Presentation I gave at EuroVis 2011 on the VIPER project

1. VISUALISING ERRORS IN
ANIMAL PEDIGREE
GENOTYPE DATA
Martin Graham, Jessie Kennedy, Trevor Paterson & Andy
Law
Edinburgh Napier University & The Roslin Institute, Univ of
Edinburgh, UK

2. Pedigrees
 Animal pedigrees are their family trees – who’s
whose father, mother etc
 In animal breeding these pedigrees are strictly
controlled to maximise traits of value or
suppress unwanted ones

3. Pedigree Genotypes
 A genotype is the genetic make-up of an
animal
Example
 Pedigree + genotype = pedigree genotype
Individual
Marker Values
M1 C|T
M2 A|A
M3 A|G
... ...
 Not the whole genotype, use sets of markers
 Marker type: SNP (Single Nucleotide
Polymorphism)

4. But...
 However, most large datasets have errors
 Errors when recording pedigree
 Technical errors e.g. wrongly detected marker
 Misassigned samples
 Also incomplete data
 These errors make the data genetically
inconsistent
 Thismakes them unusable for most downstream
analyses

5. Example
Mum Dad
?
A|A G || G
G G
C ?
Junior
A| C
C
 Various possibilities here
 Dad is Juniors’ father but the genotyping is
incorrect
 Dad isn’t Junior’s father and the genotypes are
correct
 Need to find/isolate/clean such data

6. Table Viewer
 Current table-based viewer
 Grid of markers x individuals; genotype values in
cells
 Universally ‘bad’ markers or individuals stand out

7. Table Viewer
 Expert biologists are needed to pinpoint the
source of reported errors
 But without a pedigree context to anchor the
errors in, it’s impossible to do this

8. Previous Work
 Multitude of pedigree viewers, but all have
issues with scalability or handling extra
(genotype) data

9. Voyage of Discovery
 Mainly discovering representations that didn’t
work
 Iterated through a number of different
representation styles that failed for various
reasons

10. Node-Link View
 Can see that the pedigree clusters around a few
males
 But hard to follow edges/directions, loss of
generational context

11. Hierarchical Node-Link View
 Regain visual generation structure of pedigree
 But plagued with more edge crossings than
before

12. Matrix View
 Matrices are the main alternative to drawing node-
link diagrams for relational information
 We rejected having one overall matrix due to
sparsity

13. Matrix View
 One matrix per generation ‘gap’ (parent 
offspring)
 Rather than sources v sinks - sires v dams; offspring
in cells

14. Sandwich View
 Realised that in these matrices, either the rows
or columns will only have one filled cell each if
one of the parent genders is monogamous
 In animal experiments this tends to be the
case, a female breeds with only one male per
generation
 Each matrix can thus be replaced with a
compressed view

15. Sandwich View
 The sandwich view is a specialised view of the
bipartite graph between two generations
 With
the top layer split into males/females and the
females pushed beneath the bottom layer
Parents Sires
Offsprin Offsprin
g g
Dams
Connectors to repeated
node representations if
necessary

16. Sandwich View
 Sandwich view of the relationships between
two adjacent generations
Sires (Male Parents)
Offspring
Dams (Female Parents)
1 male has children
with multiple females
 All the other pedigree views of full generations
involved tracing paths between
parents/offspring

17. Sandwich View

18. Error Information
 Colour is used to convey an individual’s error
status over all the markers in a data set
 More errors = higher saturation
 Parent – coloured by overall error count
 Offspring drawn as hexagonal glyphs
 ‘Up’ triangle – incompatibilities with sire
 ‘Down’ triangle – incompatibilities with dam
 Middle portion – markers exist that are not present
in either parent

19. Error Information
 Aggregating offspring
 Groups of siblings who share the same
parents can be aggregated under one glyph
 Colouringnow represents errors in all markers
over a group of individuals
 Troublesome families & parents can be clearly

20. Filtering
 Error Filtering
 The table view ( ) clearly showed
rogue markers and individuals, and these can be
filtered by a user in that application
 To the sandwich view we add two complementary
histograms that perform the same purpose

21. Filtering
 Error Filtering
 Each histogram shows number of errors along the X
axis
 Number of individuals/markers with that number of
errors on the Y axis
 Typical pattern: A few individuals / markers have lots
of errors, and the majority have a few or no errors
 Mantra is to discard bad markers and look at bad
individuals

22. Sandwich view
 Pic/Vid of full view (To Do)

23. Video

24. Conclusion
 Developed new style of pedigree visualisation
 Shows detailed errors at a family level
 Shows overview of errors in an entire pedigree
 Keeps offspring close to their parents for family-
centric view

25. Future Work
 Single marker views of errors
 Making the sandwich into a club sandwich
 Split the middle layer into multiple layers
 i.e. By gender to spot sex-related marker errors

26. Acknowledgements
 Reviewers
 BBSRC funded project