algorithms and available bioinformatic tools
designed for ROH detection based on WES data.
Selection of appropriate algorithms should mainly
consider specific features of the case under study,
such as the genetic context, the ROH size and the
number of relatives affected by the same condition.
Monogenic disorders have been studied by
classical approaches aiming to unravel several
disease-causing genes. The presence of numerous
genes in a candidate genomic region was a limiting
factor, considering the costs and the time required,
to screen mutations by Sanger sequencing. Another
limitation with the “traditional” approaches is more
evident when they are used to study trios (only the
parents and their affected child) or larger families
with only one or two affected members. The
genotype data extracted in these familial contexts
generally remain statistically insufficient for
classical analytical approaches (Jorde, 2000).
As compared with conventional homozygosity
mapping that uses known SNPs, WES has the added
advantage of allowing the identification of the actual
disease-causing variant. Therefore, instead of using
two different procedures, one for identifying
candidate loci and the other to identify the genetic
defect itself at the nucleotide level, both can be
performed in a single step by WES. Nonetheless,
there are still limitations and further bioinformatic
developments are required. Considering the
examples presented, there are sensitivity issues
especially if the genetic defect is in a small ROH.
Finally, we consider that it would be useful to
develop a bioinformatic tool that combines variant
filtering and homozygosity mapping (Appendix),
which currently need to be performed separately.
ACKNOWLEDGEMENTS
The authors acknowledge support from: i) Fundação
para a Ciência e Tecnologia (FCT) [Grant ref.:
PD/BD/105767/2014] (R.P.); ii) Research grant
attributed by “Fundo para a Investigação e
Desenvolvimento do Centro Hospitalar do Porto”
[Grant ref.: 336-13(196-DEFI/285-CES)] (J.O.). The
work was also supported by the Institutions of the
authors and in part by UMIB, which is funded by
through FCT under the Pest-OE/SAU/UI0215/ 2014.
The authors would like to thank the clinicians for
patient referral.
REFERENCES
Abecasis, G. R. et al., 2002. Merlin—rapid analysis of
dense genetic maps using sparse gene flow trees.
Nature Genetics, 30(1), pp.97–101.
Alkuraya, F. S., 2010. Homozygosity mapping: One more
tool in the clinical geneticist’s toolbox. Genetics in
Medicine, 12(4), pp.236–239.
Bittles, A. H., 2001. Consanguinity and its relevance to
clinical genetics. Clinical genetics, 60(2), pp.89–98.
Boycott, K. M. et al., 2013. Rare-disease genetics in the
era of next-generation sequencing: discovery to
translation. Nat Rev Genet, 14(10), pp.681–691.
Carr, I. M. et al., 2013. Autozygosity Mapping with
Exome Sequence Data. Human Mutation, 34(1),
pp.50–56.
Christianson, A., Howson, C.P. & Modell, B., 2006.
Global report on birth defects: the hidden toll of dying
and disabled children, New York. Available at:
http://www.marchofdimes.org/materials/global-report-
on-birth-defects-the-hidden-toll-of-dying-and-
disabled-children-full-report.pdf [Accessed November
15, 2016].
Gillespie, R. L., Lloyd, I. C. & Black, G. C. M., 2014. The
Use of Autozygosity Mapping and Next-Generation
Sequencing in Understanding Anterior Segment
Defects Caused by an Abnormal Development of the
Lens. Human Heredity, 77(1–4), pp.118–137.
Goodship, J. et al., 2000. Report Autozygosity Mapping of
a Seckel Syndrome Locus to Chromosome 3q22.1-
q24. Am. J. Hum. Genet, 67, pp.498–503.
Gormez, Z., Bakir-Gungor, B. & Sagiroglu, M. S., 2014.
HomSI: a homozygous stretch identifier from next-
generation sequencing data. Bioinformatics, 30(3),
pp.445–447.
Gudbjartsson, D. F. et al., 2000. Allegro, a new computer
program for multipoint linkage analysis. Nature
Genetics, 25(1), pp.12–13.
Gusev, A. et al., 2008. Whole population, genome-wide
mapping of hidden relatedness. Genome Research,
19(2), pp.318–326.
Jorde, L. B., 2000. Linkage disequilibrium and the search
for complex disease genes. Genome research, 10(10),
pp.1435–1444.
Kruglyak, L. et al., 1996. Parametric and Nonparametric
Linkage Analysis: A Unified Multipoint Approach.
Am. J. Hum. Genet, 58, pp.1347–1363.
Magi, A. et al., 2014. H3M2: detection of runs of homozy-
gosity from whole-exome sequencing data. Bioinfor-
matics (Oxford, England), 30(20), pp.2852–2859.
McQuillan, R. et al., 2008. Runs of homozygosity in
European populations. American journal of human
genetics, 83(3), pp.359–372.
Ng, S. B. et al., 2010. Exome sequencing identifies the
cause of a mendelian disorder. Nature Genetics, 42(1),
pp.30–35.
Oliveira, J. et al., 2015. New splicing mutation in the
choline kinase beta (CHKB) gene causing a muscular
dystrophy detected by whole-exome sequencing.
Journal of Human Genetics. pp. 305-312.