Evidence map›Paper›PMID 25604770›Full record

ArticleJournal of translational medicine2015

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

Roger V Lebo, Vijay S Tonk

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Article in Journal of translational medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Roger V LeboDepartment of Pathology and Laboratory Medicine, Akron Children's Hospital, One Perkins Square, Akron, OH, 44308-1062, USA. rlebo@chmca.org.
Vijay S TonkDepartment of Pediatrics, Texas Tech University Health Science Center, 4th Street 3601, Lubbock, TX, 79416, USA. Vijay.tonk@ttuhsc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Genetic LociGenetic Predisposition to DiseaseAdultChildChromosomes, HumanDiseaseFemaleGenes, RecessiveGenetics, PopulationGenetic TestingHeterozygoteHumansInfant, NewbornPregnancyPrenatal DiagnosisRare Diseases

Identifiers

PMID25604770
PMCPMC4312458

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.