Homozygosity Mapping using Whole-Exome Sequencing: A Valuable

Approach for Pathogenic Variant Identification in Genetic Diseases

Jorge Oliveira

1,2,*

, Rute Pereira

1,*

, Rosário Santos

and Mário Sousa

Instituto de Ciências Biomédicas Abel Salazar (ICBAS), Universidade do Porto,

R. Jorge de Viterbo Ferreira nº 228, Porto, Portugal

Centro de Genética Médica Dr. Jacinto Magalhães, Centro Hospitalar do Porto,

Praça Pedro Nunes nº88, Porto, Portugal

Keywords: Whole-Exome Sequencing, Homozygosity Mapping, Next-Generation Sequencing, Clinical Genetics.

Abstract: In the human genome, there are homozygous regions presenting as sizeable stretches, or ‘runs’ of

homozygosity (ROH). The length of these ROH is dependent on the degree of shared parental ancestry,

being longer in individuals descending from consanguineous marriages or those from isolated populations.

Homozygosity mapping is a powerful tool in clinical genetics. It relies on the assumption that, due to

identity-by-descent, individuals affected by a recessive disease are likely to have homozygous markers

surrounding the disease locus. Consequently, the analysis of ROH shared by affected individuals in the

same kindred often helps to identify the disease-causing gene. However, scanning the entire genome for

blocks of homozygosity, especially in sporadic cases, is not a straight-forward task. Whole-exome

sequencing (WES) has been shown to be an effective approach for finding pathogenic variants, particularly

in highly heterogeneous genetic diseases. Nevertheless, the huge amount of data, especially variants of

unknown clinical significance, and the presence of false-positives due to sequencing artifacts, makes WES

analysis complex. This paper briefly reviews the different algorithms and bioinformatics tools available for

ROH identification. We emphasize the importance of performing ROH analysis using WES data as an

effective way to improve diagnostic yield.

1 INTRODUCTION

Mendelian diseases are caused by pathogenic

variants in genes that follow the biological

inheritance laws originally proposed by Gregor

Mendel. The disease-causing gene may be in an

autosome or in a sex-chromosome, and may be

dominant or recessive. Individually, these diseases

are considered to be rare, but collectively they occur

at a high rate, with an estimated 7.9 million children

being born annually with a serious birth defect of

genetic origin (Christianson et al., 2006).

Sanger sequencing has been the gold standard in

molecular diagnostics of Mendelian diseases and is

still the first choice to confirm a suspected diagnosis,

enabling accurate genetic counselling. However, for

diseases with genetic heterogeneity, such as

hereditary myopathies or primary ciliary dyskinesia,

gene-by-gene Sanger sequencing is not the most

Equally contributing authors.

cost-effective or efficient approach (Oliveira et al.,

2015; Pereira et al., 2015). Technological advances

over the past decade has led to the development of

high-throughput sequencing platforms, revolutioni-

zing the sequencing capabilities and boosting the use

of this so called “next-generation sequencing”

(NGS) both in research and in clinical diagnostic

settings. With this technology, the human genome

can be completely sequenced, allowing the

simultaneous analysis of multiple genes. The

application of NGS in Mendelian diseases focuses

firstly on exonic regions of DNA, as the majority of

disease-causing mutations are found in exons or in

the flanking intronic regions (Ng et al., 2010).

Marriage between close biological relatives

increases the probability of the offspring inheriting

two deleterious copies of a recessive gene. Thus,

children from consanguineous couples have a higher

incidence of autosomal recessive disorders (Bittles,

2001). In addition, as alleles are parts of haplotypes,

not only will the affected descendant have two

identical copies of the ancestral allele, but the

210

Oliveira J., Pereira R., Santos R. and Sousa M.

Homozygosity Mapping using Whole-Exome Sequencing: A Valuable Approach for Pathogenic Variant Identiﬁcation in Genetic Diseases.

