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Extending long-range phasing and haplotype library imputation methods to impute genotypes on sex chromosomes

Overview of attention for article published in Genetics Selection Evolution, April 2013
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Title
Extending long-range phasing and haplotype library imputation methods to impute genotypes on sex chromosomes
Published in
Genetics Selection Evolution, April 2013
DOI 10.1186/1297-9686-45-10
Pubmed ID
Authors

John M Hickey, Andreas Kranis

Abstract

AlphaImpute is a flexible and accurate genotype imputation tool that was originally designed for the imputation of genotypes on autosomal chromosomes. In some species, sex chromosomes comprise a large portion of the genome. For example, chromosome Z represents approximately 8% of the chicken genome and therefore is likely to be important in determining genetic variation in a population. When breeding programs make selection decisions based on genomic information, chromosomes that are not represented on the genotyping platform will not be subject to selection. Therefore imputation algorithms should be able to impute genotypes for all chromosomes. The objective of this research was to extend AlphaImpute so that it could impute genotypes on sex chromosomes. The accuracy of imputation was assessed using different genotyping strategies in a real commercial chicken population. The correlation between true and imputed genotypes was high in all the scenarios and was 0.96 for the most favourable scenario. Overall, the accuracy of imputation of the sex chromosome was slightly lower than that of autosomes for all scenarios considered.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 5%
Colombia 1 5%
Unknown 19 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 48%
Student > Ph. D. Student 6 29%
Student > Doctoral Student 3 14%
Student > Bachelor 2 10%
Other 1 5%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 67%
Biochemistry, Genetics and Molecular Biology 3 14%
Mathematics 1 5%
Computer Science 1 5%
Medicine and Dentistry 1 5%
Other 0 0%
Unknown 1 5%
Attention Score in Context

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 February 2014.
All research outputs
#17,286,379
of 25,374,917 outputs
Outputs from Genetics Selection Evolution
#550
of 822 outputs
Outputs of similar age
#131,038
of 205,935 outputs
Outputs of similar age from Genetics Selection Evolution
#2
of 5 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 822 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 205,935 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.