- ISMU 1.0 and 2.0 are pipelines for genome-wide selection that allow for automated SNP detection, genotyping, and genomic selection in plant breeding. - ISMU 1.0 featured a graphical user interface and automated data cleaning. ISMU 2.0 adds capabilities for genomic selection like multiple GS methods, cross validation, and output in HTML and PDF. - Factors affecting genomic selection models include marker density, population size, trait heritability, and relationship between training and selection populations. Validation studies are needed to select the best model.

1. Rajeev K. Varshney and Abhishek Rathore
Email: r.k.varshney@cgiar.org
a.rathore@cgiar.org
Genome-Wide Selection Update
GCP General Research Meeting
Session IX
30th September 2013

2. ISMU 1.0
Challenges in SNP Detection
• Mostly Command-line based
Linux Tools
• Multiple steps involved
• Difficult pre-processing
& cleaning of raw data
• Specialized skills required to
process the job
• Developing genotyping assays
(GoldenGate and KASPar)
• Very few user-friendly
software

3. Solution: ISMU 1.0 Pipeline
Features:
– Multicore Architecture
– One stop shop for SNP detection
– Graphical User Interface
– Automated Cleaning of Data
– Integration of various popular alignment
tools
– Customized operation of tools for advanced users
– Available in Online and Standalone versions
– Easy Installation
– Works on CentOS, RHEL & Fedora
– Visualization of SNP and Alignment (TABLE/FLAPJACK)

4. Raw Reads
Reference
ISMU V1.0
Assemble& Align
Raw Reads
Mine SNPs
Generate Marker
Matrix
Visualizein TABLET
and FLAPJACK
Export in FLAT Files
• Assemble & Align Raw
Reads
• Mine SNPs
• Generate Marker Matrix
• Automated Visualize in
TABLET and FLAPJACK
• Developing genotyping
assays
• Export in FLAT Files
ISMU V1.0

5. ISMU 1.0 Standalone Edition
Selection of Alignment Tool & SNP Approach

6. ISMU 1.0 Standalone Edition
Results

7. Locus Forward Polymorphism Reverse
TC00001_1272 CGCTCAAGAGAACCAGTGTTGGAATGGTGGCGGCGATGGCTGTATTTCCA A/T GAAAAGTAAGGGACTAGAAG
TC00075_852 T GAGATGTTCCTATCACCAATGCAAATATCAGGGCAAATGCACTAACATA C/T TTGAGTAAATTTCCCATCTT
TC00118_13765 AATTAAGTTAGTAATGACTGGACGAAACCAAGAAATAACTACTTACGTGC T/G AAATTATAGAAGGTCTCCTG
TC00130_2668 GTTGTTGATCGAAAGAAAATTTAATTTCTTGTTCGACTGATCACCTTGCT G/A GGTTCCAACTATTCTAAAGT
TC00191_3430 TTAATGAATTTGCTTCATCGTCCAAGGTTTACCATTTAGGTGGGTAGAGC T/C ACAGAAATTAAGTATCTGGT
TC00212_866 CCCATGTCAATCATCCCAATTTTCTTGCATAAATTATCCTTAAATGGATA G/T CTTTACGTATGATGCTGATC
TC00295_2234 AGCCAGTGGAAGCTCCACCAGCAGCAGTAGCAGAAGTTCCAATTGAGACT C/T CTGAAGCTTAGACCAATGGA
TC00329_2112 GAGGCGTGAAAAGAAAAAGGCAAAGGAGGAGAGGGAGAAGCAAATAAGGG A/C TGCTGAGGAAAGACTACTGG
TC00336_3122 CTGAAATGGAGTGTTTTTATACAAGTTGTAAATAGTGATGTTTTGTACAT C/T TTTCTGGAAGATGATTCATG
[HEADING]
Customer_Name
Company_Name
Email_Address
Platform_Type GGGT
Format_Type Gene; Region; Sequence; Identity; ExistingDesign; or Score [select one]
Design_iteration prelim
Species
Number_of_Assays
[DATA]
Locus_name,Target_Type,Sequence,Chromosome,Coordinate,Genome_Build_Version,Source,Source_Version,Sequence_Orienta
TC00001_1272,SNP,TACTTCATCCCGCTCAAGAGAACCAGTGTTGGAATGGTGGCGGCGATGGCTGTATTTCCA[A/T]GAAAAGTAAGGGACTAGAAGGGCAGAGTGGA
72,0,0,0,Forward,Plus
TC00075_852,SNP,TTGTCGACATTGAGATGTTCCTATCACCAATGCAAATATCAGGGCAAATGCACTAACATA[C/T]TTGAGTAAATTTCCCATCTTCATTTGCACAAA
,0,0,0,Forward,Plus
TC00118_13765,SNP,ATCTAAAAATAATTAAGTTAGTAATGACTGGACGAAACCAAGAAATAACTACTTACGTGC[T/G]AAATTATAGAAGGTCTCCTGTAAGATCCAA
3765,0,0,0,Forward,Plus
TC00130_2668,SNP,TGCGGTCATTGTTGTTGATCGAAAGAAAATTTAATTTCTTGTTCGACTGATCACCTTGCT[G/A]GGTTCCAACTATTCTAAAGTAATACAGGCAT
68,0,0,0,Forward,Plus
KASPar
ILLUMINA

