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CATCHing putative causative variants in consanguineous families

Overview of attention for article published in BMC Bioinformatics, September 2015
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Title
CATCHing putative causative variants in consanguineous families
Published in
BMC Bioinformatics, September 2015
DOI 10.1186/s12859-015-0727-5
Pubmed ID
Authors

Federico Andrea Santoni, Periklis Makrythanasis, Stylianos E. Antonarakis

Abstract

Consanguinity is an important risk factor for autosomal recessive (AR) disorders. Extended genomic regions identical by descent (IBD) in the offspring of consanguineous parents give rise to recessive disorders with identical (homozygous) pathogenic variants in both alleles. However, many clinical phenotypes presenting in the offspring of consanguineous couples are still of unknown etiology. Nowadays advances in High Throughput Sequencing provide an excellent opportunity to achieve a molecular diagnosis or to identify novel candidate genes. To exploit all available information from the family structure we developed CATCH, an algorithm that combines genotyped SNPs of all family members for the optimal detection of Runs Of Homozygosity (ROH) and exome sequencing data from one affected individual to identify putative causative variants in consanguineous families. CATCH proved to be effective in discovering known or putative new causative variants in 43 out of 50 consanguineous families. Among them, novel variants causative of familial thrombocytopenia, sclerosis bone dysplasia and the first homozygous loss-of-function mutation in FGFR3 in human causing severe skeletal deformities, tall stature and hearing impairment were identified.

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The data shown below were collected from the profiles of 4 X users 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Master 4 18%
Student > Ph. D. Student 3 14%
Student > Bachelor 2 9%
Student > Doctoral Student 1 5%
Other 2 9%
Unknown 4 18%
Readers by discipline Count As %
Medicine and Dentistry 9 41%
Biochemistry, Genetics and Molecular Biology 7 32%
Computer Science 1 5%
Nursing and Health Professions 1 5%
Unknown 4 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 30 September 2015.
All research outputs
#14,238,817
of 22,829,083 outputs
Outputs from BMC Bioinformatics
#4,727
of 7,287 outputs
Outputs of similar age
#141,932
of 274,283 outputs
Outputs of similar age from BMC Bioinformatics
#87
of 143 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 31st percentile – i.e., 31% of its peers scored the same or lower than it.
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We're also able to compare this research output to 143 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.