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

BACKGROUND: Our 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, bu...

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Autores principales: Lebo, Roger V, Tonk, Vijay S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312458/
https://www.ncbi.nlm.nih.gov/pubmed/25604770
http://dx.doi.org/10.1186/s12967-014-0333-8
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author Lebo, Roger V
Tonk, Vijay S
author_facet Lebo, Roger V
Tonk, Vijay S
author_sort Lebo, Roger V
collection PubMed
description BACKGROUND: Our 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. METHODS: Diseases 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. RESULTS: Among 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. CONCLUSION: Testing 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-014-0333-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-43124582015-02-01 Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide Lebo, Roger V Tonk, Vijay S J Transl Med Research BACKGROUND: Our 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. METHODS: Diseases 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. RESULTS: Among 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. CONCLUSION: Testing 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-014-0333-8) contains supplementary material, which is available to authorized users. BioMed Central 2015-01-21 /pmc/articles/PMC4312458/ /pubmed/25604770 http://dx.doi.org/10.1186/s12967-014-0333-8 Text en © Lebo and Tonk; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lebo, Roger V
Tonk, Vijay S
Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title_full Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title_fullStr Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title_full_unstemmed Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title_short Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
title_sort analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312458/
https://www.ncbi.nlm.nih.gov/pubmed/25604770
http://dx.doi.org/10.1186/s12967-014-0333-8
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