Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2020 Oct 28;10(11):258.
doi: 10.3390/life10110258.

Connexin Genes Variants Associated with Non-Syndromic Hearing Impairment: A Systematic Review of the Global Burden

Samuel Mawuli Adadey  1   2   3 , Edmond Wonkam-Tingang  3 , Elvis Twumasi Aboagye  2   3 , Daniel Wonder Nayo-Gyan  4 , Maame Boatemaa Ansong  2 , Osbourne Quaye  1   2 , Gordon A Awandare  1   2 , Ambroise Wonkam  3
Affiliations
Review

Connexin Genes Variants Associated with Non-Syndromic Hearing Impairment: A Systematic Review of the Global Burden

Samuel Mawuli Adadey et al. Life (Basel). .

Abstract

Mutations in connexins are the most common causes of hearing impairment (HI) in many populations. Our aim was to review the global burden of pathogenic and likely pathogenic (PLP) variants in connexin genes associated with HI. We conducted a systematic review of the literature based on targeted inclusion/exclusion criteria of publications from 1997 to 2020. The databases used were PubMed, Scopus, Africa-Wide Information, and Web of Science. The protocol was registered on PROSPERO, the International Prospective Register of Systematic Reviews, with the registration number "CRD42020169697". The data extracted were analyzed using Microsoft Excel and SPSS version 25 (IBM, Armonk, New York, United States). A total of 571 independent studies were retrieved and considered for data extraction with the majority of studies (47.8% (n = 289)) done in Asia. Targeted sequencing was found to be the most common technique used in investigating connexin gene mutations. We identified seven connexin genes that were associated with HI, and GJB2 (520/571 publications) was the most studied among the seven. Excluding PLP in GJB2, GJB6, and GJA1 the other connexin gene variants (thus GJB3, GJB4, GJC3, and GJC1 variants) had conflicting association with HI. Biallelic GJB2 PLP variants were the most common and widespread variants associated with non-syndromic hearing impairment (NSHI) in different global populations but absent in most African populations. The most common GJB2 alleles found to be predominant in specific populations include; p.Gly12ValfsTer2 in Europeans, North Africans, Brazilians, and Americans; p.V37I and p.L79Cfs in Asians; p.W24X in Indians; p.L56Rfs in Americans; and the founder mutation p.R143W in Africans from Ghana, or with putative Ghanaian ancestry. The present review suggests that only GJB2 and GJB3 are recognized and validated HI genes. The findings call for an extensive investigation of the other connexin genes in many populations to elucidate their contributions to HI, in order to improve gene-disease pair curations, globally.

Keywords: GJB2; connexin; gap junction protein; gene variant; systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results”.

Figures

Figure 1
Figure 1
Geographical distributions of the studies included in this review. (A) A bar chart showing frequency of articles by the year of publication. (B) A pie chart of distribution of articles from which data were extracted by continent. (C) A map of countries showing the number studies that reported at least one connexin gene variant. The gray regions have no record included in this study. Different shades of blue were used to represent the number of studies retrieved and reviewed per country with the darkest shade of blue as the highest number and the lightest as the smallest number. The number written on the map denotes the number of studies. The map was created in Microsoft Excel (Office 365 education license under the University of Cape Town, South Africa) (D) Network of connexin gene plotted against continents from which they were reported. The nodes on the left (green) and the right (pink) correspond to connexin genes and continents respectively. The size of the nodes and the thickness of the lines between nodes are proportional to the number of publications. The network was built using the open-source software Gephi [22]
Figure 2
Figure 2
Methods used to investigate connexin gene variants. Among the methods are denaturing high-performance liquid chromatography (DHPLC), multiplex ligation-dependent probe amplification (MLPA), polymerase chain reaction (PCR), next-generation sequencing (NGS), restriction fragment length polymorphism (RFLP), and single-strand conformational polymorphism (SSCP).
Figure 3
Figure 3
Common GJB2 variants. (A) Clinical significance of identified variants. (B) The top eight GJB2 variants ranked based on the total number of alleles.
Figure 4
Figure 4
Global distribution of common GJB2 variants. A graph showing the total number of reported alleles of (A) p.Gly12ValfsTer2 (c.35delG), (B) p.M34T (c.101T > C), (C) p.L79Cfs/c.235delC, (D) p.V37I/c.109G > A, (E) p.H100RfsTer14/c.299_300delAT, (F) p.W24X/c.71G > A, (G) p.L56Rfs/c.167delT, and (H) p.R143W/c.427C > T. The countries were colored with a gradient from red (highest number of alleles) to brown (lowest number of alleles). Countries shaded grey either had no reports or no alleles. The map was created by the authors in Microsoft Excel (Office 365 education license of the University of Cape Town, South Africa).
Figure 5
Figure 5
Global distribution of del(GJB6-D13S1830). The variant del(GJB6-D13S1830) was reported in all the countries highlighted in blue color. The intensity of the blue color denotes the frequency of reported alleles. The map was created by the authors in Microsoft Excel (Office 365 education license of the University of Cape Town, South Africa).
Figure 6
Figure 6
Flow diagram illustrating the screening of articles obtained after the literature search.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bitner-Glindzicz M. Hereditary deafness and phenotyping in humans. Br. Med. Bull. 2002;63:73–94. doi: 10.1093/bmb/63.1.73. - DOI - PubMed
    1. James M., Kumar P., Ninan P. A study on prevalence and risk factors of hearing impairment among newborns. Int. J. Contemp. Pediatr. 2018;5:304–309. doi: 10.18203/2349-3291.ijcp20180018. - DOI
    1. Olusanya B.O., Neumann K.J., Saunders J.E. The global burden of disabling hearing impairment: A call to action. Bull. World Health Organ. 2014;92:367–373. doi: 10.2471/BLT.13.128728. - DOI - PMC - PubMed
    1. WHO Prevention of Blindness and Deafness. [(accessed on 30 March 2019)]; Available online: https://www.who.int/pbd/deafness/hearing_impairment_grades/en/
    1. Bayazit Y.A., Yılmaz M. An overview of hereditary hearing loss. ORL. 2006;68:57–63. doi: 10.1159/000091090. - DOI - PubMed
-