Comprehensive functional network analysis and screening of deleterious pathogenic variants in non-syndromic hearing loss causative genes

Hearing loss (HL) is a significant public health problem and causes the most frequent congenital disability in developed societies. The genetic analysis of non-syndromic hearing loss (NSHL) may be considered as a complement to the existent plethora of diagnostic modalities available. The present study focuses on exploring more target genes with respective non-synonymous single nucleotide polymorphisms (nsSNPs) involved in the development of NSHL. The functional network analysis and variant study have successfully been carried out from the gene pool retrieved from reported research articles of the last decade. The analyses have been done through STRING. According to predicted biological processes, various variant analysis tools have successfully classified the NSHL causative genes and identified the deleterious nsSNPs, respectively. Among the predicted pathogenic nsSNPs with rsIDs rs80356586 (I515T), rs80356596 (L1011P), rs80356606 (P1987R) in OTOF have been reported in NSHL earlier. The rs121909642 (P722S), rs267606805 (P722H) in FGFR1, rs121918506 (E565A) and rs121918509 (A628T, A629T) in FGFR2 have not been reported in NSHL yet, which should be clinically experimented in NSHL. This also indicates this variant’s novelty as its association in NSHL. The findings and the analyzed data have delivered some vibrant genetic pathogenesis of NSHL. These data might be used in the diagnostic and prognostic purposes in non-syndromic congenitally deaf children.     

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

Gene variant discovery is becoming routine, but it remains difficult to usefully interpret the functional consequence or disease relevance of most variants. To fill this interpretation gap, experimental assays of variant function are becoming common place. Yet, it remains challenging to make these assays reproducible, scalable to high numbers of variants, and capable of assessing defined gene-disease mechanism for clinical interpretation aligned to the ClinGen Sequence Variant Interpretation (SVI) Working Group guidelines for ‘well-established assays’. Drosophila melanogaster offers great potential as an assay platform, but was untested for high numbers of human variants adherent to these guidelines. Here, we wished to test the utility of Drosophila as a platform for scalable well-established assays. We took a genetic interaction approach to test the function of ~100 human PTEN variants in cancer-relevant suppression of PI3K/AKT signaling in cellular growth and proliferation. We validated the assay using biochemically characterized PTEN mutants as well as 23 total known pathogenic and benign PTEN variants, all of which the assay correctly assigned into predicted functional categories. Additionally, function calls for these variants correlated very well with our recent published data from a human cell line. Finally, using these pathogenic and benign variants to calibrate the assay, we could set readout thresholds for clinical interpretation of the pathogenicity of 70 other PTEN variants. Overall, we demonstrate that Drosophila offers a powerful assay platform for clinical variant interpretation, that can be used in conjunction with other well-established assays, to increase confidence in the accurate assessment of variant function and pathogenicity.     

Predicting Mendelian Disease-Causing Non-Synonymous Single Nucleotide Variants in Exome Sequencing Studies

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ∼22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.     

Estimation of the number of SNP genetic markers required for parentage verification

Formulae were developed to compute exclusion probabilities for parentage confirmation for any number of diallelic markers under the assumption that the minor allele frequency (MAF) varied among markers, but has a uniform distribution. Three scenarios were analysed: a progeny with (1) a single putative parent; (2) two putative parents; and (3) one actual parent and one putative parent. Exclusion probabilities were computed for minimum values for the MAFs of 0.1, 0.2 and 0.3, and required either one or at least two conflicts for exclusion. The numbers of markers required to obtain 99% exclusion probabilities based on a single conflict for the three minimum MAFs were 54, 45 and 39 for scenario 1; 17, 16 and 15 for scenario 2; and 28, 25 and 24 for scenario 3. The requirement of at least two conflicts for exclusion increased the number of markers required by approximately 45% for all three scenarios and all three minimum MAFs. The results obtained by the analytical formulae were very close to results obtained by simulation and to values in the literature for specific marker sets.     

