GridQTL: High performance QTL analysis via the Grid

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GridQTL High performance QTL analysis via the
Grid

• University of Edinburgh
• Roslin Institute

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Overview

• People
• Science / biology
• Objectives
• e-Science Why the Grid?
• Management
• Summary

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People
University of Edinburgh University of Edinburgh Roslin Institute
Institute of Evolutionary Biology NeSC
Sara Knott Dave Berry Chris Haley
Peter Visscher Denise Ecklund Dirk-Jan De Koning
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Rationale for QTL analysis

• QTL quantitative trait locus
• Biology Understanding genetic variation by
  dissecting complex traits
• basic biology
• applications in agriculture
• applications in medicine

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Dissection of Complex Traits
GridQTL
Genetics
Chromosome Region
Association Study
Sib pairs
Genomics
Candidate Gene Selection/ Polymorphism Detection
Mutation Characterization/ Functional Annotation
Physical Mapping/ Sequencing
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QTL Express User-friendly web-based software to
map QTL in outbred populations

• George Seaton, Sara Knott, Chris Haley, Peter
  Visscher

http//QTL.cap.ed.ac.uk/
1 Roslin Institute 2 University
of Edinburgh 3
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Example Obesity in pigs
Position (cM)
Knott et al., 1998
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Present and future paradigms

• Now Future
• individuals 100s 1000s
• phenotypes 10s 10000s
• markers 100s 100000s
• analyses 100s O(106 108)
• models simple complex
• data sources homogeneous heterogeneous
• Present statistical algorithms and computer
  platforms will be inadequate for future analysis

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400 individuals, 9000 phenotypes, 3000 markers
Nature. 2004 430743-7.
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Objectives

• To develop apply a grid-based platform
• for robust and fast multiple trait mapping
• of multiple quantitative trait loci
• in simple and complex pedigrees
• and disseminate the results

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Objectives Grid implementation

• Transform existing QTL Expressto be grid
  compatible
• Deliver an essential analysis componentfor
  integrative biology workflow
• Integrate new analytical approachesand grid
  components to deploy GridQTL

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Objectives QTL analysis

• Robust multiple trait algorithms
• Expression QTL
• Methods and algorithms for gene-gene interactions
• Combining gene-phenotype associations within and
  between families

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Objectives dissemination

• Dissemination of the developed grid applications
  and QTL mapping algorithms
• The existing QTL Express user-base
• (Inter)national postgraduate courses
• Scientific publications
• e-Science meetings
• Websites and internet postings

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Why the Grid?

• Grid helps achieve our key goals to
• Scale-up analysis complexity
• Analyse more individuals, phenotypes and markers
• Provide a growing public service to the
  research community
• Provide a component for integrative biology
• Make QTL analysis services available in a larger
  workflow
• Using the grid we can leverage
• Essential computation and data storage resources
• Existing middleware to manage these resources

But we need to build on top of the middlewareto
get what we needto effectively support
multi-trait analysis
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GridQTL Portal The Challenges

• Execute QTL analyses on grid computing resources
• Describe parallel computation requirements
• Automatic task-level decomposition of analysis
  requests
• Schedule, monitor and re-start decomposed tasks
• Provide a secure and private data space for each
  researcher
• Synchronise application input and output
• Enable analysis re-start from intermediate
  results
• Be a robust public service

GridQTL Portal
Data Mgr
Analysis 1
AnalysisPortlet
Analysis 2
Analysis 3
Meta Sched
Analysis 4
Analysis 5
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Management

• Research partners
• science partner coordination
• Knott, De Koning, Haley, Visscher
• science e-science coordination
• All PI
• Seaton and NeSC software engineers
• User group
• current QTL Express users
• Scientific Advisory Board
• science and e-science academics

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Summary

• GridQTL provides an essential core component of a
  future integrated system incorporating genetic,
  phenotypic, transcription and comparative
  information to allow prediction from sequence to
  consequence