This presentation summarizes methods for detecting identity by descent (IBD) tracts from sequence data and compares the performance of existing algorithms. It introduces IBD and current population-based methods like GERMLINE, FastIBD, and RefinedIBD. A coalescence-based method called SMCSD is described that uses hidden Markov models and can infer shorter IBD tracts. Simulation results on European and African populations show that while existing methods perform well for long tracts, SMCSD has higher recall, precision, and F-score for shorter tracts due to its probabilistic modeling of recombination breakpoints.

1. Introduction Methods Results Conclusions
Sequence Based Identity by Descent Detection
Joint work with Jasmine Nirody & Yun S. Song
@ University of California, Berkeley
Paula Tataru
Mols Meeting
August 15, 2013
Sequence Based IBD detection 1

2. Introduction Methods Results Conclusions
Sequence Based IBD detection 2

3. Introduction Methods Results Conclusions
G
G
A
A
C
C
T
T
G
G
A
A
G
A
C
C
Identity By Descent (IBD) tracts
DNA segments that are inherited from a common ancestor
recombination disrupts them
expected length depends on the TMRCA
Sequence Based IBD detection 3

4. Introduction Methods Results Conclusions
G
G
A
A
C
C
T
T
G
G
A
A
G
A
C
C
IBD is fundamental in genetics
selection
phasing
imputation
association studies
Sequence Based IBD detection 4

5. Introduction Methods Results Conclusions
G
G
A
A
C
C
T
T
G
G
A
A
G
A
C
C
Current methods use population-wide SNP genotype data
work best for recent IBD (longer than 1cM)
different IBD definitions
pairwise SNPs disrupt predicted IBD tracts
probabilistic, deterministic
Sequence Based IBD detection 5

6. Introduction Methods Results Conclusions
GERMLINE
Gusev et al., 2009
Identical By State (IBS)
Deterministic
Linear in number of samples
Phased SNP data
Sliding window to find IBS
Allows for genotyping error
Sequence Based IBD detection 6

7. Introduction Methods Results Conclusions
FastIBD
Browning & Browning, 2011
IBD inside IBS
Deterministic
Quadratic in number of samples
Unphased SNP data; phasing done with Beagle
Accounts for phase uncertainty and background levels of LD
Models shared haplotype frequencies
Sequence Based IBD detection 7

8. Introduction Methods Results Conclusions
RefinedIBD
Browning & Browning, 2013
IBD inside IBS
Probabilistic
Quadratic in number of samples
Very similar to FastIBD
Identifies candidate IBD segments using GERMLINE
Filter candidates based on a probabilistic model
Sequence Based IBD detection 8

9. Introduction Methods Results Conclusions
SMCSD
Paul et al., 2011, Sheehan et al., 2013
same TMRCA
Probabilistic: HMM
Quadratic in number of samples
Phased sequence data
Based on coalescence theory
Predicts recombination breakpoints that change TMRCA
Sequence Based IBD detection 9

10. Introduction Methods Results Conclusions
SMCSD in a nutshell
Designed to estimate demographic history
partition time in discrete intervals
assume constant population size per time interval
use EM to train model
Sequence Based IBD detection 10

11. Introduction Methods Results Conclusions
SMCSD in a nutshell
Designed to estimate demographic history
partition time in discrete intervals
assume constant population size per time interval
use EM to train model
Use decoding to infer IBD
assume demography given
run posterior decoding
changes of TMRCA reveal recombination breakpoints
use posterior probabilities to trim tracts’ endpoints
Sequence Based IBD detection 10

12. Introduction Methods Results Conclusions
Data simulation
Simulate trees in ms
µ = 1.25 × 10−8
r = 10−8
sequences of length 10MB
10 sequences (45 pairs)
10 replicates
Collect recombination breakpoints from ms output
Reconstruct pairwise IBD tracts
Sequence Based IBD detection 11

13. Introduction Methods Results Conclusions
Human Population
Tenessen et al., 2012, Simons et al., 2013
Sequence Based IBD detection 12

14. Introduction Methods Results Conclusions
Human Population
0.
0.0
0.5
1.0
CumProb
0 1000 2000 3000 4000 5000 6000
Generations back in time
103
104
105
106
PopSize
EA EA Watt A
Sequence Based IBD detection 13

15. Introduction Methods Results Conclusions
European Population
Recall Precision F-score0.0
0.5
1.0
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
TruePositive
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
FalseNegative
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
FalsePositive
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
Power
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
Under-prediction
0.1 0.55 1.0
Tract length (cM)
0
0.5
1.0
Over-prediction
0 1000 2000 3000 4000 5000 6000
Generations back in time
103
104
105
106
PopSize
GERMLINE FastIBD RefinedIBD SMCSD-W SMCSD-T
Sequence Based IBD detection 14

16. Introduction Methods Results Conclusions
African Population
Recall Precision F-score0.0
0.5
1.0
0.1 0.6 1.1
Tract length (cM)
0
0.5
1.0
TruePositive
0.1 0.6 1.1
Tract length (cM)
0
0.5
1.0
FalseNegative
0.1 0.65 1.2
Tract length (cM)
0
0.5
1.0
FalsePositive
0.1 0.6 1.1
Tract length (cM)
0
0.5
1.0
Power
0.1 0.6 1.1
Tract length (cM)
0
0.5
1.0
Under-prediction
0.1 0.6 1.1
Tract length (cM)
0
0.5
1.0
Over-prediction
0 1000 2000 3000 4000 5000 6000
Generations back in time
103
104
105
106
PopSize
GERMLINE FastIBD RefinedIBD SMCSD-W SMCSD-T
Sequence Based IBD detection 15

17. Introduction Methods Results Conclusions
Conclusion
Simulated data from outbred populations
Existing programs are strong performers for long tracts
SMCSD performs better on shorter tracts
SMCSD uses a more robust IBD definition
Sequence Based IBD detection 16

18. Introduction Methods Results Conclusions
Thank you!
Sequence Based IBD detection 17