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

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

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)

Raw Reads

Reference

ISMU V1.0

Assemble & Align Raw Reads Mine SNPs Generate Marker Matrix Visualize in 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

ISMU 1.0 Standalone Edition Selection of Alignment Tool & SNP Approach

ISMU 1.0 Standalone Edition Results

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]GAAAAGTAAGGGACTAGAAGGGCAGAGTGGA72,0,0,0,Forward,Plus TC00075_852,SNP,TTGTCGACATTGAGATGTTCCTATCACCAATGCAAATATCAGGGCAAATGCACTAACATA[C/T]TTGAGTAAATTTCCCATCTTCATTTGCACAAA,0,0,0,Forward,Plus TC00118_13765,SNP,ATCTAAAAATAATTAAGTTAGTAATGACTGGACGAAACCAAGAAATAACTACTTACGTGC[T/G]AAATTATAGAAGGTCTCCTGTAAGATCCAA3765,0,0,0,Forward,Plus TC00130_2668,SNP,TGCGGTCATTGTTGTTGATCGAAAGAAAATTTAATTTCTTGTTCGACTGATCACCTTGCT[G/A]GGTTCCAACTATTCTAAAGTAATACAGGCAT68,0,0,0,Forward,Plus

KASPar

ILLUMINA

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

?

Breeding Cycle

Crossing

Field evaluation Line Selection

yirR A

tσ

=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

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

GS

ISMU V2 Raw Reads

Reference

Assemble & Align Raw Reads Mine SNPs Generate Marker Matrix Visualize in 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

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

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

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

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

• 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

ISMU 2.0

ISMU 2.0

Browse Data

Data in ISMU2.0

Calculation of Marker Summary

Summary Plots

Various Statistics

Export to MS-Excel (Windows)

GS Methods

GS Methods

GS Results

GS Results

Export to PDF

Export to High Quality Graphics 300DPI

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

Acknowledgements

Many Friends & Collaborators

Thanks…

Thanks…