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Keywords:

  • Noise-induced hearing loss;
  • candidate gene;
  • association study;
  • SNP

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

Millions of people are daily exposed to high levels of noise. Consequently, noise-induced hearing loss (NIHL) is one of the most important occupational health hazards worldwide. In this study, we performed an association study for NIHL based on a candidate gene approach. 644 Single Nucleotide Polymorphisms (SNPs) in 53 candidate genes were analyzed in two independent NIHL sample sets, a Swedish set and part of a Polish set. Eight SNPs with promising results were selected and analysed in the remaining part of the Polish samples. One SNP in PCDH15 (rs7095441), resulted in significant associations in both sample sets while two SNPs in MYH14 (rs667907 and rs588035), resulted in significant associations in the Polish sample set and significant interactions with noise exposure level in the Swedish sample set. Calculation of odds ratios revealed a significant association of rs588035 with NIHL in the Swedish high noise exposure level group. Our studies suggest that PCDH15 and MYH14 may be NIHL susceptibility genes, but further replication in independent sample sets is mandatory.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

Since the industrial revolution, an increasing number of people are being exposed to extreme levels of noise. Noise is harmful starting from 85 dBA and can lead both to mechanical and metabolic damage of the cochlea (Lim, 1986; Borg et al., 1995). Single, repeated or continuous exposure to high levels of noise can cause Noise-Induced Hearing Loss (NIHL). Since millions of people are daily exposed to harmful levels of noise, NIHL is one of the most important occupational hazards. NIHL is a complex form of hearing loss, induced by an interaction between genetic and environmental factors. Beside noise, other environmental factors play a role in the development of NIHL. Chemicals like organic solvents, heat, vibrations and individual factors such as smoking, cholesterol level and blood pressure can augment the susceptibility to noise (Borg et al., 1995; Campo & Lataye, 2000; Toppila et al., 2000; Fechter, 2004; Sliwinska-Kowalska et al., 2004; Ni et al., 2007).

A formal heritability study for NIHL in humans has not yet been performed, but the role of genetic factors in NIHL was confirmed by several animal studies. Mouse strains exhibiting age-related hearing impairment were more susceptible to noise than other mouse strains (Erway et al., 1996; Davis et al., 2001; Harding et al., 2005). Also, several knock-out mice, like PMCA2−/− (Kozel et al., 2002), CDH23+/− (Holme & Steel, 2004), SOD1−/− (Ohlemiller et al., 1999) and GPX1−/− (Ohlemiller et al., 2000), were more susceptible to noise than their wild-type littermates.

Since the development of high throughput genotyping technologies and the increasing knowledge on single nucleotide polymorphisms (SNPs) and their characteristics in the human genome as established by the HapMap project (http://www.hapmap.org), whole genome association studies (WGAS) are increasingly used to identify susceptibility genes involved in complex diseases. However, WGAS remain rather expensive. Therefore, a candidate gene approach to identify susceptibility genes is still often pursued. Many genes are excellent candidate genes for NIHL. A number of association studies on candidate susceptibility genes have been performed, studying several oxidative stress genes (GSTM1, GSTT1, PON1, PON2, SOD2, GSTP, CAT, SOD, GPX1 and GSR) (Rabinowitz et al., 2002; Fortunato et al., 2004; Carlsson et al., 2005; Konings et al., 2007) and K-recycling pathway genes (GJB1, GJB2, GJB3, GJB4, GJB6, KCNJ10, KCNQ1, KCNQ4, KCNE1 and SLC12A2) (Carlsson et al., 2004; Van Laer et al., 2006; Van Eyken et al., 2007). Three of these failed to detect significant associations with NIHL (Carlsson et al., 2004, 2005; Van Eyken et al., 2007). Other association studies identified KCNE1 (Van Laer et al., 2006), and GSTM1 (Rabinowitz et al., 2002) and CAT (Konings et al., 2007) as possible NIHL susceptibility genes.

In this study, an association study was performed on 53 candidate genes of NIHL using two independent populations of noise-exposed workers, one Swedish and one Polish. Genes that play a role in the inner ear as well as all genes causing nonsyndromic or syndromic forms of hearing loss in humans or mice, were analyzed.