DOI: 10.5220/0006248502100216

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 210-216

ISBN: 978-989-758-214-1

surrounding DNA segment (haplotype) will also be

homozygous. Thus, the child will be homozygous

for that segment, the so-called runs of homozygosity

(ROH) (McQuillan et al., 2008). These homozygous

segments that are identical by descent (IBD) are

generally longer in consanguinity cases.

Nevertheless, even in the absence of known recent

inbreeding, ROH can be detected in geographically

isolated populations and historical bottlenecks

(Pemberton et al., 2012). The ROH length is

dependent on the degree of shared parental ancestry

and its age. Recent inbreeding events/parental

consanguinity tend to have longer ROH (measuring

tens of Mb) since there are fewer recombination

events interrupting the segments that are IBD.

Conversely, older ROH are generally much shorter

because the homozygous stretches have been split

down by repeated meioses over the generations, with

the exception of genomic regions where the

recombinetion rates are lower (McQuillan et al.,

2008).

2 HOMOZYGOSITY MAPPING

Homozygosity mapping (also known as autozygosity

mapping), consists in the identification of

homozygous regions in the genome. This is a

powerful strategy

to associate new genes to diseases

(Alkuraya 2010; Goodship et al., 2000). As

mentioned, affected individuals are likely to have

two IBD alleles at markers located in the vicinity of

the disease locus and thus will be homozygous for

these markers. This method relies on the search for

ROH that are shared by affected individuals in the

same family. However, scanning the genome for

blocks of homozygosity, although simple and

efficient, requires sophisticated techniques such as

the use of numerous microsatellite markers or high-

density single nucleotide polymorphism (SNP)

genotyping. For most autozygosity mapping

projects, multipoint linkage analyses under a

recessive disease model have been performed, with

software such as GENEHUNTER (Kruglyak et al.,

1996), SIMWALK2 (Sobel et al., 2002), MERLIN

(Abecasis et al., 2002) or ALLEGRO (Gudbjartsson

et al., 2000).

In general, haplotypes are inspected manually for

homozygous regions that are shared by all affected

individuals, and can be inferred to be IBD if

genotypes from the parents or other close relatives

are available. But, in practical terms, conventional

parametric methods of multipoint linkage analysis

for large datasets in complex consanguineous

families are often difficult, because of the time and

computational power required, since homozygous

blocks among affected individuals tend to be large

(mean 4.4 Mb) and contain dozens or hundreds of

genes. Moreover, genotyping errors of SNP array

platforms and poorly represented chromosomal

regions also limit the potential of this technology. If

ROH are incorrectly mapped by the introduction of

erroneous heterozygous genotypes, the analysis of

the causative gene will be compromised.

3 NEXT-GENERATION

SEQUENCING

NGS, and in particular whole-exome sequencing

(WES), prompted great advances in the study of

genetic diseases and gene discovery (Boycott et al.

2013; Ng et al., 2010). However, this technology has

still some limitations (Sirmaci et al., 2012). Firstly,

some deleterious variations may be in non-coding

regions, which cannot be detected by WES. Genetic

and phenotypic heterogeneity in affected individuals

makes exome sequencing difficult to interpret.

Sequencing errors related to poor capture efficiency,

mechanical and analytical errors, as well as

misalignment of repetitive regions, lead to erroneous

results. These hamper the analysis and impose the

need to validate candidate causative variants by

Sanger sequencing. Moreover, WES analysis

imposes the need of applying filtering strategies.

This step is critical as it will limit data analysis and

consequently influence the results. For instance,

almost half of the variants may be excluded for

being synonymous. Despite the fact that these are

usually not considered deleterious, numerous

synonymous mutations have been implicated in

human diseases (Sauna & Kimchi-Sarfaty, 2011). In

addition, during WES analysis a frequency filter is

usually set to exclude variants with minor allele

frequency above a certain threshold (above 1% in

most cases). This threshold should be set in

accordance with the expected prevalence of the

disease. Even so, considering that in recessive

disorders carriers do not show any signs of the

disease, the frequency of damaging alleles in

populational variant databases can still be higher

than the established threshold. This would lead to

the erroneous exclusion of this variant during

filtering.

