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Next-generation-sequencing-based identification of familial hypercholesterolemia-related mutations in subjects with increased LDL–C levels in a latvian population

Overview of attention for article published in BMC Medical Genomics, September 2015
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Title
Next-generation-sequencing-based identification of familial hypercholesterolemia-related mutations in subjects with increased LDL–C levels in a latvian population
Published in
BMC Medical Genomics, September 2015
DOI 10.1186/s12881-015-0230-x
Pubmed ID
Authors

Ilze Radovica-Spalvina, Gustavs Latkovskis, Ivars Silamikelis, Davids Fridmanis, Ilze Elbere, Karlis Ventins, Guna Ozola, Andrejs Erglis, Janis Klovins

Abstract

Familial hypercholesterolemia (FH) is one of the commonest monogenic disorders, predominantly inherited as an autosomal dominant trait. When untreated, it results in early coronary heart disease. The vast majority of FH remains undiagnosed in Latvia. The identification and early treatment of affected individuals remain a challenge worldwide. Most cases of FH are caused by mutations in one of four genes, APOB, LDLR, PCSK9, or LDLRAP1. The spectrum of disease-causing variants is very diverse and the variation detection panels usually used in its diagnosis cover only a minority of the disease-causing gene variants. However, DNA-based tests may provide an FH diagnosis for FH patients with no physical symptoms and with no known family history of the disease. Here, we evaluate the use of targeted next-generation sequencing (NGS) to identify cases of FH in a cohort of patients with coronary artery disease (CAD) and individuals with abnormal low-density lipoprotein-cholesterol (LDL-C) levels. We used targeted amplification of the coding regions of LDLR, APOB, PCSK9, and LDLRAP1, followed by NGS, in 42 CAD patients (LDL-C, 4.1-7.2 mmol/L) and 50 individuals from a population-based cohort (LDL-C, 5.1-9.7 mmol/L). In total, 22 synonymous and 31 nonsynonymous variants, eight variants in close proximity (10 bp) to intron-exon boundaries, and 50 other variants were found. We identified four pathogenic mutations (p.(Arg3527Gln) in APOB, and p.(Gly20Arg), p.(Arg350*), and c.1706-10G > A in LDLR) in seven patients (7.6 %). Three possible pathogenic variants were also found in four patients. NGS-based methods can be used to detect FH in high-risk individuals when they do not meet the defined clinical criteria.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Unknown 69 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 19%
Student > Bachelor 10 14%
Student > Ph. D. Student 9 13%
Student > Master 7 10%
Professor 5 7%
Other 15 21%
Unknown 11 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 33%
Medicine and Dentistry 16 23%
Agricultural and Biological Sciences 9 13%
Pharmacology, Toxicology and Pharmaceutical Science 4 6%
Unspecified 1 1%
Other 2 3%
Unknown 15 21%
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 22 April 2016.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from BMC Medical Genomics
#1,315
of 2,444 outputs
Outputs of similar age
#171,319
of 286,439 outputs
Outputs of similar age from BMC Medical Genomics
#40
of 68 outputs
Altmetric has tracked 25,374,647 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 2,444 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 36th percentile – i.e., 36% 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 286,439 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.