Material and Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

Samples

Swedish population

A detailed description of the Swedish sample population can be found elsewhere (Carlsson et al., 2004, 2005). In brief, 1261 male noise-exposed workers from two paper pulp mills and one steel factory in the mid-western part of Sweden were divided into 9 categories (three age ranges, below 35, 35 to 50 and above 50 years, and three occupational noise exposure categories, ≤85dBA, 86–91dBA and ≥92dBA, all leq (Equivalent Continuous Noise Level), 8h, 5 days a week). This division was based on noise measurement data at each workstation from the three factories collected and updated for over 30 years. From each category, the 10% most resistant and the 10% most sensitive individuals were selected using the hearing threshold level (HTL) of the left ear at 3 kHz as a measure of noise susceptibility. The left ear was chosen since this is most affected by NIHL. 3 kHz was preferred for the selection of susceptible individuals over 4 or 6 kHz for several reasons. Firstly, an increase in damage leads to a widening of the initial 4–6 kHz notch to lower frequencies (ISO 1999 – International Organization for Standardization, 1990). Furthermore, the HTL at 3 kHz continues to increase over a longer period of time than does the HTL at 4 and 6 KHz (Taylor et al., 1965) and the majority of the Swedish subjects (79%) had been exposed to noise for 20 to 30 years or more. In addition, the ISO 1999 norm shows that individuals who have been exposed to noise (≥90dBA) for 20 years or more have a higher HTL at 3 kHz than at 4 and 6 kHz in the 0.1 fractile. Blood samples were taken from a total of 215 subjects; 103 noise susceptible and 112 noise resistant subjects.

Polish population

Information concerning the audiometric status, noise exposure, and exposure to chemicals was gathered from 3860 Polish workers from different industries, including a coal mine, an electric power station, a dockyard, a glass bottle factory and a lacquer and paint factory. An inclusion criterion for this study was an exposure to noise of at least 3 years. Subjects with a history of middle ear disease, conductive hearing loss or skull trauma, and subjects with a family history of hearing loss were excluded. Among the Polish workers only a minority had been exposed to noise during 20 years or more, which is in contrast with the situation among the Swedish workers. Therefore it was decided to evaluate the HTLs at 4 and 6 kHz instead of 3 kHz since 4 and 6 kHz are most easily affected by NIHL. In former genetic studies on these noise-exposed workers, the 10% most resistant and sensitive subjects were selected using a Z-score based on the ISO 1999 (ISO 1999 – International Organization for Standardization, 1990) (Sliwinska-Kowalska et al., 2006), leading to a selection of 347 sensitive and 338 resistant subjects (Konings et al., 2007). Age and noise exposure level in this initial selection were not equally distributed between resistant and sensitive workers. This resulted in significant effects of the interaction between age and noise exposure level and quadratic effects of age and noise exposure level. The statistical analysis that was applied corrected for these differences and interactions in age and noise exposure between resistant and sensitive workers (Konings et al., 2007). Although this approach was valid, it led to a complex statistical model of which the results were difficult to interpret. That is why we opted to perform a stricter and matched selection of resistant and sensitive subjects. Additional exclusion criteria were applied, such as exclusion of female subjects, exclusion of subjects with a history of extended treatment with aminoglycosides, the duration of exposure to noise had to exceed [(age * 0.666) – 20 years] and finally, no subjects were included that had been exposed to noise in a previous workplace longer than 15 years. Next, the remaining population of 3390 Polish workers was divided into 9 categories (three years of exposure ranges, below 15, 15 to 25 and above 25 years, and three occupational noise exposure categories, ≤85 dBA, 86–91 dBA and ≥92 dBA, all leq, 8h, 5 days a week). Subsequently, the mean left ear HTL at 4 and 6 kHz was calculated for each subject. From each category, the 20% most resistant and the 20% most sensitive persons were selected. At this level, additional exclusion criteria were applied to the sensitive subjects only. These could not have a history of meningitis, acoustic trauma or aminoglycoside treatment or an asymmetry between the right and left ear of more than or equalling 40 dB. Finally, they should not have been exposed to noise in a previous workplace for more than 5 years. For genetic analysis, we were limited to the subjects from whom DNA was collected, which are the subjects who had been selected as resistant or sensitive subjects using the previous selection procedure based on the ISO 1999. For each resistant subject, a sensitive subject for factory and age (+/− 10 years), was matched. This selection procedure resulted in 119 pairs of samples, 119 resistant and 119 sensitive subjects.