Homozygosity Mapping using Whole-Exome Sequencing: A Valuable Approach for Pathogenic Variant Identiﬁcation in Genetic Diseases

211

Table 1: List of bioinformatic tools developed for ROH detection and their main features.

Software OS UI Algorithm

Main features /

experimental

design

Input

data

files

ROH

size

range

(Mb)

Other features Ref.

Homozygosity-

Mapper

Unix/Li

nux

(web

server

[a])

GUI

Detection of

homozygous

blocks of

selectable length

Perform

autozygosity

mapping from

SNP arrays and

NGS data

VCF

files

and

SNP

geno-

types

> 1.5

• Independent of

parameters like family

structure/allele

frequencies.

• Robust against

genotyping errors.

• Integrated with

GeneDistiller (candidate

gene search engine).

Seelow et

al., 2009

PLINK

Unix/Li

nux,

Mac

OS,

Win.

CLI &

GUI

Sliding-window

WGAS

analysis tool

set. Estimation

and use of IBD

in the context

of population-

based studies

BED,

PED,

and

FAM

files

[0.5 -

1.5]

> 1.5

• Makes a variety of

standard association tests.

• Maps disease loci that

contain multiple rare

variants in a population-

based linkage analysis.

• Integrated with

Haploview.

Purcell et

al., 2007

GERMLINE

Unix/Li

nux

CLI Sliding-window

Designed for

genome-wide

discovery of

IBD segments

shared within

large

populations

(SNP arrays)

PED,

MAP

and

Hap-

map

files

> 1.5

• Overcomes the

computational barrier of

pairwise analysis and can

scale the analysis linearly

with the sample size.

Gusev et

al., 2008

HomSI

Unix/Li

nux,

Mac

OS,

Win.

GUI Sliding-window

Identify ROH

in consangui-

neous families

from NGS data

VCF

files

> 1.5

• Takes into account

the distribution of the

variants within genomic

coordinates.

• Reported to be

consistent with data

derived from SNP

microarrays.

Gormez

et al.,

2014

Unix/Li

nux

CLI

Heterogeneous

hidden Markov

model

Analyse

medium and

short ROH

obtained from

WES data

BAM

files

< 0.5

[0.5-1.5]

> 1.5

• Reported to be more

accurate than GERMLINE

and PLINK, especially in

the detection of short and

medium ROHs.

Magi et

al., 2014

Agile-Genotyper

and

Agile-

VariantMapper

Win. GUI

User-controllable

visualization of

homozygous

regions

ROH analysis

WES data and

SNP

genotyping file

data

SAM;

tab-

delimite

d text

files

> 1.5

• AgileVariantMapper

uses the genotypes of all

positions found to be

polymorphic.

• AgileGenotyper

deduces genotypes from

positions previously found

to be polymorphic in the

1000 Genomes Project

data set.

Carr et

al., 2013

Footnote: [a]-http://www.homozygositymapper.org; CLI- command-line interface; GUI- graphical user interface; NGS-

Next-generation sequencing; OS- operative system; Ref.- references; ROH- runs of homozygosity; SNP- single nucleotide

polymorphism; UI- user interface; VCF- variant call format; WES- whole-exome sequencing; WGAS- whole-genome

association studies; Win.- Windows.

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3.1 Homozygosity Mapping using WES

Data

Recent studies have clearly demonstrated the power

and the effectiveness of applying homozygosity

mapping to WES data, in an attempt to identify

causative genes for Mendelian disorders (Gillespie

et al., 2014; Shamseldin et al., 2015). This approach

has the advantage of unraveling the causal variant

irrespective of the gene involved. The homozygosity

map allows narrowing down of the target data sets;

examination at the base-pair level then enables

identification of candidate causative variants.

