Whole exome sequencing of a family revealed a novel variant in the CHM gene, c.22delG p.(Glu8Serfs*4), which co-segregated with choroideremia

Abstract

Choroideremia is a complex form of blindness-causing retinal degeneration. The aim of the present study was to investigate the pathogenic variant and molecular etiology associated with choroideremia in a Chinese family. All available family members underwent detailed ophthalmological examinations. Whole exome sequencing, bioinformatics analysis, Sanger sequencing, and co-segregation analysis of family members were used to validate sequencing data and confirm the presence of the disease-causing gene variant. The proband was diagnosed with choroideremia on the basis of clinical manifestations. Whole exome sequencing showed that the proband had a hemizygous variant in the CHM gene, c.22delG p. (Glu8Serfs*4), which was confirmed by Sanger sequencing and found to co-segregate with choroideremia. The variant was classified as likely pathogenic and has not previously been described. These results expand the spectrum of variants in the CHM gene, thus potentially enriching the understanding of the molecular basis of choroideremia. Moreover, they may provide insight for future choroideremia diagnosis and gene therapy.

Clinical and genetic findings in a Chinese cohort with choroideremia

“…We combined the 27 patients in our current cohort with 68 Chinese male patients from six previously reported studies [13][14][15][16][17][18] to determine a transition age for BCVA rapid decline in the Chinese population. These six studies described the age, BCVA, and variants of the CHM of 68 patients from 54 unrelated families (Table S1).…”

“…We obtained a more precise relationship between BCVA and age of Chinese patients with CHM by combining the 27 patients in the current cohort with 68 Chinese patients from six previously published studies [13][14][15][16][17][18]. The mean age of the 95 patients was 33.27 ± 16.27 years (range 5-78), and their average (LogMAR) was 0.72 ± 0.82 (range 0-2.70).…”

“…Comprehensive studies of the clinical and genetic features of patients with CHM have been conducted in the western population, but similar studies are lacking in Chinese patients. In the current study, we characterized the phenotypes and genotypes of 27 Chinese male patients and then combined our cohort of 27 patients with 68 Chinese patients from 6 previously published studies [13][14][15][16][17][18] to estimate a transition age of the decline of BCVA in Chinese patients with CHM.…”

“…Another study that included 48 Chinese patients revealed a transition age of 20 years for Chinese patients with CHM, which was much younger than the transition age observed in western patients [13]. Considering the small numbers of Chinese patients in our cohort, we combined our patients with the 68 Chinese patients described in six previous studies [13][14][15][16][17][18] and calculated a transition age using the method of function fitting combined with preset piecewise regression. Our results showed a transition age of 25 years, which was relatively close to the age of 30 years described by Heon et al [25], but much younger than the ages described in several Caucasian studies [9,10,12,24] and a transition age of 40 years recently reported in Japanese patients [26].…”

Whole exome sequencing revealed 14 variants in NDP, FZD4, LRP5, and TSPAN12 genes for 20 families with familial exudative vitreoretinopathy

“…Quality control was recalibrated by Picard Mark Duplicates ( http://sourceforge.net/projects/picard/ ), Genome Analysis Toolkit ( https://gatk.broadinstitute.org/hc/en-us ), and SAM tools ( http://samtools.sourceforge.net/ ). Variants were validated and analyzed preferentially if they met the following previously reported criteria [ 24 – 26 ]: (1) minor allele frequency of the variant < 0.01 in the 1000 Genomes Project database ( http://www.internationalgenome.org/ ), Exome Aggregation Consortium database ( http://exac.broadinstitute.org/ ), Genome Aggregation database ( http://gnomad.broadinstitute.org/ ), and an in-house Chinese individuals database; (2) variant location in an exon region or canonical splicing intron region that affected transcription splicing; (3) damaging or deleterious variant prediction using Sorting Intolerant From Tolerant ( http://sift.jcvi.org/ ), Protein Variation Effect Analyzer ( http://provean.jcvi.org ), Polymorphism Phenotyping ( http://genetics.bwh.harvard.edu/pph2/ ), ClinPred ( https://sites.google.com/site/clinpred/ ), Likelihood Ratio Test ( http://www.genetics.wustl.edu/jflab/lrt ), Mutation Taster ( http://www.mutationtaster.org/ ), Mutation Assessor ( http://mutationassessor.org/r3 ), and Functional Analysis Through Hidden Markov Models ( http://fathmm.biocompute.org.uk/ ); (4) highly conserved variant prediction using Genomic Evolutionary Rate Profiling ( http://mendel.stanford.edu/SidowLab/downloads/gerp ); (5) other reported pathogenic variant that did not meet the above criteria (e.g., high minor allele frequency variant, deep-intronic variant, or synonymous single nucleotide variant).…”