Cohen’s h for detection of disease association with rare genetic variants

Background

The power of the genome wide association studies starts to go down when the minor allele frequency (MAF) is below 0.05. Here, we proposed the use of Cohen’s h in detecting disease associated rare variants. The variance stabilizing effect based on the arcsine square root transformation of MAFs to generate Cohen’s h contributed to the statistical power for rare variants analysis. We re-analyzed published datasets, one microarray and one sequencing based, and used simulation to compare the performance of Cohen’s h with the risk difference (RD) and odds ratio (OR).

Results

The analysis showed that the type 1 error rate of Cohen’s h was as expected and Cohen’s h and RD were both less biased and had higher power than OR. The advantage of Cohen’s h was more obvious when MAF was less than 0.01.

Conclusions

Cohen’s h can increase the power to find genetic association of rare variants and diseases, especially when MAF is less than 0.01.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-875) contains supplementary material, which is available to authorized users.     

Molecular analysis of the TMPRSS3 gene in Moroccan families with non-syndromic hearing loss

Autosomal recessive non-syndromic hearing impairment (ARNSHI) is the most common type of inherited hearing impairment, accounting for approximately 80% of inherited prelingual hearing impairment. Hearing loss is noted to be both phenotypically and genetically heterogeneous. Mutations in the TMPRSS3 gene, which encodes a transmembrane serine protease, are known to cause autosomal recessive non-syndromic hearing impairment DFNB8/10. In order to elucidate if the TMPRSS3 gene is responsible for ARNSHI in 80 Moroccan families with non-syndromic hearing impairment, the gene was sequenced using DNA samples from these families. Nineteen TMPRSS3 variants were found, nine are located in the exons among which six are missense and three are synonymous. The 10 remaining variations are located in non-coding regions. Missense variants analysis show that they do not have a significant pathogenic effect on protein while pathogenicity of some variant remains under discussion. Thus we show that the TMPRSS3 gene is not a major contributor to non-syndromic deafness in the Moroccan population.     

Rare and common variants in ROM1 and PRPH2 genes trans-modify Stargardt/ABCA4 disease

Over 1,500 variants in the ABCA4 locus cause phenotypes ranging from severe, early-onset retinal degeneration to very late-onset maculopathies. The resulting ABCA4/Stargardt disease is the most prevalent Mendelian eye disorder, although its underlying clinical heterogeneity, including penetrance of many alleles, are not well-understood. We hypothesized that a share of this complexity is explained by trans-modifiers, i.e., variants in unlinked loci, which are currently unknown. We sought to identify these by performing exome sequencing in a large cohort for a rare disease of 622 cases and compared variation in seven genes known to clinically phenocopy ABCA4 disease to cohorts of ethnically matched controls. We identified a significant enrichment of variants in 2 out of the 7 genes. Moderately rare, likely functional, variants, at the minor allele frequency (MAF) <0.005 and CADD>25, were enriched in ROM1, where 1.3% of 622 patients harbored a ROM1 variant compared to 0.3% of 10,865 controls (p = 2.41E04; OR 3.81 95% CI [1.77; 8.22]). More importantly, analysis of common variants (MAF>0.1) identified a frequent haplotype in PRPH2, tagged by the p.Asp338 variant with MAF = 0.21 in the matched general population that was significantly increased in the patient cohort, MAF 0.25, p = 0.0014. Significant differences were also observed between ABCA4 disease subgroups. In the late-onset subgroup, defined by the hypomorphic p.Asn1868Ile variant and including c.4253+43G>A, the allele frequency for the PRPH2 p.Asp338 variant was 0.15 vs 0.27 in the remaining cohort, p = 0.00057. Known functional data allowed suggesting a mechanism by which the PRPH2 haplotype influences the ABCA4 disease penetrance. These associations were replicated in an independent cohort of 408 patients. The association was highly statistically significant in the combined cohorts of 1,030 cases, p = 4.00E-05 for all patients and p = 0.00014 for the hypomorph subgroup, suggesting a substantial trans-modifying role in ABCA4 disease for both rare and common variants in two unlinked loci.     

