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Imputation of Microsatellite Alleles from Dense SNP Genotypes for Parental Verification

Overview of attention for article published in Frontiers in Genetics, January 2012
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Title
Imputation of Microsatellite Alleles from Dense SNP Genotypes for Parental Verification
Published in
Frontiers in Genetics, January 2012
DOI 10.3389/fgene.2012.00140
Pubmed ID
Authors

Matthew McClure, Tad Sonstegard, George Wiggans, Curtis P Van Tassell

Abstract

Microsatellite (MS) markers have recently been used for parental verification and are still the international standard despite higher cost, error rate, and turnaround time compared with Single Nucleotide Polymorphisms (SNP)-based assays. Despite domestic and international interest from producers and research communities, no viable means currently exist to verify parentage for an individual unless all familial connections were analyzed using the same DNA marker type (MS or SNP). A simple and cost-effective method was devised to impute MS alleles from SNP haplotypes within breeds. For some MS, imputation results may allow inference across breeds. A total of 347 dairy cattle representing four dairy breeds (Brown Swiss, Guernsey, Holstein, and Jersey) were used to generate reference haplotypes. This approach has been verified (>98% accurate) for imputing the International Society of Animal Genetics recommended panel of 12 MS for cattle parentage verification across a validation set of 1,307 dairy animals. Implementation of this method will allow producers and breed associations to transition to SNP-based parentage verification utilizing MS genotypes from historical data on parents where SNP genotypes are missing. This approach may be applicable to additional cattle breeds and other species that wish to migrate from MS- to SNP-based parental verification.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Colombia 1 2%
Netherlands 1 2%
South Africa 1 2%
New Zealand 1 2%
Thailand 1 2%
Unknown 53 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 23%
Researcher 12 20%
Student > Ph. D. Student 10 17%
Student > Postgraduate 4 7%
Professor > Associate Professor 3 5%
Other 6 10%
Unknown 11 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 57%
Biochemistry, Genetics and Molecular Biology 3 5%
Veterinary Science and Veterinary Medicine 3 5%
Computer Science 2 3%
Medicine and Dentistry 2 3%
Other 4 7%
Unknown 12 20%
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 14 August 2012.
All research outputs
#20,165,369
of 22,675,759 outputs
Outputs from Frontiers in Genetics
#8,510
of 11,737 outputs
Outputs of similar age
#221,176
of 244,088 outputs
Outputs of similar age from Frontiers in Genetics
#195
of 255 outputs
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We're also able to compare this research output to 255 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.