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Whole genome SNP genotype piecemeal imputation

Overview of attention for article published in BMC Bioinformatics, October 2015
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Title
Whole genome SNP genotype piecemeal imputation
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0770-2
Pubmed ID
Authors

Yining Wang, Tim Wylie, Paul Stothard, Guohui Lin

Abstract

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. 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. 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Finland 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 42%
Student > Bachelor 2 17%
Student > Ph. D. Student 1 8%
Professor 1 8%
Student > Master 1 8%
Other 1 8%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 42%
Biochemistry, Genetics and Molecular Biology 4 33%
Business, Management and Accounting 1 8%
Computer Science 1 8%
Engineering 1 8%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2015.
All research outputs
#8,687,834
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#3,314
of 4,195 outputs
Outputs of similar age
#156,418
of 250,697 outputs
Outputs of similar age from BMC Bioinformatics
#106
of 137 outputs
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So far Altmetric has tracked 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.