Candidate Gene and SNP Selection

Candidate gene and SNP selection was described elsewhere (Van Laer et al., 2008). Briefly, a list of candidate genes was generated based on the literature and information available on several websites (the Hereditary Hearing Loss website: http://webh01.ua.ac.be/hhh/; the Jackson Laboratories Hereditary Hearing Impairment in Mice website: http://www.jax.org/hmr/; the Sanger Institute Deaf Mouse Mutants website: http://www.sanger.ac.uk/PostGenomics/mousemutants/deaf/). From the list of age-related hearing impairment candidate genes used by Van Laer et al., (2008) we had already investigated the oxidative stress genes (Carlsson et al., 2005; Konings et al., 2007) and the genes of the potassium recycling pathway (unpublished results) in previous studies. Therefore, we restricted our current analysis to the 53 remaining genes. An overview of these genes can be found in Tables 1 and 2. SNPs were selected in the candidate genes and in the region 3000 bp upstream of the genes (putatively containing regulatory elements). Based on the HapMap data for these regions (release #16; http://www.hapmap.org), MARKER (http://www.gmap.net/marker) was used to select the tag SNPs. In addition, SNPs that were putatively functionally important were selected from dbSNP (http://www.ncbi.nlm.nih.gov), the SNPeffect database (http://snpeffect.vib.be), the Genetic Association database (http://geneticassociationdb.nih.gov), and the ABI SNP database (http://appliedbiosystems.com). A list of 644 SNPs was obtained. The exact number of selected SNPs per gene is shown in Table 1. A classification of the selected SNPs is given in Table 3.

Table 1.  Candidate genes and number of selected SNPs
Gene symbolAliasGene descriptionCategorya# SNPs
  1. aFunctional: genes that have a known function in the inner ear. Non-syndromic: genes leading to both syndromic and non-syndromic human hearing loss were classified as non-syndromic genes. Mouse: genes in which mutations cause hearing loss in mice (no human counterpart has been identified yet). Expression: genes that are expressed in the inner ear. Syndromic: gene that causes syndromic hearing loss.

ACCN3DRASICAmiloride-sensitive cation channel 3Functional2
ACTG1 Cytoplasmic actin gammaNon-syndromic1
BARHL1 Barh-like 1Mouse3
BDNF Brain-derived neurotrophic factorMouse6
CDH23 Cadherin 23Non-syndromic63
CHRNB2EFNL3Cholinergic receptor, nicotinic, beta polypeptide 2Functional2
CLDN14 Claudin 14Non-syndromic8
COCH Coagulation factor C homologNon-syndromic8
COL11A2 Procollagen, type XI, alpha 1Non-syndromic7
CRYM Crystallin, muNon-syndromic4
DFNA5 Deafness, autosomal dominant 5Non-syndromic12
DIAPH1 Diaphanous homolog 1Non-syndromic8
ESPN EspinNon-syndromic5
EYA4 Eyes absent 4 homologNon-syndromic25
FIGN FidgetinMouse2
GR Glucocorticoid receptorFunctional3
GRHL2TFCP2L3/BOMGrainyhead like 2 / transcription factor CP2-like3Non-syndromic26
ITGA8 Integrin, alpha 8Mouse21
KCNMA1BKTM/KCa1.1/BKa1Alfa subunit of calcium-activated potassium channelFunctional81
MTHFR 5,10-methylenetetrahydrofolate reductase (NADPH)Functional2
MYH14 Non-muscle myosin heavy chain 14Non-syndromic17
MYH9 Non-muscle myosin heavy chain 9Non-syndromic15
MYO15a Myosin XVNon-syndromic10
MYO1A Myosin IANon-syndromic7
MYO3A Myosin IIIANon-syndromic12
MYO6 Myosin VINon-syndromic12
MYO7A Myosin VIIANon-syndromic14
OCP2SKP1Organ of Corti protein 2Expression3
OTOA OtoancorinNon-syndromic10
OTOF OtoferlinNon-syndromic16
OTOG OtogelinMouse11
OTOR OtoraplinExpression2
OTOS OtospiralinFunctional2
PCDH15 Protocadherin 15Non-syndromic73
PMCA2ATP2B2Plasma membrane calcium atpase type 2Mouse17
POU4F3BRN3CPOU domain, class 4, transcription factor 3Non-syndromic2
RDX RadixinMouse4
SLC26A4PDSPendrin/solute carrier family 26 member 4Non-syndromic7
SLC26A5PRESPrestin / solute carrier family 26 member 5Non-syndromic11
STRC StereocilinNon-syndromic2
TECTA Tectorin alphaNon-syndromic13
TGFb1 Transforming growth factor beta 1Functional6
TMC1 Transmembrane cochlear-expressed gene 1Non-syndromic15
TMIE Trans membrane inner earNon-syndromic3
TMPRSS3 Transmembrane protease, serine 3Non-syndromic13
TRPA1ANKTM1Transient receptor potential cation channel A1Functional13
UCN UrocortinMouse1
USH1C HarmoninNon-syndromic10
USH1GSANSScaffold protein containing ankyrin repeats and SAM domainSyndromic1
VEGF Vascular endothelial growth factorFunctional8
VEZATIN Transmembrane protein vezatinFunctional4
WFS1 WolframinNon-syndromic8
WHRN WhirlinNon-syndromic13
Total   644
Table 2.  Classification of candidate genes
CategoryNumber of genes
  1. aGenes leading to both syndromic and non-syndromic hearing loss were classified as non-syndromic genes.

