Title
Novel mutations in genes causing hereditary spastic paraplegia and Charcot-Marie-Tooth neuropathy identified by an optimized protocol for homozygosity mapping based on whole-exome sequencing Novel mutations in genes causing hereditary spastic paraplegia and Charcot-Marie-Tooth neuropathy identified by an optimized protocol for homozygosity mapping based on whole-exome sequencing
Author
Faculty/Department
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Philadelphia, Pa. ,
Subject
Human medicine
Source (journal)
Genetics in medicine. - Philadelphia, Pa.
Volume/pages
18(2016) :6 , p. 600-607
ISSN
1098-3600
ISI
000378184300009
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Purpose: Homozygosity mapping is an effective approach for detecting molecular defects in consanguineous families by delineating stretches of genomic DNA that are identical by descent. Constant developments in next-generation sequencing created possibilities to combine whole-exome sequencing (WES) and homozygosity Mapping in a single step. Methods: Basic optimization of homozygosity mapping parameters was performed in a group of families with autosomal-recessive (AR) mutations for which both single-nucleotide polymorphism (SNP) array and WES data were available. We varied the criteria for SNP extraction and PLINK thresholds to estimate their effect on the accuracy of homozygosity mapping based on WES. Results: Our protocol showed high specificity and sensitivity for homozygosity detection and facilitated the identification of novel mutations in GAN, GBA2, and ZFYVE26 in four families affected by hereditary spastic paraplegia or Charcot-Marie-Tooth disease. Filtering and mapping with optimized parameters was integrated into the HOMWES (homozygosity mapping based on WES analysis) tool in the GenomeComb package for genomic data analysis. Conclusion: We present recommendations for detection of homozygous regions based on WES data and a bioinformatics tool for their identification, which can be widely applied for studying AR disorders.
E-info
https://repository.uantwerpen.be/docman/iruaauth/0e008e/134629.pdf
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