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Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide

Overview of attention for article published in Journal of Translational Medicine, January 2015
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Title
Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
Published in
Journal of Translational Medicine, January 2015
DOI 10.1186/s12967-014-0333-8
Pubmed ID
Authors

Roger V Lebo, Vijay S Tonk

Abstract

BackgroundOur genomewide studies support targeted testing the most frequent genetic diseases by patient category: (1) pregnant patients, (2) at-risk conceptuses, (3) affected children, and (4) abnormal adults. This approach not only identifies most reported disease causing sequences accurately, but also minimizes incorrectly identified additional disease causing loci.MethodsDiseases were grouped in descending order of occurrence from four data sets: (1) GeneTests 534 listed population prevalences, (2) 4129 high risk prenatal karyotypes, (3) 1265 affected patient microarrays, and (4) reanalysis of 25,452 asymptomatic patient results screened prenatally for 108 genetic diseases. These most frequent diseases are categorized by transmission: (A) autosomal recessive, (B) X-linked, (C) autosomal dominant, (D) microscopic chromosome rearrangements, (E) submicroscopic copy number changes, and (F) frequent ethnic diseases.ResultsAmong affected and carrier patients worldwide, most reported mutant genes would be identified correctly according to one of four patient categories from at-risk couples with <64 tested genes to affected adults with 314 tested loci. Three clinically reported patient series confirmed this approach. First, only 54 targeted chromosomal sites would have detected all 938 microscopically visible unbalanced karyotypes among 4129 karyotyped POC, CVS, and amniocentesis samples. Second, 37 of 48 reported aneuploid regions were found among our 1265 clinical microarrays confirming the locations of 8 schizophrenia loci and 20 aneuploidies altering intellectual ability, while also identifying 9 of the most frequent deletion syndromes. Third, testing 15 frequent genes would have identified 124 couples with a 1 in 4 risk of a fetus with a recessive disease compared to the 127 couples identified by testing all 108 genes, while testing all mutations in 15 genes could have identified more couples.ConclusionTesting the most frequent disease causing abnormalities in 1 of 8 reported disease loci [~1 of 84 total genes] will identify ~ 7 of 8 reported abnormal Caucasian newborn genotypes. This would eliminate ~8 to 10 of ~10 Caucasian newborn gene sequences selected as abnormal that are actually normal variants identified when testing all ~2500 diseases looking for the remaining 1 of 8 disease causing genes. This approach enables more accurate testing within available laboratory and reimbursement resources.

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The data shown below were collected from the profiles of 2 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 21%
Researcher 4 17%
Student > Doctoral Student 2 8%
Student > Bachelor 2 8%
Other 2 8%
Other 5 21%
Unknown 4 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 17%
Psychology 4 17%
Social Sciences 4 17%
Nursing and Health Professions 3 13%
Medicine and Dentistry 2 8%
Other 0 0%
Unknown 7 29%
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 22 January 2015.
All research outputs
#16,047,334
of 25,373,627 outputs
Outputs from Journal of Translational Medicine
#2,132
of 4,635 outputs
Outputs of similar age
#199,995
of 359,545 outputs
Outputs of similar age from Journal of Translational Medicine
#52
of 122 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,635 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 49th percentile – i.e., 49% 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 359,545 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 122 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.