How to assess the pathogenicity of mutations in Charcot-Marie-Tooth disease and other diseases?

Knowledge whether a certain DNA variant is a pathogenic mutation or a harmless polymorphism is a critical issue in medical genetics, in which results of a molecular analysis may serve as a basis for diagnosis and genetic counseling. Due to its genetic heterogeneity expressed at the levels of loci, genes and mutations, Charcot-Marie-Tooth (CMT) disease can serve as a model group of clinically homogenous diseases for studying the pathogenicity of mutations. Close to a 17p11.2-p12 duplication occurring in 70% of patients with the demyelinating form of CMT disease, numerous mutations have been identified in poorly characterized genes coding for proteins of an unknown function. Functional analyses, segregation analyses of large pedigrees, and inclusion of large control groups are required to assess the potential pathogenicity of CMT mutations. Hence, the pathogenicity of numerous CMT mutations remains unclear. Some variants detected in the CMT genes and originally described as pathogenic mutations have been shown to have a polymorphic character. In contrast, polymorphisms initially considered harmless were later reclassified as pathogenic mutations. However, the process of assessing the pathogenicity of mutations, as presented in this study for CMT disorders, is a more general issue concerning all disorders with a genetic background. Since the number of DNA variants is still growing, in the near future geneticists will increasingly have to cope with the problem of pathogenicity of identified genetic variants.     

Minor allele frequency thresholds strongly affect population structure inference with genomic data sets

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure—often the first step in analysis of population genomic data. We find that model‐based inference of population structure is confounded when singletons are included in the alignment, and that both model‐based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.     

Mutation analysis of mitochondrial DNA 12SrRNA and tRNASer(UCN) genes in non-syndromic hearing loss patients

Specific mitochondrial DNA (mtDNA) mutations in 12SrRNA and tRNASer(UCN) cause non-syndromic hearing loss (NSHL). In this study, we screened 466 hearing loss (HL) patients, negative for GJB2 mutations, for mutations in the two mtDNA genes and flanking regions. In total, 43 different variants were identified, 31 of which were polymorphisms, one was a mutation (m.1555A-->G), two were known variants of controversial pathological nature (m.827A-->G and m.961delTinsC(n)) and nine were newly identified variants. The frequency of m.1555A-->G in this set of HL patients was 0.3%, which was lower than expected. To assess the putative causative nature of controversial or newly identified variants, the frequencies of these variants were determined in 400 Belgian control subjects, and their effect on the secondary structure and their conservation among different species was determined. Our data provide further support for a polymorphic nature of the controversial m.961delTinsC(n) variant. In addition, two of the newly identified variants, m.636A-->G in the 12SrRNA flanking tRNA(Phe) and m.990T-->C in 12SrRNA, may be new candidates for pathogenic HL variants. If the pathogenic nature of m.636A-->G can be confirmed, this would be the first NSHL mutation in tRNA(Phe).     

Diagnostic Application of Targeted Resequencing for Familial Nonsyndromic Hearing Loss

Identification of causative genes for hereditary nonsyndromic hearing loss (NSHL) is important to decide treatment modalities and to counsel the patients. Due to the genetic heterogeneity in sensorineural genetic disorders, the high-throughput method can be adapted for the efficient diagnosis. To this end, we designed a new diagnostic pipeline to screen all the reported candidate genes for NSHL. For validation of the diagnostic pipeline, we focused upon familial NSHL cases that are most likely to be genetic, rather than to be infectious or environmental. Among the 32 familial NSHL cases, we were able to make a molecular genetic diagnosis from 12 probands (37.5%) in the first stage by their clinical features, characteristic inheritance pattern and further candidate gene sequencing of GJB2, SLC26A4, POU3F4 or mitochondrial DNA. Next we applied targeted resequencing on 80 NSHL genes in the remaining 20 probands. Each proband carried 4.8 variants that were not synonymous and had the occurring frequency of less than three among the 20 probands. These variants were then filtered out with the inheritance pattern of the family, allele frequency in normal hearing 80 control subjects, clinical features. Finally NSHL-causing candidate mutations were identified in 13(65%) of the 20 probands of multiplex families, bringing the total solve rate (or detection rate) in our familial cases to be 78.1% (25/32) Damaging mutations discovered by the targeted resequencing were distributed in nine genes such as WFS1, COCH, EYA4, MYO6, GJB3, COL11A2, OTOF, STRC and MYO3A, most of which were private. Despite the advent of whole genome and whole exome sequencing, we propose targeted resequencing and filtering strategy as a screening and diagnostic tool at least for familial NSHL to find mutations based upon its efficacy and cost-effectiveness.     