Non-syndromica32
Functional10
Mouse8
Syndromic1
Expression2
Total53
Table 3.  Classification of the selected SNPs
Category# SNPsPercentage (%)
Intronic53382,8
UTR182,8
Locus274,2
Exon/synonymous111,7
Exon/non-synonymous558,5
Total644100,0

SNP Genotyping

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

Assay design and genotyping of all SNPs on all selected samples were performed by Illumina (San Diego, CA, USA). For each population, four samples were genotyped in duplicate.

Eight SNPs were selected for further analysis. Of these eight SNPs, four SNPs were analyzed using the melting-temperature-shift method (Tm-shift method) (Wang et al., 2005). In brief, two GC-rich tails of different length were attached to allele-specific PCR primers. In this way, SNP alleles could be discriminated by the Tm-shift of the resulting PCR products. Primers are listed in Table 4. A 10 μl reaction consisted of 5ng DNA, 1x Colorless GoTaq® Reaction Buffer (Promega, Madison, WI, USA), 0.8mM dNTPs (Promega), 2 μM of allele-specific primer 1, 0.5 μM of allele-specific primer 2, 1 μM of common primer, 0.2 U GoTaq® DNA Polymerase (Promega) and 0.2x SYBR Green (Molecular probes, Invitrogen™, San Diego, CA, USA). The PCR reaction was initiated by 1 min activation at 95°C, followed by 40 cycles of 5 seconds denaturation at 95°C, 15 seconds annealing at 58°C and 10 seconds extension at 72°C and ended with 10 seconds at 95°C, 10 seconds at 45°C and a melting curve analysis from 46°C to 95°C with 5 data acquisitions per degree Celcius. PCR reaction and melting curve analysis were performed on a Roche LightCycler®480 system (Roche, Basel, Switzerland). The remaining four SNPs were genotyped using the SNaPshot™ Detection Method (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions and as described previously (Konings et al., 2007).

Table 4.  Primer and PCR conditions of Tm-shift and SNaPshot assays
SNPGeneNucleotide changeAnalysis methodPrimerPrimer sequenceaPCR annealing temperature (°C)
  1. aGC-clamps are indicated in bold

