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Journal of Allergy and Clinical ImmunologyGeneticsPeer-reviewed journal article

Array-based sequencing of filaggrin gene for comprehensive detection of disease-associated variants

X.F. Colin C. Wong, Simon L. I. J. DeniliD, Jia Nee FooiD, Huijia Chen, Angeline Su Ling Tay, Rebecca L. Haines, Mark Boon Yang TangiD, W.H. Irwin McLeaniD, Aileen SandilandsiD, Frances J.D. SmithiD, Ellen Birgitte LaneiD, Jianjun LiuiD, John CommoniD

PDFDOIPreprint

Abstract

The filaggrin gene (FLG) is essential for skin differentiation and epidermal barrier formation with links to skin diseases, however it has a highly repetitive nucleotide sequence containing very limited stretches of unique nucleotides for precise mapping to reference genomes. Sequencing strategies using polymerase chain reaction (PCR) and conventional Sanger sequencing have been successful for complete FLG coding DNA sequence amplification to identify pathogenic mutations but this time-consuming, labour intensive method has restricted utility. Next-generation sequencing (NGS) offers obvious benefits to accelerate FLG analysis but standard re-sequencing techniques such as oligoprobe-based exome or customized targeted-capture can be expensive, especially for a single target gene of interest. We therefore designed a protocol to improve FLG sequencing throughput using a set of FLG-specific PCR primer assays compatible with microfluidic amplification, multiplexing and current NGS protocols. Using DNA reference samples with known FLG genotypes for benchmarking, this protocol is shown to be concordant for variant detection across different sequencing methodologies. We applied this methodology to analyze cohorts from ethnicities previously not studied for FLG variants and demonstrate usefulness for discovery projects. This comprehensive coverage sequencing protocol is labour-efficient and offers an affordable solution to scale up FLG sequencing for larger cohorts. Robust and rapid FLG sequencing can improve patient stratification for research projects and provide a framework for gene specific diagnosis in the future.

Citations
49
FWCI
7.15
Field percentile
top 3%

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