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Open Access Research article

Fast accurate missing SNP genotype local imputation

Yining Wang1, Zhipeng Cai2, Paul Stothard3, Steve Moore3, Randy Goebel1, Lusheng Wang4 and Guohui Lin1*

Author Affiliations

1 Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada

2 Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

3 Department of Agriculture, Food, and Nutritional Science, University of Alberta, Edmonton, Alberta T6G 2C8, Canada

4 Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong SAR, China

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BMC Research Notes 2012, 5:404  doi:10.1186/1756-0500-5-404

Published: 3 August 2012

Abstract

Background

Single nucleotide polymorphism (SNP) genotyping assays normally give rise to certain percents of no-calls; the problem becomes severe when the target organisms, such as cattle, do not have a high resolution genomic sequence. Missing SNP genotypes, when related to target traits, would confound downstream data analyses such as genome-wide association studies (GWAS). Existing methods for recovering the missing values are successful to some extent – either accurate but not fast enough or fast but not accurate enough.

Results

To a target missing genotype, we take only the SNP loci within a genetic distance vicinity and only the samples within a similarity vicinity into our local imputation process. For missing genotype imputation, the comparative performance evaluations through extensive simulation studies using real human and cattle genotype datasets demonstrated that our nearest neighbor based local imputation method was one of the most efficient methods, and outperformed existing methods except the time-consuming fastPHASE; for missing haplotype allele imputation, the comparative performance evaluations using real mouse haplotype datasets demonstrated that our method was not only one of the most efficient methods, but also one of the most accurate methods.

Conclusions

Given that fastPHASE requires a long imputation time on medium to high density datasets, and that our nearest neighbor based local imputation method only performed slightly worse, yet better than all other methods, one might want to adopt our method as an alternative missing SNP genotype or missing haplotype allele imputation method.