rs1981361GRHL2A/GTm-shiftAllele-specific primer 1GCGGGCCTCAGAGGCCTAGCGACT58
Allele-specific primer 2GCGGGCAGGGCGGCCTCAGAGGCCTAGCGACC 
Common primerGGGGCAGATGGGAAATGCAC 
rs10508489ITGA8C/TSNaPshotForward PCR primerCTAGAAAGTAATATAAAGTCTCCATC56
Reverse PCR-primerCATGTCTCATTACCGTTGGGAG 
SNP-primerCCATCACAACTAGATTGTCATTATA 
rs1436089KCNMA1A/GTm-shiftAllele-specific primer 1GCGGGCGGGGTTACGATTGGAAAATGA58
Allele-specific primer 2GCGGGCAGGGCGGCGGGGTTACGATTGGAAAATGG 
Common primerGGTGAGTGGATGCAAAAGTAACAT 
rs667907MYH14C/TSNaPshotForward PCR primerGCAGAAGTGAGATGAGTTATTTG56
Reverse PCR-primerGGTTTTTGGATGACAAAATATACGA 
SNP-primerGAGTTATTTGATTTAATTCTCATAAGCAA 
rs588035MYH14C/GSNaPshotForward PCR primerGATATGTGTGTGGGTTAATGGGTC56
Reverse PCR-primerCAGCCAAACCATCTTCCTCCC 
SNP-primerCATGGCAGGTTAATGGGTTGG 
rs7095441PCDH15C/TTm-shiftAllele-specific primer 1GCGGGCCATTCCTTCTTATAAATGACCTA58
Allele-specific primer 2GCGGGCAGGGCGGCCATTCCTTCTTATAAATGACCTG 
Common primerTTTTCACTTTCTACTATAGTCATAAAC 
rs11004085PCDH15C/TTm-shiftAllele-specific primer 1GCGGGCTATCCAAAGTAATCCTTGTAATAT58
Allele-specific primer 2GCGGGCAGGGCGGCTATCCAAAGTAATCCTTGTAATAC 
Common primerGTGAATCATAGGCACTGCATCG 
rs891969POU4F3A/GSNaPshotForward PCR primerCCACCAAAAAAATTTTGGCGCTG56
Reverse PCR-primerGGCAAATTTCAGCACCACGGAC 
SNP-primerGCTGGGAAAATATTGCAGAAGGGC 

Data Polishing

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

A detailed description of the data polishing process is described elsewhere (Van Laer et al., 2008). In brief, samples with 10% or more and SNPs with 4% or more missing genotypes were removed. Hardy-Weinberg equilibrium was checked on all approved SNPs and all approved samples by a χ2 test for goodness-of-fit using a significance level of 0.001. Subsequently, genetic outliers were detected and removed using CHECKHET (Curtis et al., 2002) and GRR (Graphical Representation of Relationship errors) (Abecasis et al., 2001) (http://bioinformatics.well.ox.ac.uk/GRR). The cut off value for exclusion was 1.75 on a scale of 2.0 identical by state.

Statistical Analysis

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

The Polish matched sample set was analyzed using conditional stepwise backward logistic regression. For the Swedish sample set, regular stepwise backward logistic regression was performed. In both methods, the affection status (resistant versus sensitive) was regressed on the genotypes and the interaction of the genotype with noise exposure level, while correcting for age and noise exposure level. We assumed an additive genetic model where counts of a reference allele were treated as a continuous covariate. In the second phase of the study, the statistical analysis of the eight selected SNPs was performed on the total set of Polish samples in order to obtain the highest statistical power possible. This method was preferred since it has been shown that a joint analysis of the data from both phases of a two-stage association study almost always results in an increased power to detect genetic association (Skol et al., 2006). All statistical analyses were performed using SAS (SAS 9.1.3 for Windows, SAS Institute Inc., Cary, NC, USA). If a SNP showed a significant interaction between genotype and noise exposure level, odds ratios were calculated for the three different noise exposure levels.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

