bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P54. TOWARDS A BELGIAN REFERENCE SET
Erika Souche 1* , Amin Ardeshirdavani 2 , Yves Moreau 2 , Gert Matthijs 1 & Joris Vermeesch 1 .
Department of Human Genetics, KU Leuven 1 ; ESAT-STADIUS Center for Dynamical Systems, Signal Processing and
Data Analytic, KU Leuven 2 . * Erika.souche@uzleuven.be
Next-Generation Sequencing (NGS) is increasingly used to study and diagnose human disorders. The simultaneous
sequencing of a large number of genes leading to the detection of a large number of variants, the bottleneck has moved
from sequencing to variant interpretation and classification. Although publically available databases of variant
frequencies help distinguishing causative mutations from common variants, they often lack population specific variant
frequencies. To circumvent this shortage of population specific information, most genetic centers exploit their sequence
data of unrelated and unaffected individuals to filter out common local variants is often done. However the
files/databases are rarely shared and they are mainly based on whole exome data. In this project we demonstrate the
utility of a local variant database generated from whole exome data, describe a procedure allowing the sharing of
information between genetic centers and mine low coverage whole genome data for common variants.
INTRODUCTION
Next-Generation Sequencing (NGS) is increasingly used
to study and diagnose human disorders. The simultaneous
sequencing of a large number of genes leading to the
detection of a large number of variants, the bottleneck has
moved from sequencing to variant interpretation and
classification. Publically available databases of variant
frequencies provided by, among others, the Exome
Sequencing Project (ESP) the 1000 genomes project
(McVean et al., 2012) or dbSNP (Sherry et al., 2001) help
distinguishing causative mutations from common variants,
identifying up to 78% of variants as common for a Belgian
exome. However, these data sets often lack population
specific variant frequencies and are outperformed by
databases of local variants. For example, using GoNL
(The Genome of the Netherlands Consortium, 2014) alone
allowed the identification of up to 85% of variants as
common for the same Belgian exome. The fact that the
GoNL is based on only 498 individuals further highlights
the importance of building and using population specific
databases.
Such population specific data can be retrieved from locally
sequenced individuals that underwent Whole Exome
Sequencing (WES) or Whole Genome Sequencing (WGS).
Storing only the frequencies and genotype counts of the
variants provides a valuable tool for variant classification
while no sensitive information on the individuals is
included.
METHODS
WES data of 350 unrelated and unaffected individuals
have been parsed. All samples were analysed in a similar
way i.e. reads were aligned to the reference genome with
BWA (Li & Durbin, 2009) and genotyping was performed
according to GATK best practices (McKenna et al., 2010;
DePristo et al., 2011). All samples were genotyped at all
polymorphic positions using GATK HaplotypeCaller and
GenotypeGVCFs. For each position, samples with low
quality genotype were considered as not genotyped and
excluded from the genotype counts. The number of
alternate alleles, allele counts and genotypes were
compiled in a population VCF file, in which individual
genotypes are not accessible.
Variant frequencies can also be extracted from low
coverage WGS. As a pilot we processed the data of
chromosome 21 of about 4,000 WGS. The mapping was
performed with BWA (Li & Durbin, 2009) and the BAM
files were merged per 200 samples. All positions were
genotyped using freebayes (Garrison & Marth, 2012).
Genotype information of all locations outside low
complexity regions were then compiled for all samples
using the integration of Apache Hadoop, HBase and Hive
(see poster “Big data solutions for variant discovery from
low coverage sequencing data, by integration of Hadoop,
Hbase and Hive”). Several models were then used to
distinguish real variants from sequencing errors: the Minor
Allele Frequency (MAF), the transition/transversion ratio,
the expected number of loci with a MAF of 5%, etc.
RESULTS & DISCUSSION
We demonstrated the effect of our reference set on several
exomes. The inclusion of only 350 individuals allowed the
identification of about 3% additional common variants,
not listed as common by ESP, dbSNP (Sherry et al., 2001),
1000 Genomes (McVean et al., 2012) and GoNL (The
Genome of the Netherlands Consortium, 2014). Since only
the frequencies of the variants in the screened populations
are reported, this file can easily be shared between
laboratories. Besides, the procedure used to generate the
population VCF file can easily be applied to several
genetic centers in order to generate a common population
VCF file, as planned within the BeMGI project.
Finally we expect that the data from WGS will further
increase the performance of our reference set. A genomewide
variant frequencies file from local population will
become worthwhile when WGS is routinely used in
diagnostics.
REFERENCES
DePristo M et al. Nature Genetics 43, 491-498 (2011).
Exome Variant Server, NHLBI Exome Sequencing Project (ESP), Seattle,
WA (URL: http://evs.gs.washington.edu/EVS/).
Garrison E & Marth G http://arxiv.org/abs/1207.3907 (2012).
Li H & Durbin R Bioinformatics 25, 1754-60 (2009).
McKenna A et al. Genome Research 20, 1297-303 (2010).
McVean et al. Nature 491, 56–65 (2012).
Sherry ST, et al. Nucleic Acids Res. 29, 308-11 (2001).
The Genome of the Netherlands Consortium. Nature Genetics 46,
818–825 (2014).
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