CLINICAL NEXT GENERATION SEQUENCING REVEALS AN H3F3A GENE AS A NEW POTENTIAL GENE CANDIDATE FOR MICROCEPHALY ASSOCIATED WITH SEVERE DEVELOPMENTAL DELAY, INTELLECTUAL DISABILITY AND GROWTH RETARDATION
Maver A1, Čuturilo G2,3, Ruml Stojanović J3, Peterlin B1,*
*Corresponding Author: Professor Borut Peterlin, Clinical Institute of Genomic Medicine, University Medical Center Ljubljana, Šlajmerjeva 4, 1000 Ljubljana, Slovenia. Tel: +38615401137. E-mail: borut.peterlin@kclj.si
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METHODS

Exome Sequencing. Exome sequencing was performed in the affected proband and unaffected parents. Briefly, the library preparation was performed using the TruSeq protocol (Illumina Inc., San Diego, CA, USA), followed by the capture of exonic targets using the xGen Exome Research Panel v1.0 using Integrated DNA Technologies (IDT), probes (Coralville, IA, USA). The exome capture targeted 39 Mb of exonic regions of 19,396 genes in the human genome (hg19). Sequencing was subsequently performed using the pair-end sequencing protocol on Next- Seq 550 (Illumina Inc.) in 2 × 150 cycles. Sequencing data was processed using an in-house analysis pipeline, based on the combination of Burrows-Wheeler (BWA) aligner (v0.7.2) (http://bio-bwa.sourceforge.net/) and GATK software (v3.2) (https://software.broadinstitute.rg/ gatk/) for variant calling. Duplicate sequences were removed using Picard MarkDuplicates (https://broadinstitute. github. io.picard/), followed by base quality score recalibration, variant calling, variant quality score recalibration and variant filtering using the tools in the GATK framework [7]. Variant Analysis. The resulting variants were collected using the VariantTools software (https:/github. com/ vatlab/variant tools) and their transcript and protein consequences predicted using the ANNOVAR (http:// annovar. openbioinformatics.org/en/latest/) and SNPeff software (http://snpeff.sourceforge.net/) [8-10]. Variant consequences were predicted based on Refseq gene models (https://www.ncbi.nlm.nih.gov/refseq/). Precomputed pathogenicity predictions for missense variants were obtained from the dbNSFP v2 database (https://google.com/ site/pop gen/dbNSFP) [11] and the MutationTaster, SIFT, Polyphen2, MetaSVM, CADD and REVEL (which are included in the dbNSFP database referred above) estimates were used in prioritization of pathogenic variants. Evolutionary conservation rates of the variant sites was based on GERP++ rejected substation (RS) scores [12]. Variant frequency information for worldwide populations was based on the data from gnomAD project (gnomad. broadinstitute.com). We used the in-house population variant frequency estimates based on the internal data of 3000 Slovenian exomes as a source of variation frequency in the background population. We used ClinVar as the source of information on variant-disease associations [13]. Variant Filtration Strategy. The annotated variants were filtered using three strategies, based on the assumed pattern of inheritance, autosomal dominant (de novo variants), autosomal recessive (homozygous and compound heterozygous variants) and X-linked (de novo and maternally inherited variants). We used the frequency threshold of 0.01% in any of the surveyed populations in the dominant scenario and the frequency threshold of 0.1% for the autosomal recessive and X-linked scenarios. We performed manual interpretation of the remaining variants with consideration of clinical overlap, variant rarity in the general population, theoretical pathogenicity predictions and evolutionary conservation. At this stage, all the variants were also manually inspected at read level to control the quality of the variant calls.



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