In this study, 644 SNPs in 53 genes were genotyped in a Swedish and a Polish sample set. In the first phase of the study, all the Swedish samples and 104 Polish samples were analyzed. Twelve Swedish samples, but none of the Polish samples, were excluded as genetic outliers based on a CHECKHET and GRR analysis. Of the 644 SNPs, 37 could not be genotyped by Illumina and were excluded. Results of the analysis of the remaining 607 SNPs can be found in Table S1. Seven SNPs which resulted in significant associations (P-value < 0.05) or interactions in both sample sets, or which were significant in one sample set and showed a trend towards significance (P-value < 0.1) in the other sample set, were selected. In addition, one SNP with a P-value of 0.150 was selected. This SNP was located in the GRHL2 gene. This gene was of special interest since it has been shown to be involved in age-related hearing loss (Van Laer et al., 2008). These eight SNPs (rs1981361, rs10508489, rs1436089, rs667907, rs588035, rs7095441, rs11004085 and rs891969) were genotyped in the remaining 134 Polish samples that were not used in the first phase (Table 5). For the analysis of these eight SNPs in the Polish sample set, the results of the first and of the second phase of the study were combined. Three of these eight SNPs (rs7095441, rs667907 and rs588035), were significantly associated with NIHL both in the total Polish and in the Swedish sample set. Two of these three SNPs were located in the same gene (MYH14). Rs7095441 of PCDH15 resulted in a significant P-value of 0.026 in the Polish sample set and of 0.001 in the Swedish sample set. Most interestingly, the odds ratios for this SNP pointed in the same direction for these two sample sets (Polish sample set OR = 1.666; 95% CI = 1.063–2.610; Swedish sample set OR = 2.076; 95% CI = 1.344–3.206). Rs667907 in MYH14 showed a P-value of 0.009 in the Polish sample set while a significant interaction P-value (0.049) was observed in the Swedish sample set. Rs588035, also in MYH14, resulted in a significant P-value of 0.021 in the Polish sample set and a significant interaction P-value of 0.012 in the Swedish sample set. A significant interaction P-value indicated that a significant difference in genotype distribution was observed between sensitive and resistant persons for different noise exposure levels. In other words, a differential effect of the genotype on the noise sensitivity according to the noise exposure level may exist. Subsequently, the odds ratios were calculated for three different noise exposure level categories (low: ≤85 dBA, mid: 86–91 dBA and high: ≥92 dBA) for these two SNPs in the Swedish sample set. For rs667907, none of the odds ratios for the different noise exposure levels were significant, while for rs588035 a significant odds ratio (OR = 0.399; 95% CI = 0.180–0.886) was obtained for the high noise exposure level category. Moreover in the Polish sample set, an odds ratio that pointed in the same direction (OR = 0.570; 95% CI = 0.353–0.918) was observed for this latter SNP.

Table 5.  Results of the analysis of the selected SNPs in the total Polish and Swedish sample set
SNPGeneSwedenPoland
MAFP-valueP-value genotype* exposure levelOR (95% CI)MAFP-valueOR (95% CI)
  1. P-values that are significant at the 5% level are indicated in bold. All results are adjusted for age and noise exposure level.

  2. The interaction P-value (genotype* exposure level) is only listed if it was significant. Only when this interaction P-value was significant, ORs were calculated in the different noise exposure categories.

rs1981361GRHL20,3820,045  0,3870,128 
rs10508489ITGA80,1110,033  0,0680,051 
rs1436089KCNMA10,182-0,012 0,1660,707 
rs667907MYH140,317-0,049Low: 0,481 (0,212–1,094)0,3100,0091,828 (1,162–2,875)
Mid: 0,916 (0,595–1,411)   
High: 1,743 (0,848–3,583)   
rs588035MYH140,291-0,012Low: 2,318 (0,996–5,392)0,2400,0210,570 (0,353–0,918)
Mid: 0,962 (0,615–1,505)   
-High: 0,399 (0,180–0,886)   
rs7095441PCDH150,3890,001 2,076 (1,344–3,206)0,4160,0261,666 (1,063–2,610)
rs11004085PCDH150,3330,003  0,3310,380 
rs891969POU4F30,259-0,014 0,1670,134 

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

We performed an association study for NIHL based on a candidate gene approach. In a first phase, 644 SNPs were genotyped in a Swedish sample set and a part of the Polish sample set. Eight SNPs were subsequently further analyzed on the remaining Polish samples. Three of these eight SNPs resulted in significant associations in the total Polish and the Swedish sample set. For one SNP, rs7095441 located in PCDH15 (protocadherin 15), the directions of the odds ratios of the Swedish and Polish sample set pointed in the same direction. For the two other SNPs, rs667907 and rs588035, both located in MYH14 (myosin, heavy chain 14), odds ratios were calculated for the three different noise exposure levels in the Swedish sample set since a significant interaction P-value was obtained. This analysis resulted in a significant odds ratio for the high noise exposure level group for SNP rs588035. Interestingly, this odds ratio pointed in the same direction as the odds ratio calculated in the Polish sample set. Therefore, both the PCDH rs7095441 and the MYH14 rs588035 SNPs replicate in two independent sample sets. Replication in independent sample sets is crucial to confirm susceptibility genes for complex diseases. Moreover, replication of findings in several independent sample sets is nowadays believed to be more important than obtaining highly significant P-values (Neale & Sham, 2004). In our current study, replication of a finding was the main criterion; only findings that resulted in significant associations in both sample sets were considered of interest. Applying this criterion of replication may lead to more reliable association results and also circumvents, in our opinion, the problem of multiple testing.

