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Inferring Missing Genotypes in Large SNP Panels
Adam Roberts, Leonard McMillan, Wei Wang, Joel Parker, Ivan Rusyn, and David Threadgill University of North Carolina at Chapel Hill, USA
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Motivation and Overview
High-throughput genotyping techniques yield many missing calls We have developed fast algorithms for inferring missing genotypes Tested on isogenic animals (recombinant-inbred lines) where phasing is not a confounding issue Our method delivers accuracy competitive to the best imputation algorithms but only costs a few s per imputation.
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Mouse SNP Data SNP Strain A (A/J) Strain B (BALB) Strain C (B6)
Strain D (C3) Strain E (DBA) 1.2830 C T G A Y
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Mouse SNP Data SNP Strain A (A/J) Strain B (BALB) Strain C (B6)
Strain D (C3) Strain E (DBA) 1.2830 1 Y
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Realistic SNP Data Typical genotyping technologies give “no-calls” for approximately 5%-10% of a SNP dataset Strains A B C D E . 1 SNPs
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Realistic SNP Data Typical genotyping technologies give “no-calls” for approximately 5%-10% of a SNP dataset Four options: Modify tools to accommodate missing data Throw away SNPs Resequence Prohibitively expensive Impute Less accurate but “free” Strains A B C D E . 1 ? SNPs
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Previous Imputation Approaches
Hidden Markov Models (Stephens et al., 2001; Lin et al., 2002; Niu et al., 2002) Entropy Measures (Su et al., 2005) Expectation Maximization (Qin et al., 2002) Tree-Based Perfect Phylogeny (Eskin et al., 2003) Despite of their methodological differences, they have two things in common: Complex Slow
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NPUTE A simple method for imputing missing genotypes based on a “nearest-neighbor” approach within arbitrary windows An efficient data structure for finding pairwise haplotype similarity This simplicity leads to benefits in: Speed Exhaustive searches over multiple parameters The result is a fast imputation approach with competitive accuracy.
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Imputation Approach Ideal Method: Our Method:
Within a haplotype block, find the nearest neighbor to the strain missing a genotype and fill it in with the neighbor’s value. Problem: Finding haplotype blocks is a very difficult and time consuming problem on its own. Our Method: Find the nearest neighbor within a window extending L SNPs above and below the missing value.
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How to Find the Best Window
We consider all symmetric windows of size 2L+1 for each practical L across the genome and use the closest match to “impute” all known values. Accuracy is estimated by imputing values of every known site for each L. The best L is an estimate of the average haplotype block size and is used for the imputation of “no-calls”.
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Naïve Approach Strains A B C D E . 1 ? SNPs
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Naïve Approach L = 2 . 1 ? Strains A B C D E SNPs Scoring Function ? 1
? L = 2 SNPs Scoring Function ? 1 0.5
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Naïve Approach Strains A B C D E . 1 ? L = 2 A B C E 1.5 2 3.5 SNPs
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Naïve Approach Strains A B C D E . 1 ? L = 2 A B C E 1.5 2 3.5 SNPs
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NPUTE Data Structures Begin with ternary SNPs Sij {0, 1, ?}
Build Pairwise Mismatch Vector (PMV) for each SNP (scaled by 2 to allow integer arithmetic) 0 = Match 1 = Unknown 2 = Mismatch Sum PMVs to make Mismatch Accumulator Array (MAA) Constant time lookup for the PMV over any window using row subtraction 2202 020 20 2 1 10010 MAA Mismatch Vector SNPs 10010 10001 011?0 00101 0?100 0??01
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NPUTE Approach . 1 ? Strains A B C D E SNPs MAA 35 59 52 32 55 52 45 3
. 1 ? SNPs 35 59 52 32 55 52 45 3 4 7
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NPUTE on Real Data Perlegen Data (http://mouse.perlegen.com) 150K Data
8.3 million SNPs 16 mouse strains 11.1% missing calls 150K Data 140K Broad/MIT mouse dataset + 10K GNF mouse dataset 46 mouse strains 4.2% missing calls
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NPUTE on Perlegen Data
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NPUTE on Perlegen Data
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NPUTE on Perlegen Data 8.3 Million SNPs with 16 strains
We estimate that it will take 88 days for fastPhase to impute the data 60 s per imputation, ~135 minutes for entire dataset
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NPUTE on 150K Data
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NPUTE on 150K Data
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65 s per imputation, ~7.5 minutes for the entire dataset
NPUTE on 150K Data 150 K SNPs with 46 Strains 65 s per imputation, ~7.5 minutes for the entire dataset
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Extensions to NPUTE We can establish a measure of confidence in our calls based on the fraction of matching values of the nearest neighbor. A threshold can be set to only impute high confidence calls. Imputation can proceed iteratively allowing high- confidence calls to aid in the imputation of lower-confidence calls.
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Summary Available at http://compgen.unc.edu
Better or competitive accuracy to alternative approaches Orders of magnitude faster O(NS2) space where N is the number of SNPs S is the number of strains O(S) time per imputation O(NS2) time for the whole genome Enables genome wide imputation Further optimization and extension
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MAA is Versatile Small tweaks to the Mismatch Accumulator Array (MAA) support a variety of queries Finding local regions of Identity-by-descent Counting the number of unique haplotypes within arbitrary windows Query speed is independent of window size
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Acknowledgement: EPA STAR RD832720 NSF IIS 0448392 NSF IIS 0534580
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