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
page: 65
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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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