↓ Skip to main content

A phased SNP-based classification of sickle cell anemia HBB haplotypes

Overview of attention for article published in BMC Genomics, August 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
82 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A phased SNP-based classification of sickle cell anemia HBB haplotypes
Published in
BMC Genomics, August 2017
DOI 10.1186/s12864-017-4013-y
Pubmed ID
Authors

Elmutaz M. Shaikho, John J. Farrell, Abdulrahman Alsultan, Hatem Qutub, Amein K. Al-Ali, Maria Stella Figueiredo, David H.K. Chui, Lindsay A. Farrer, George J. Murphy, Gustavo Mostoslavsky, Paola Sebastiani, Martin H. Steinberg

Abstract

Sickle cell anemia causes severe complications and premature death. Five common β-globin gene cluster haplotypes are each associated with characteristic fetal hemoglobin (HbF) levels. As HbF is the major modulator of disease severity, classifying patients according to haplotype is useful. The first method of haplotype classification used restriction fragment length polymorphisms (RFLPs) to detect single nucleotide polymorphisms (SNPs) in the β-globin gene cluster. This is labor intensive, and error prone. We used genome-wide SNP data imputed to the 1000 Genomes reference panel to obtain phased data distinguishing parental alleles. We successfully haplotyped 813 sickle cell anemia patients previously classified by RFLPs with a concordance >98%. Four SNPs (rs3834466, rs28440105, rs10128556, and rs968857) marking four different restriction enzyme sites unequivocally defined most haplotypes. We were able to assign a haplotype to 86% of samples that were either partially or misclassified using RFLPs. Phased data using only four SNPs allowed unequivocal assignment of a haplotype that was not always possible using a larger number of RFLPs. Given the availability of genome-wide SNP data, our method is rapid and does not require high computational resources.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 13 16%
Researcher 8 10%
Student > Postgraduate 7 9%
Student > Master 6 7%
Lecturer 6 7%
Other 17 21%
Unknown 25 30%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 26%
Medicine and Dentistry 18 22%
Agricultural and Biological Sciences 8 10%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Unspecified 1 1%
Other 7 9%
Unknown 25 30%
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 23 December 2017.
All research outputs
#15,486,175
of 23,012,811 outputs
Outputs from BMC Genomics
#6,724
of 10,697 outputs
Outputs of similar age
#199,730
of 318,488 outputs
Outputs of similar age from BMC Genomics
#129
of 222 outputs
Altmetric has tracked 23,012,811 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,697 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 28th percentile – i.e., 28% 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 318,488 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 222 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.