Gender differences in the relationship of ENPP1/PC-1 variants to obesity in a Turkish population

The ectoenzyme ENPP1 (also termed membrane glycoprotein PC-1 or ENPP1/PC-1) is an inhibitor of insulin-induced activation of the insulin receptor. There is evidence from previous studies that coding variants of ENPP1/PC-1 (K121Q) are associated with type 2 diabetes (T2D) and obesity. Studies in the general Turkish population have demonstrated: unique plasma lipid characteristics, a high prevalence of cardiovascular risk factors, and an increased prevalence of obesity and T2D. We investigated, therefore, the association of ENPP1/PC-1 variants with obesity and T2D in Turkish individuals. The TaqMan allelic discrimination assay was used for genotyping the relationship of ENPP1/PC-1 variants to obesity and T2D in a genetic association study of 1,553 genotyped, randomly selected subjects from the Turkish Heart Study. The K121Q (rs1044498) variant and other previously reported variants (rs997509, rs1799774, rs1044548, rs11964389, rs7754561) were analyzed. In this cohort, the minor allele frequency (MAF) of the K121Q variant was associated with obesity in male, but not in female subjects (male, odds ratio 1.64, 95% confidence interval 1.004-2.698, P = 0.048; female, odds ratio 1.003, 95% confidence interval 0.684-1.471, P = ns). In addition, the previously reported ENPP1/PC-1 "risk haplotype" (Q (rs1044498), delT (rs1799774), and G (rs7754561) alleles) was found to be associated with obesity in male, but not in female, subjects (P = 0.035). In contrast, there was no association of either the K121Q variant or the ENPP1/PC-1 haplotype with T2D. We find evidence that variants of ENPP1/PC-1 are associated with obesity in the male Turkish population; thus, these variants may contribute to the development of the obesity in these individuals.     

Whole-Exome Sequencing Identifies a Novel Genotype-Phenotype Correlation in the Entactin Domain of the Known Deafness Gene TECTA

Postlingual progressive hearing loss, affecting primarily the high frequencies, is the clinical finding in most cases of autosomal dominant nonsyndromic hearing loss (ADNSHL). The molecular genetic etiology of ADNSHL is extremely heterogeneous. We applied whole-exome sequencing to reveal the genetic etiology of high-frequency hearing loss in a mid-sized Korean family without any prior linkage data. Whole-exome sequencing of four family members (two affected and two unaffected), together with our filtering strategy based on comprehensive bioinformatics analyses, identified 21 potential pathogenic candidates. Sanger validation of an additional five family members excluded 20 variants, leaving only one novel variant, TECTA c.710C>T (p.T237I), as the strongest candidate. This variant resides in the entactin (ENT) domain and co-segregated perfectly with non-progressive high-frequency hearing loss in the family. It was absent among 700 ethnically matched control chromosomes, and the T237 residue is conserved among species, which supports its pathogenicity. Interestingly, this finding contrasted with a previously proposed genotype-phenotype correlation in which variants of the ENT domain of TECTA were associated with mid-frequency hearing loss. Based upon what we observed, we propose a novel “genotype to phenotype” correlation in the ENT domain of TECTA. Our results shed light on another important application of whole-exome sequencing: the establishment of a novel genotype-phenotype in the molecular genetic diagnosis of autosomal dominant hearing loss.     

Novel EDGE encoding method enhances ability to identify genetic interactions

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.     