Beside the three SNPs mentioned above, several other SNPs were significantly associated in one of the two sample sets. These differences in significance that we obtained in the two independent sample sets may be due to false positive association signals or may be due to population differences, such as differences in the allele frequencies of the causative variants or in the LD pattern of the associated region. Replication of these results in additional noise-exposed populations is necessary in order to determine whether some of these signals may represent true positive associations.

A significant interaction term between genotypes of a SNP and noise exposure level indicates that the same genotype will have a differential effect on the susceptibility to NIHL depending on the noise exposure level. In other words, a specific SNP genotype may render a subject more resistant or more susceptible to NIHL depending on the noise exposure level. We have observed this remarkable finding before when we investigated the association between CAT genotypes and NIHL (Konings et al., 2007). Moreover, association studies to determine the genetic factors involved in asthma also reported a similar observation (Hoffjan et al., 2005; Ober, 2005). As hypothesized in these studies and possibly also in our study, an environmental factor may have influenced the results leading to the observed opposite effect. However, it is also possible that a specific genotype may result in better hearing abilities under normal conditions (i.e. under low noise exposure), while this same genotype may make people more susceptible to hearing loss under conditions of high noise exposure. Alternatively, this observation may be the result of sound conditioning. Moderate levels of noise exposure may protect the ear from a subsequent high-level exposure (Campo et al., 1991; Canlon et al., 1988). Possibly, certain genotypes might have different effects on noise susceptibility depending on whether the subject has been conditioned to noise or not. In addition, in rabbits, short-term high level noise exposure resulted in more inner ear cell damage while for long-term low level noise exposure, more outer hair cell damage was observed (Borg et al., 1995). Also this observation might explain the opposite genotype effect between the different noise exposure levels.

PCDH15 is a member of the cadherin superfamily encoding a protein that mediates calcium-dependent cell-cell adhesion. It plays an essential role in the maintenance of normal retinal and cochlear function. Mutations in this gene have been associated with both non-syndromic (DFNB23, Ahmed et al., 2003) and syndromic hearing loss (Usher syndrome type 1F, USH1F, Alagramam et al., 2001; Ahmed et al., 2001). Rs7095441 is located in intron 15, in a region with high LD of 71 kb. One other SNP (rs7093558) that was analyzed in the first phase of our study, was also located in this region but did not result in a significant association. This can be caused by a number of reasons and therefore, additional studies using independent noise-exposed sample sets are necessary to draw a definitive conclusion about PCDH15.

MYH14 encodes a member of the myosin superfamily. This family comprises actin-dependent motor proteins with diverse functions including regulation of cytokinesis, cell motility and cell polarity. Mutations in MYH14 result in a form of autosomal dominant hearing impairment, DFNA4 (Donaudy et al., 2004; Chen et al., 1995). Rs667907 and rs588035 are located in intron 32 and intron 34, respectively, in a region with high LD of 19 kb. Because of this LD, it is not unexpected that both SNPs lead to significant results. In the Swedish sample set significant interactions were observed for both SNPs, but only the odds ratio for the high noise exposure level for SNP rs588035 was significant. None of the odds ratios for rs667907 were significant. It is too early to categorize this gene as a definitive NIHL susceptibility gene. First, additional replication studies are needed.

In conclusion, in this study an association study with 53 candidate genes was performed in two independent noise-exposed populations. For two genes, PCDH15 and MYH14, suggestive results were obtained but these should be replicated in additional independent sample sets before they can definitively be categorized as NIHL susceptibility genes.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

This research was supported by a grant of the British Royal Institute for Deaf and Hard of Hearing People (RNID) to G.V.C. and L.V.L. and a TOP grant of the University of Antwerp to G.V.C.

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  10. Acknowledgements
  11. References
  12. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Material and Methods
  5. SNP Genotyping
  6. Data Polishing
  7. Statistical Analysis
  8. Results
  9. Discussion
  10. Acknowledgements
  11. References
  12. Supporting Information

Table S1 Results of the analysis of 621 SNPs in the Swedish sample set and in 104 subjects of the Polish sample set

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AHG_499_sm_TableS1.xls118KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.