Sequence Variations and Haplotypes of the GJB2 Gene Revealed by Resequencing of 192 Chromosomes from the General Population in Korea

The effectiveness of expanded carrier screening based on next-generation sequencing for severe monogenic genetic diseases

Expanded carrier screening (ECS) based on next-generation sequencing has been the subject of few studies to estimate the effectiveness of ECS in the Chinese population. A total of 3737 individuals from Southwest China or the general Chinese population, including 1048 pairs and 1641 individuals, were analysed by ECS for 155 monogenetic diseases. An ECS panel was used to detect 147 genes and 10,449 variants in 145 autosomal recessive and 10 X-linked recessive disorders. A total of 43.27% (1617/3737) were found to be carriers of at least one of the 155 monogenetic diseases. The average number of carriers of these recessive mutations was 0.54 and ranged from 0 to 4. Of the 1048 couples, 74.81% (n = 784) were found to have at least one partner carrying more than one disease. In addition, 5.34% of the couples at risk (n = 56) were heterozygous for the same autosomal recessive disease, and 0.37% of the women (9/2440) were carriers of X-linked diseases. Our study demonstrated the clinical significance of ECS in Chinese populations and the need for a programme of familial screening for the prevention of severe recessive monogenetic diseases.

Machine learning-based genetic diagnosis models for hereditary hearing loss by the GJB2, SLC26A4 and MT-RNR1 variants

Background

Hereditary hearing loss (HHL) is the most common sensory deficit, which highly afflicts humans. With gene sequencing technology development, more variants will be identified and support genetic diagnoses, which is difficult for human experts to diagnose. This study aims to develop a machine learning-based genetic diagnosis model of HHL-related variants of GJB2, SLC26A4 and MT-RNR1.

Methods

This case-control study included 1898 subjects, among which 1354 were HHL patients and 544 were carriers. Risk assessment models were established based on variants at 144 sites in three genes related to HHL by building six machine learning (ML) models. We compared the ML models with the genetic risk score (GRS) and expert interpretation (EI) to verify the clinical performance.

Findings

Among the six ML models, the support vector machine (SVM) showed the best performance. For the prediction of HHL-related gene sites in subjects with variants, the area under the receiver operating characteristic (AUC) of the SVM model was 0.803 (0.680–0.814) in the 10-fold stratified cross-validation and 0.751 (0.635–0.779) in external validation. The predicted results were better than both EI and GRS. Furthermore, 11 sites were identified as the smallest feature set that can be accurately predicted.

Interpretation

The developed SVM model has great potential to be an efficient and effective tool for HHL prediction when high throughput sequencing data are available.

Identification of a novel homozygous mutation, TMPRSS3: c.535G>A, in a Tibetan family with autosomal recessive non-syndromic hearing loss

Different ethnic groups have distinct mutation spectrums associated with inheritable deafness. In order to identify the mutations responsible for congenital hearing loss in the Tibetan population, mutation screening for 98 deafness-related genes by microarray and massively parallel sequencing of captured target exons was conducted in one Tibetan family with familiar hearing loss. A homozygous mutation, TMPRSS3: c.535G>A, was identified in two affected brothers. Both parents are heterozygotes and an unaffected sister carries wild type alleles. The same mutation was not detected in 101 control Tibetan individuals. This missense mutation results in an amino acid change (p.Ala179Thr) at a highly conserved site in the scavenger receptor cysteine rich (SRCR) domain of the TMPRSS3 protein, which is essential for protein-protein interactions. Thus, this mutation likely affects the interactions of this transmembrane protein with extracellular molecules. According to our bioinformatic analyses, the TMPRSS3: c.535G>A mutation might damage protein function and lead to hearing loss. These data suggest that the homozygous mutation TMPRSS3: c.535G>A causes prelingual hearing loss in this Tibetan family. This is the first TMPRSS3 mutation found in the Chinese Tibetan population.

Etiology and audiological outcomes at 3 years for 364 children in Australia

Hearing loss is an etiologically heterogeneous trait with differences in the age of onset, severity and site of lesion. It is caused by a combination of genetic and/or environmental factors. A longitudinal study to examine the efficacy of early intervention for improving child outcomes is ongoing in Australia. To determine the cause of hearing loss in these children we undertook molecular testing of perinatal “Guthrie” blood spots of children whose hearing loss was either detected via newborn hearing screening or detected later in infancy. We analyzed the GJB2 and SLC26A4 genes for the presence of mutations, screened for the mitochondrial DNA (mtDNA) A1555G mutation, and screened for congenital CMV infection in DNA isolated from dried newborn blood spots. Results were obtained from 364 children. We established etiology for 60% of children. One or two known GJB2 mutations were present in 82 children. Twenty-four children had one or two known SLC26A4 mutations. GJB2 or SLC26A4 changes with unknown consequences on hearing were found in 32 children. The A1555G mutation was found in one child, and CMV infection was detected in 28 children. Auditory neuropathy spectrum disorder was confirmed in 26 children whose DNA evaluations were negative. A secondary objective was to investigate the relationship between etiology and audiological outcomes over the first 3 years of life. Regression analysis was used to investigate the relationship between hearing levels and etiology. Data analysis does not support the existence of differential effects of etiology on degree of hearing loss or on progressiveness of hearing loss.