8.  MABC, MARS and GS
approaches seem to most
promising for crop
improvement
 Need to have genomic
resources and cost-
effective genotyping
platforms
 Breeders-friendly pipelines
and decision support tools
required for prediction of
phenotype
Novel breeding approaches for
developing countries
MBDT
MBDT
OptiMAS
GS
?

9. Breeding Cycle
Crossing
Field evaluation Line Selection
y
ir
R A
t
σ
=
genetic gain over time
years per cycle
selection intensity selection accuracy
genetic variance
NEW
cheaper to
genotype = larger
populations for
same $$
make selections in
‘off target’ years
maintain favorable
rare alleles
Select years
earlier on single
plant basis
Inbreeding
Multi-location, Multi-year
testing
Seed Increase
Based on discussions
with several colleagues
e.g. Jesse Poland,
J-L Jannink, Gary Atlin

10. GS-Models
• Usually involves relatively high
number of markers
• To meet the challenges,
statistical methods that can
handle high-dimensional data
have been developed
• However, their respective properties are
still not fully understood,
• Causing considerable uncertainty about
the choice of models for genomic
prediction
• Factors affecting GS are also not very clear

11. GS
ISMU V2
Raw Reads
Reference
Assemble& Align
Raw Reads
Mine SNPs
Generate Marker
Matrix
Visualizein TABLET
and FLAPJACK
Export in FLAT Files
GDMS
Genotypic Matrix
& QTLs
Lines selected
for further
crossing in
GS
External
Genotyping
Platforms
Called SNPs
ISMU V2.0

12. GS-Models
• To meet the challenges, statistical methods
that can handle high-dimensional data have
been developed
• However, their respective properties are
still not fully understood
• Causing considerable uncertainty about
the choice of models for genomic
prediction
• Factors affecting GS are also not very clear

13. Factors Affecting GS-Models
• Marker density, genome size and
structure
• Size of the training population
• Historical effective population size
• Trait heritability
• Relationship between training
population & selection candidates
• Number of genes and distribution of
their effects
• Method used for the estimation of
marker effects
• GxE

14. Validation Studies
• Fit available models
• Cross Validation
• Prepare a matrix of validation scores
• Compare over the multiple environments
• Select Final model
Training set Testing set
Cross Validation K(=5) - fold cross-validation

15. ISMU 2.0 Pipeline
Analysis Capabilities to ISMU 1.0
• GUI for Genomic Selection
• Multicore Support
• R and Fortran Libraries for GS
• Project Mode Development
• IDE Supports
• Multiple Method & Traits at once
• Platform Support
– Windows x64 and x32
– CentOS x64 and Ubuntu x64
– MAC (Under Testing…)
In collaboration with
J L Jannink, John Hickey and Aaron Lorenz

16. • Data Diagnostics
– Graphical Summary
– Tabular Summary
• Subset Data
– Missing %
– MAF
– PIC
• Genomic Selection
– RR-BLUP
– Kinship Gauss
– Bayesian LASSO
– BayesB and BayesCπ
– Random Forest Regression (RFR)
• HTML & PDF Output
ISMU 2.0 Pipeline
Analysis Capabilities to ISMU 2.0

17. ISMU 2.0

18. ISMU 2.0

19. Browse Data

20. Data in ISMU2.0

21. Calculation of Marker Summary

22. Summary Plots

23. Various Statistics

24. Export to MS-Excel (Windows)

25. GS Methods

26. GS Methods

27. GS Results

28. GS Results

29. Export to PDF

30. Export to High Quality Graphics 300DPI

31. Future Plans
• Customized Parameters for GS Scripts
• Integrating more Algorithms
• Implementation of Cross Validation
• Linking with IBWS
• Data Import/Export Module
• Online Version of ISMU 2.0
• Linking with Agricultural Genomics Network
• Making available on more OS
• Average GEBVs
• Multi-trait GS
• Capacity building in NARS Partners
– 4th International Workshop on Next Generation
Genomics and Integrated Breeding for Crop
Improvement, Feb 19th -21st 2014

32. Acknowledgements
Many Friends & Collaborators

33. Thanks…

34. Thanks…