Kyle R. Taylor

Comprehensive genetic testing for hereditary hearing loss using massively parallel sequencing

The extreme genetic heterogeneity of nonsyndromic hearing loss (NSHL) makes genetic diagnosis expensive and time consuming using available methods. To assess the feasibility of target-enrichment and massively parallel sequencing technologies to interrogate all exons of all genes implicated in NSHL, we tested nine patients diagnosed with hearing loss. Solid-phase (NimbleGen) or solution-based (SureSelect) sequence capture, followed by 454 or Illumina sequencing, respectively, were compared. Sequencing reads were mapped using GSMAPPER, BFAST, and BOWTIE, and pathogenic variants were identified using a custom-variant calling and annotation pipeline (ASAP) that incorporates publicly available in silico pathogenicity prediction tools (SIFT, BLOSUM, Polyphen2, and Align-GVGD). Samples included one negative control, three positive controls (one biological replicate), and six unknowns (10 samples total), in which we genotyped 605 single nucleotide polymorphisms (SNPs) by Sanger sequencing to measure sensitivity and specificity for SureSelect-Illumina and NimbleGen-454 methods at saturating sequence coverage. Causative mutations were identified in the positive controls but not in the negative control. In five of six idiopathic hearing loss patients we identified the pathogenic mutation. Massively parallel sequencing technologies provide sensitivity, specificity, and reproducibility at levels sufficient to perform genetic diagnosis of hearing loss.

Advancing genetic testing for deafness with genomic technology

Background Non-syndromic hearing loss (NSHL) is the most common sensory impairment in humans. Until recently its extreme genetic heterogeneity precluded comprehensive genetic testing. Using a platform that couples targeted genomic enrichment (TGE) and massively parallel sequencing (MPS) to sequence all exons of all genes implicated in NSHL, we tested 100 persons with presumed genetic NSHL and in so doing established sequencing requirements for maximum sensitivity and defined MPS quality score metrics that obviate Sanger validation of variants. Methods We examined DNA from 100 sequentially collected probands with presumed genetic NSHL without exclusions due to inheritance, previous genetic testing, or type of hearing loss. We performed TGE using post-capture multiplexing in variable pool sizes followed by Illumina sequencing. We developed a local Galaxy installation on a high performance computing cluster for bioinformatics analysis. Results To obtain maximum variant sensitivity with this platform 3.2–6.3 million total mapped sequencing reads per sample were required. Quality score analysis showed that Sanger validation was not required for 95% of variants. Our overall diagnostic rate was 42%, but this varied by clinical features from 0% for persons with asymmetric hearing loss to 56% for persons with bilateral autosomal recessive NSHL. Conclusions These findings will direct the use of TGE and MPS strategies for genetic diagnosis for NSHL. Our diagnostic rate highlights the need for further research on genetic deafness focused on novel gene identification and an improved understanding of the role of non-exonic mutations. The unsolved families we have identified provide a valuable resource to address these areas.

DFNA8/12 caused by TECTA mutations is the most identified subtype of nonsyndromic autosomal dominant hearing loss.

The prevalence of DFNA8/DFNA12 (DFNA8/12), a type of autosomal dominant nonsyndromic hearing loss (ADNSHL), is unknown as comprehensive population‐based genetic screening has not been conducted. We therefore completed unbiased screening for TECTA mutations in a Spanish cohort of 372 probands from ADNSHL families. Three additional families (Spanish, Belgian, and English) known to be linked to DFNA8/12 were also included in the screening. In an additional cohort of 835 American ADNSHL families, we preselected 73 probands for TECTA screening based on audiometric data. In aggregate, we identified 23 TECTA mutations in this process. Remarkably, 20 of these mutations are novel, more than doubling the number of reported TECTA ADNSHL mutations from 13 to 33. Mutations lie in all domains of the α‐tectorin protein, including those for the first time identified in the entactin domain, as well as the vWFD1, vWFD2, and vWFD3 repeats, and the D1–D2 and TIL2 connectors. Although the majority are private mutations, four of them—p.Cys1036Tyr, p.Cys1837Gly, p.Thr1866Met, and p.Arg1890Cys—were observed in more than one unrelated family. For two of these mutations founder effects were also confirmed. Our data validate previously observed genotype–phenotype correlations in DFNA8/12 and introduce new correlations. Specifically, mutations in the N‐terminal region of α‐tectorin (entactin domain, vWFD1, and vWFD2) lead to mid‐frequency NSHL, a phenotype previously associated only with mutations in the ZP domain. Collectively, our results indicate that DFNA8/12 hearing loss is a frequent type of ADNSHL. Hum Mutat 32:1–10, 2011.

AudioGene: Predicting Hearing Loss Genotypes from Phenotypes to Guide Genetic Screening

Autosomal dominant nonsyndromic hearing loss (ADNSHL) is a common and often progressive sensory deficit. ADNSHL displays a high degree of genetic heterogeneity and varying rates of progression. Accurate, comprehensive, and cost‐effective genetic testing facilitates genetic counseling and provides valuable prognostic information to affected individuals. In this article, we describe the algorithm underlying AudioGene, a software system employing machine‐learning techniques that utilizes phenotypic information derived from audiograms to predict the genetic cause of hearing loss in persons segregating ADNSHL. Our data show that AudioGene has an accuracy of 68% in predicting the causative gene within its top three predictions, as compared with 44% for a majority classifier. We also show that AudioGene remains effective for audiograms with high levels of clinical measurement noise. We identify audiometric outliers for each genetic locus and hypothesize that outliers may reflect modifying genetic effects. As personalized genomic medicine becomes more common, AudioGene will be increasingly useful as a phenotypic filter to assess pathogenicity of variants identified by massively parallel sequencing.