Whole-genome sequencing in French Canadians from Quebec

Genome-wide association studies (GWAS) have had a tremendous success in the identification of common DNA sequence variants associated with complex human diseases and traits. However, because of their design, GWAS are largely inappropriate to characterize the role of rare and low-frequency DNA variants on human phenotypic variation. Rarer genetic variation is geographically more restricted, supporting the need for local whole-genome sequencing (WGS) efforts to study these variants in specific populations. Here, we present the first large-scale low-pass WGS of the French-Canadian population. Specifically, we sequenced at ~5.6× coverage the whole genome of 1970 French Canadians recruited by the Montreal Heart Institute Biobank and identified 29 million bi-allelic variants (31 % novel), including 19 million variants with a minor allele frequency (MAF) <0.5 %. Genotypes from the WGS data are highly concordant with genotypes obtained by exome array on the same individuals (99.8 %), even when restricting this analysis to rare variants (MAF <0.5, 99.9 %) or heterozygous sites (98.9 %). To further validate our data set, we showed that we can effectively use it to replicate several genetic associations with myocardial infarction risk and blood lipid levels. Furthermore, we analyze the utility of our WGS data set to generate a French-Canadian-specific imputation reference panel and to infer population structure in the Province of Quebec. Our results illustrate the value of low-pass WGS to study the genetics of human diseases in the founder French-Canadian population.     

Challenges in assessing pathogenicity based on frequency of variants in mismatch repair genes: an extreme case of a MSH2 variant and a meta-analysis

The clinical interpretation of variants in mismatch repair (MMR) genes associated with Lynch syndrome can be confusing when the functional nature of the variant is not clearly defined. We report an extreme case where a polymorphism in the MSH2 gene which had a low minor allele frequency, was misclassified as a mutation based on low evidential methods in the database and previous publications. We expanded this experience to perform a systematic meta-analysis in order to investigate other variants that have potentially been misclassified. Our results suggested that the interpretation of pathogenicity should be more cautious and emphasized the need for solid validation through multiple analyses including functional analysis for variants in MMR genes.     

Rare Variant Association Testing by Adaptive Combination of P-values

With the development of next-generation sequencing technology, there is a great demand for powerful statistical methods to detect rare variants (minor allele frequencies (MAFs)<1%) associated with diseases. Testing for each variant site individually is known to be underpowered, and therefore many methods have been proposed to test for the association of a group of variants with phenotypes, by pooling signals of the variants in a chromosomal region. However, this pooling strategy inevitably leads to the inclusion of a large proportion of neutral variants, which may compromise the power of association tests. To address this issue, we extend the -MidP method (Cheung et al., 2012, Genet Epidemiol 36: 675–685) and propose an approach (named ‘adaptive combination of P-values for rare variant association testing’, abbreviated as ‘ADA’) that adaptively combines per-site P-values with the weights based on MAFs. Before combining P-values, we first imposed a truncation threshold upon the per-site P-values, to guard against the noise caused by the inclusion of neutral variants. This ADA method is shown to outperform popular burden tests and non-burden tests under many scenarios. ADA is recommended for next-generation sequencing data analysis where many neutral variants may be included in a functional region.     

Variant detection at the δ opioid receptor (OPRD1) locus and population genetics of a novel variant affecting protein sequence

The three opioid receptor genes, and in particular the mu and delta loci (OPRM1 and OPRD1, respectively), are compelling candidates to influence risk for substance dependence. Previous study of a variant at the OPRD1 locus, T921C, has shown association with opioid dependence. This variant does not alter protein sequence, and could not be directly responsible for a physiologic effect. We sequenced the OPRD1 coding region in six individuals with differing T921C alleles, to identify new common variants more likely to explain the association with phenotype. We identified one novel variant in exon 1, 80T-->G, which predicts a change in amino acid sequence from phenylalanine (80T) to cysteine (80G) (F27C). We present here basic population genetics of this variant, and population genetic data for the T921C variant. We found significant differences in allele frequency between populations, and a maximum frequency of the 80G allele of 9%, in each of two European populations. This variant could contribute to the previously reported association results.