High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays - PubMed (original) (raw)

High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays

Prachi Kothiyal et al. BMC Biotechnol. 2010.

Abstract

Background: Despite current knowledge of mutations in 45 genes that can cause nonsyndromic sensorineural hearing loss (SNHL), no unified clinical test has been developed that can comprehensively detect mutations in multiple genes. We therefore designed Affymetrix resequencing microarrays capable of resequencing 13 genes mutated in SNHL (GJB2, GJB6, CDH23, KCNE1, KCNQ1, MYO7A, OTOF, PDS, MYO6, SLC26A5, TMIE, TMPRSS3, USH1C). We present results from hearing loss arrays developed in two different research facilities and highlight some of the approaches we adopted to enhance the applicability of resequencing arrays in a clinical setting.

Results: We leveraged sequence and intensity pattern features responsible for diminished coverage and accuracy and developed a novel algorithm, sPROFILER, which resolved >80% of no-calls from GSEQ and allowed 99.6% (range: 99.2-99.8%) of sequence to be called, while maintaining overall accuracy at >99.8% based upon dideoxy sequencing comparison.

Conclusions: Together, these findings provide insight into critical issues for disease-centered resequencing protocols suitable for clinical application and support the use of array-based resequencing technology as a valuable molecular diagnostic tool for pediatric SNHL and other genetic diseases with substantial genetic heterogeneity.

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Figures

Figure 1

Improvement in array call rates with protocol optimization and application of sPROFILER to GSEQ calls. (data shown for Cincinnati arrays). Data is separated into two categories based upon protocol (short and long range PCR vs. short range only PCR) and then arranged in ascending order of GSEQ call rates.

Figure 2

Performance improvement with protocol optimization; array sensitivity and specificity with application of sPROFILER to GSEQ calls. (data shown for Cincinnati arrays). Data is arranged in the same patient ID order as figure 1. (a) False positive calls with and without protocol optimization/sPROFILER. No-calls and positive calls were processed for the first 12 chips (short and long range PCR protocol) while only no-calls were processed for the remaining 13 chips (short range only PCR protocol). No-calls were converted to wild-type, left as no-call, or were assigned a variant call. Chips that were analyzed only for no-calls may show an increase in false positive rate due to conversion of a fraction of no-calls to variant calls, some of which are not true variants. (b) False negative calls with and without protocol optimization/sPROFILER represented as a portion of total true variants.

Figure 3

Differential impact of high probe G-content and C-content on probe performance; G-richness of a probe has a more severe impact on hybridization intensity than C-richness and G-stretches degrade peak intensity. (a) Peak feature intensity versus probe G-content and C-content. (b) Peak feature intensity for probes with same G-content grouped based on presence of G-stretch, C-stretch and no continuous stretches. Error bars represent one standard deviation.

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High-throughput detection of mutations responsible for childhood hearing loss using resequencing microarrays - PubMed (original) (raw)