Whole genome SNP genotype piecemeal imputation.
BACKGROUND
Despite ongoing reductions in the cost of sequencing technologies, whole genome SNP genotype imputation is often used as an alternative for obtaining abundant SNP genotypes for genome wide association studies. Several existing genotype imputation methods can be efficient for this purpose, while achieving various levels of imputation accuracy. Recent empirical results have shown that the two-step imputation may improve accuracy by imputing the low density genotyped study animals to a medium density array first and then to the target density. We are interested in building a series of staircase arrays that lead the low density array to the high density array or even the whole genome, such that genotype imputation along these staircases can achieve the highest accuracy.


RESULTS
For genotype imputation from a lower density to a higher density, we first show how to select untyped SNPs to construct a medium density array. Subsequently, we determine for each selected SNP those untyped SNPs to be imputed in the add-one two-step imputation, and lastly how the clusters of imputed genotype are pieced together as the final imputation result. We design extensive empirical experiments using several hundred sequenced and genotyped animals to demonstrate that our novel two-step piecemeal imputation always achieves an improvement compared to the one-step imputation by the state-of-the-art methods Beagle and FImpute. Using the two-step piecemeal imputation, we present some preliminary success on whole genome SNP genotype imputation for genotyped animals via a series of staircase arrays.


CONCLUSIONS
From a low SNP density to the whole genome, intermediate pseudo-arrays can be computationally constructed by selecting the most informative SNPs for untyped SNP genotype imputation. Such pseudo-array staircases are able to impute more accurately than the classic one-step imputation.
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. yining@ualberta.ca.
Yining
Y
Wang
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. timothy.wylie@utrgv.edu.|Currently with Department of Computer Science, University of Texas - Rio Grande Valley, Edinburg, 78539, Texas, USA. timothy.wylie@utrgv.edu.
Tim
T
Wylie
Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, T6G 2C8, Alberta, Canada. stothard@ualberta.ca.
Paul
P
Stothard
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada. guohui@ualberta.ca.
Guohui
G
Lin
D000818:Animals; D002417:Cattle; D016000:Cluster Analysis; D016678:Genome; D055106:Genome-Wide Association Study; D005838:Genotype; D020641:Polymorphism, Single Nucleotide; D012984:Software