1. Harnessing the Power of Condor for Human Genetics
Bret A. Payseur
Laboratory of Genetics
University of Wisconsin

2. Our research: evolutionary genetics
Analysis of DNA variation across human populations to understand:
Roles of different evolutionary forces
Prospects for finding genes that cause disease
Analysis of crosses between mouse strains to understand:
How anatomy evolves
How new species arise

3. Our computational needs
Multi-dimensional statistical inference: we measure many different (partially correlated) features of DNA variation
Genome-scale analyses: we measure variation at thousands to millions of sites
Replicates: we conduct population simulations to measure stochastic effects

4. Haplotype phasing
Each human has two copies of each site on a chromosome (one from each parent) A T G C
Site Site 2

5. We want to know which variant goes with which on the chromosome
Haplotype phasing
We want to know which variant goes with which on the chromosome A T G C
Site Site 2

6. Haplotype phasing
Genotyping technology cannot distinguish between these two possibilities in individuals that vary at both sites A T T A G C G C
Configuration 1 Configuration 2

7. Solution: PHASE algorithm
Uses Markov Chain Monte Carlo (MCMC) sampling scheme
Uses coalescent simulations based on population genetic principles
Identifies haplotypes for each individual with statistical uncertainty (posterior probability)
State of the art method in human genetics

8. Scope of problem
Goal: reconstruct phase in a human dataset of genomic proportions
Dataset is large
720 regions of the genome
100 variable sites per region
3 populations
60 individuals per population
Computational approach is intensive

9. 720 regions x 3 populations x 8 hours =
Scope of problem
Average run time
8 hours
720 regions x 3 populations x 8 hours =
17,280 hours

10. 5 Payseur lab computers:
Scope of problem
Running full time on
5 Payseur lab computers:
144 days!

11. ENTER CONDOR

12. Approach
Create submit file for each job – automated using perl script
Submit each job – automated using a perl script

13. CONDOR submit file universe = standard executable = PHASE
error = phase.err
log = phase.log
should_transfer_files = YES
when_to_transfer_output = ON_EXIT
transfer_input_files = phase.in
transfer_output_files = phase.out
Requirements = ((OpSys == "LINUX") && ((Arch == "INTEL") || (Arch == "X86_64")))
Arguments = -MR -P1 phase.in phase.out
queue

14. Running on vanilla universe
Huge increase in efficiency
Challenge
Run times often exceeded allocated CPU time
Many jobs did not finish

15. CONDOR solution
Use condor_compile on the standard universe to allow checkpointing
Expand machine pool to include X86_64/LINUX and INTEL/LINUX nodes

16. Result Vanilla universe Standard universe Jobs finished Required time
500
2 months
720
10 days

17. We have also used CONDOR to…
Simulate genetic mapping of complex diseases in mice (Payseur and Place 2007; Genetics)
Infer relationships among mouse strains used in biomedical research

18. We hope to use CONDOR for…
EVERYTHING

19. Acknowledgments
Miron Livny
Zach Miller
David Schwartz