Using the Phenome and Genome to Improve Genetic Diagnosis for Deafness

The advent of massively parallel sequencing (MPS) has revolutionized genetic testing for deafness by enabling personal genomics in diagnosis (for a comprehensive review, see Shearer et al1). This technology has drastically increased the throughput of genetic testing but concomitantly has exponentially increased the amount of genetic data generated. To address this deluge of data and to streamline analysis, we have developed a custom variant prioritization pipeline incorporating data from a patient’s genome and phenome (the patient’s phenotype). In aggregate, the patient’s phenome is his or her constellation of phenotypic traits, which for hearing loss includes the patien’s audioprofile (pattern of hearing loss on audiogram), temporal bone anatomy (imaging), and ocular pathology (fundoscopy). Here we present 3 cases to illustrate how knowledge of a patient’s phenome can assist variant prioritization by corroborating likely pathogenic variants and excluding variants of unknown significance (VUS).

Prioritization of Retinal Disease Genes: An Integrative Approach

The discovery of novel disease‐associated variations in genes is often a daunting task in highly heterogeneous disease classes. We seek a generalizable algorithm that integrates multiple publicly available genomic data sources in a machine‐learning model for the prioritization of candidates identified in patients with retinal disease. To approach this problem, we generate a set of feature vectors from publicly available microarray, RNA‐seq, and ChIP‐seq datasets of biological relevance to retinal disease, to observe patterns in gene expression specificity among tissues of the body and the eye, in addition to photoreceptor‐specific signals by the CRX transcription factor. Using these features, we describe a novel algorithm, positive and unlabeled learning for prioritization (PULP). This article compares several popular supervised learning techniques as the regression function for PULP. The results demonstrate a highly significant enrichment for previously characterized disease genes using a logistic regression method. Finally, a comparison of PULP with the popular gene prioritization tool ENDEAVOUR shows superior prioritization of retinal disease genes from previous studies. The java source code, compiled binary, assembled feature vectors, and instructions are available online at https://github.com/ahwagner/PULP.

Audioprofile Surfaces: The 21st Century Audiogram

Objective: To present audiometric data in 3 dimensions by considering age as an addition dimension. Methods: Audioprofile surfaces (APSs) were fitted to a set of audiograms by plotting each measurement of an audiogram as an independent point in 3 dimensions with the x, y, and z axes representing frequency, hearing loss in dB, and age, respectively. Results: Using the Java-based APS viewer as a standalone application, APSs were pre-computed for 34 loci. By selecting APSs for the appropriate genetic locus, a clinician can compare this APS-generated average surface to a specific patient’s audiogram. Conclusion: Audioprofile surfaces provide an easily interpreted visual representation of a person’s hearing acuity relative to others with the same genetic cause of hearing loss. Audioprofile surfaces will support the generation and testing of sophisticated hypotheses to further refine our understanding of the biology of hearing.

Sequencing and disease variation detection tools and techniques

Next-generation sequencing technology provides greater sequencing capacity to identify disease-causing variations. Currently there are many alignment tools and variation detection techniques, multiple SNP data sets used to filter out benign polymorphisms with the intent of enriching for disease-causing variations, and many tools and websites for annotating variations and performing pathogenicity prediction of variations. There is no consensus or standard with regards to which tools and corresponding parameter settings for each tool is appropriate for the different application of next-generation sequencing technologies for disease-causing variation identification. We have implemented an annotation pipeline that can easily use the output from different alignment tools (BLAT, BWA, BFAST, Bowtie) typically used to align sequence from next-generation sequencing and Sanger sequencing experiments. The Automated Sequence Analysis Pipeline (ASAP) reports variations using the HGVS standardized nomenclature[1] to facilitate comparison across tools and publications. To date, we have annotated the sequence from 13 exomes in an effort to identify disease-causing mutations.

Insights from brands in Facebook

Companies are increasingly turning to social media as a way to engage with their customers and to promote their brand and products. We analyzed the content of Facebook fan pages of the top 100 brands to gain insight into how companies utilize social media. These brands span 14 different categories from fashion and apparel to computer hardware. We find that different brands use different types of posts and get varying levels of engagement from users based on their business category. Interestingly, 45.53% of posts contained a reference to a company or a person with many of the companies referencing others.

Cordova: Web-based management of genetic variation data

UNLABELLED Cordova is an out-of-the-box solution for building and maintaining an online database of genetic variations integrated with pathogenicity prediction results from popular algorithms. Our primary motivation for developing this system is to aid researchers and clinician-scientists in determining the clinical significance of genetic variations. To achieve this goal, Cordova provides an interface to review and manually or computationally curate genetic variation data as well as share it for clinical diagnostics and the advancement of research. AVAILABILITY AND IMPLEMENTATION Cordova is open source under the MIT license and is freely available for download at https://github.com/clcg/cordova.