Download Genotype Error Detection using Hiddend Markov Models of

Computational Challenges in
Whole-Genome Association Studies
Ion Mandoiu
Computer Science and Engineering Department
University of Connecticut
Approaches to Disease Gene Mapping
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Linkage analysis
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Association analysis
Cases
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LOD:=log10(L()/L(1/2))
Very successful for Mendelian
diseases (cystic fibrosis,
Huntington’s,…)
Low power to detect genes
with small relative risk in
complex diseases
[RischMerikangas’96]
Controls
2-test
Genome-wide scans made
possible by recent
progress in SNP
genotyping technologies
Computational Challenges
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Detecting genotyping errors
Imputation of missing genotypes
Imputation of untyped genotypes based on
reference population (e.g., Hapmap)
Haplotype inference and haplotype-based
association tests
Modeling gene-gene interactions
Handling structural variation data provided by
new sequencing technologies
Optimal multi-stage study design
3
Genotype Error Detection
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A real problem despite advances in technology
In [KMP07] we proposed efficient methods for error detection
in trio data based on LLR approach combined with an HMM
model of haplotype diversity
Parents-COMBINED
NO_ERR
ERR
1
1000000
0.9
100000
#FP=#FN line
0.8
10000
1000
0.7
TotalProb-TRIO
10
5.94
5.4
5.67
5.13
4.86
4.59
4.32
4.05
3.78
3.51
3.24
2.7
2.97
2.43
2.16
1.89
1.62
1.35
1.08
0.81
0.54
0
0.27
1
Children-COMBINED
NO_ERR
Sensitivity
100
0.6
0.5
TotalProbCOMBINED
0.4
0.3
ERR
1000000
0.2
100000
0.1
FAMHAP
10000
0
1000
0
100
0.005
0.01
0.015
FP rate
10
5.88
5.6
5.32
5.04
4.76
4.48
4.2
3.92
3.64
3.36
3.08
2.8
2.52
2.24
1.96
1.68
1.4
1.12
0.84
0.56
0

0.28
1
In ongoing work we seek to improve error detection accuracy
by using low-level data such as typing confidence scores
Genotype Imputation
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Current genotyping platforms cover <1 mil. SNPs of ~10mil.
SNPs  causal variant unlikely to be assayed directly
Untyped SNPs can be imputed based on linkage disequilibrium
info inferred from high-density datasets such as Hapmap
Maximum likelihood approach:
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probabilities computed using HMM
Allele frequency, typed genotypes
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Allele frequency, imputed genotypes
Acknowledgements & Advertisment
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
Justin Kennedy, Bogdan Pasaniuc
NSF funding (Awards 0546457 and 0543365)
DIMACS Workshop on Computational Issues in Genetic Epidemiology
August 21 - 22, 2008
DIMACS Center, CoRE Building, Rutgers University
Presented under the auspices of the DIMACS/BioMaPS/MB Center Special Focus
on Information Processing in Biology.
Organizers:
Andrew Scott Allen, Duke University, Ion Mandoiu, University of Connecticut
Dan Nicolae, University of Chicago, Yi Pan, Georgia State University, Alex
Zelikovsky, Georgia State University