained:
– Lelystad and VR2332 references
– 3 vaccines sequences
– 838 “anchor” sequences used to classify PRRSV into 9
different lineages1 2
6
1901 MSHMP sequences
5 prototypes or vaccines
838 anchors
518 duplicates
9 recombinants
1374 MSHMP
5 prototypes or vaccines
838 anchors
Total of 2217
sequences
1. Shi et al., JV, 2010 2. Shi et al., Virol, 2013

7. MSHMP sequences were assigned to a
lineage based on genetic distance
7
1,140 (83%) 119 (9%) 32 (2%) 5 (0%)
78 (6%)

8. 8
• 83% of MSHMP
sequences assigned to
a single lineage
• Group of sequences
with poor classification
Assignment of MSHMP sequences to a
lineage
Lineage Frequency Percent
L1 1140 83.0
L5 119 8.6
L8 32 2.3
L9 5 0.4
Poor classification 78 5.7

9. Further stratification of MSHMP sequences
to a genetic groups
• Further stratification of lineages using Cluster Picker
9
L1.A L1.B L1.C Type 1 α Type 1 β
L1 921 59 145 0 0
Poor classification 0 0 0 40 36
Lineage
Cluster Picker groups
Lineage Frequency
L1.A 1282
L1.B 65
L1.C 184
L5 204
L8 43
L9 5
Type 1 α 48
Type 1 β 42
• MSHMP sequences (including the
duplicated ones) were thus assigned
to one of 8 genetic groups

10. Further stratification of MSHMP sequences
to a genetic groups
• Relative frequency of RFLP patterns for sequences in
different lineages
10
1-7-4 2-5-2 1-4-4 1-6-4 1-3-4 1-158-1 1-18-2 1-1-1 1-3-2 Others
L1.A 64.0 . 4.1 9.1 . . . . . 22.8
L1.B . . . . . . 69.2 . . 30.8
L1.C 7.6 . 45.7 . 32.1 . . . 1.1 13.6
L5 . 81.4 . . . . . . . 18.6
L8 . . . . . . . . 62.8 37.2
L9 . . . . . . . . . 100.0
Type 1 α . . . . . 89.6 . 2.1 . 8.3
Type 1 β . . . . . 4.8 . 90.5 . 4.8
Lineage
RFLP

11. Distribution of genetic groups over space
11

12. Distribution of genetic groups over time
12

13. Distribution of genetic groups over space
and time
• Frequency of
strains is not
equal in all
years
13
2015
2015 2016 2017
L1.A 75.6 68.2 57.6
L1.B 6.6 1.7 0.8
L1.C 5.2 11.8 14.5
L5 7.7 12.7 13.5
L8 0.9 2.2 4.6
L9 0.4 0.2 0.2
Type 1 α 3.5 1.4 2.7
Type 1 β 0.1 1.7 6.2
Genetic group
Frequency (%)
Colored cells show significant difference (blue - less;
red - more) in frequency versus the previous year

14. Distribution of genetic groups over space
and time
14
• Frequency of
strains is not
equal in all
years
2016
2015 2016 2017
L1.A 75.6 68.2 57.6
L1.B 6.6 1.7 0.8
L1.C 5.2 11.8 14.5
L5 7.7 12.7 13.5
L8 0.9 2.2 4.6
L9 0.4 0.2 0.2
Type 1 α 3.5 1.4 2.7
Type 1 β 0.1 1.7 6.2
Genetic group
Frequency (%)
Colored cells show significant difference (blue - less;
red - more) in frequency versus the previous year

15. Distribution of genetic groups over space
and time
15
• Frequency of
strains is not
equal in all
years
2017
2015 2016 2017
L1.A 75.6 68.2 57.6
L1.B 6.6 1.7 0.8
L1.C 5.2 11.8 14.5
L5 7.7 12.7 13.5
L8 0.9 2.2 4.6
L9 0.4 0.2 0.2
Type 1 α 3.5 1.4 2.7
Type 1 β 0.1 1.7 6.2
Genetic group
Frequency (%)
Colored cells show significant difference (blue - less;
red - more) in frequency versus the previous year

16. Frequency of genetic groups is not equal
across systems
16
A B C
Lineage 1A 66.9 69.2 76.9
Lineage 1B 2.7 5.2 4.1
Lineage 1C 7.0 19.9 0
Lineage 5 14.3 4.6 5.3
Lineage 8 2.6 0.4 5.9
Lineage 9 0.3 0.4 0
Type 1 α 3.4 0 4.1
Type 1 β 2.9 0.2 3.6
Genetic group
Frequency (%)
Colored cells show significant difference (blue -
less; red - more) in frequency versus system A

17. Frequency of genetic groups is not equal
across production types
17
Finisher Nursery Sow
Lineage 1A 65.2 59.4 72.1
Lineage 1B 1.8 2.1 4.1
Lineage 1C 10.5 13.0 8.7
Lineage 5 17.0 19.5 6.7
Lineage 8 2.9 4.1 1.5
Lineage 9 0.4 0.3 0.3
Type 1 α 0.7 0.5 3.8
Type 1 β 1.5 1.0 2.9
Genetic group
Frequency (%)
Colored cells show significant difference (blue -
less; red - more) in frequency versus sow farms

18. 18
Movement of animals and genetic
groups occurence
• Data only for some systems
– Importance of registering animal
movement data
• Movement is not equal among all
farms in a given system – some
farms links more often
• Communities of movement within
systems
• Great number of connection links
between different farms
Network of animal
movement for systems
which data was
available

19. • We used a statistical test1 to
evaluate if the occurrence of cases
is a result of propagation of the
pathogen through network links
19
1. VanderWaal et al., 2016
Movement of animals and genetic
groups occurence
Gray: farms without PRRSV
Red: farms with L1A
Blue, farms with PRRSV
other than L1A

20. • We used a statistical test1 to
evaluate if the occurrence of cases
is a result of propagation of the
pathogen through network links
20
Movement of animals and genetic
groups occurence
Gray: farms without PRRSV
Red: farms with L1A
Blue, farms with PRRSV
other than L1A
1. VanderWaal et al., 2016

21. • We used a statistical test1 to
evaluate if the occurrence of cases
is a result of propagation of the
pathogen through network links
21
Movement of animals and genetic
groups occurence
2015 2016 2017 All years
L1A 0.000 0.000 0.000 0.000
L1B 0.001 0.000 1.000 0.000
L1C 0.108 0.002 0.000 0.000
L5 0.001 0.001 0.081 0.000
L8 1.000 1.000 0.003 0.001
L9 1.000 1.000 1.000 1.000
T1α 1.000 1.000 1.000 1.000
T1β 1.000 0.084 0.315 0.224
All Genetic groups 0.000 0.000 0.000 0.000
Genetic group
Time period
Gray: farms without PRRSV
Red: farms with L1A
Blue, farms with PRRSV
other than L1A
1. VanderWaal et al., 2016

22. • We used a statistical test1 to
evaluate if the occurrence of cases
is a result of propagation of the
pathogen through network links
22
Movement of animals and genetic
groups occurence
• Movement of animals is associated
with occurence of certain PRRSV
genetic groups
• Similar patterns when data is
stratified by year
• Data available only for some systems
Gray: farms without PRRSV
Red: farms with L1A
Blue, farms with PRRSV
other than L1A
1. VanderWaal et al., 2016

23. 23
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

24. 24
Lelystad
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

25. 25
Lelystad
VR2332
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

26. 26
Lelystad
VR2332
Vaccine 1
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

27. 27
Lelystad
VR2332
Vaccine 2
Vaccine 1
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

28. 28
Lelystad
VR2332
Vaccine 2
Vaccine 1
Vaccine 3
PRRSV vaccines are not from the
same genetic group as the most
prevalent sequences

29. Limitations
• Convenience sample
– Reasons for sample collection not always clear
– Sample composition not clear (single animal, pool of
animals)
– Lack of a denominator – we only have information on farms
that are positive for sequencing
• Data on animal movement is not available for all
systems
• Analysis restricted to a fraction of the MSHMP data
– Restricted systems
– Restricted length of time series

30. Take home messages
• Prevalence of PRRSV genetic groups is not equal in
different:
–Years
–Systems
–Production types
• Occurence of PRRSV genetic groups is associated
with movement of animals
–Recording this data is important, specially for forecasting
the occurence of the disease
• PRRSV vaccines are not from the same genetic
group as the most prevalent sequences
30

31. Acknowledgements
• Faculty: Kimberly VanderWaal,
Cesar Corzo, Montse
Torremorrell, Andres Perez
• Other collaborators: Albert
Rovira, Michael Murtaugh,
Emily Smith
• MSHMP team
– Juan Sanhueza
– Carles Vilalta
– Emily Geary
– Paulo Fioravante