This approach would start by mapping WES

reads against a reference genome (human hg19 in

our examples). Data derived from whole-genome

and RNA sequencing can also be used. This step is

usually performed through a bioinformatic pipeline

that generates SAM/ BAM (Sequence /Binary

Alignment Map) and, at the end of the process, a

variant call format (VCF) file. These files are then

used as input for homozygosity mapping analysis.

The position and zygosity of the obtained sequence

variants can be used to retrieve/infer ROH regions.

As all bioinformatic approaches, there is an error

rate associated that is difficult to estimate, since it is

highly dependent on the WES metrics and the

sequencing platform used.

Table 1 lists some of the tools available to

perform ROH. For instance, the web-based tool

HomozygosityMapper (Seelow & Schuelke 2012),

allows users to interactively analyze NGS data for

homozygosity mapping. Furthermore, PLINK

(Purcell et al., 2007) and GERMLINE (Gusev et al.,

2008), originally developed for the analysis of SNP

array data, are tools based on sliding-window

algorithms. In a sliding window analysis, the

statistics are calculated for a small frame of the data.

The window incrementally advances across the

region of interest and, at each new position, the

reported statistics are calculated. In this way,

chromosomes are scanned by moving a window of a

fixed size along their entire length and variation in

genetic markers across the region of interest can be

measured. This type of analysis reveals how

variation patterns change across a surveyed genomic

segment (Srinuandee & Satirapod 2015). EXome-

HOMozygosity is an example in which a sliding-

window algorithm (PLINK) is applied for WES-

based ROH detection (Pippucci et al., 2011).

However, the sliding-windows approaches

cannot be used easily with short/medium ROH sizes.

In order to solve this issue, Magi et al proposed a

new algorithm, H

, that is capable of detecting

smaller ROH (Magi et al., 2014). AgileGenotyper

(Carr et al., 2013) and HomSI (Gormez et al., 2014)

are other tools that can be used for the graphical

visualization of ROHs.

3.2 Case Studies

The following examples are shown to elucidate the

relevance and limitations of WES-based ROH. Two

patients listed in Table 1 have recessive conditions

caused by homozygous pathogenic variants

identified by WES. There was no indication of

parental consanguinity, and patient P2 had a sibling

affected by the same condition.

In both cases, retrospective analysis was based

on genome-wide homozygosity mapping performed

using HomozygosityMapper algorithm.

Data obtained from patient P1 revealed the

presence of two long stretches of homozygous SNPs

in chromosomes 1 and 17 (with 19 and 17 Mb,

respectively) (Figure 1, top panel). The affected

gene (CCDC103) is located in one of these regions,

specifically in chromosome 17. In this example the

proposed approach would be suitable to generate a

considerably shorter list of candidate loci, and

consequently reduce the number of variants to be

evaluated in terms of their pathogenicity. In the

second example the same strategy was applied in a

patient with a rare neuromuscular disease.

Table 2: Cases selected to illustrate the use of homozygosity mapping using WES data in patients with rare genetic diseases.

Patient Phenotype Genotype Reference

P1, male adult

Infertility due to total sperm immotility.

Clinical features compatible with Kartagener

syndrome. No known parental consanguinity.

Homozygous pathogenic

variant in CCDC103 gene:

Chr17(h19):g.42978470G>C

Pereira et al., 2015.

P2, female

neonate

Severe neuromuscular disease, congenital

hypotonia, respiratory distress and bone

fractures. No known parental consanguinity.

Homozygous pathogenic variant in

ASCC1 gene:

Chr10(hg19):g.73970545dupC

Oliveira et al.,

(submitted).

Homozygosity Mapping using Whole-Exome Sequencing: A Valuable Approach for Pathogenic Variant Identiﬁcation in Genetic Diseases

213

Chr. 10

Chr. 17

1.0 x max

0.9 x max

0.8 x max

0.7 x max

0.6 x ma

1.0 x max

0.9 x max

0.8 x max

0.7 x max

0.6 x max

Figure 1: Genome-wide homozygosity mapping using WES and the HomozygosityMapper software. Results are shown for

patients P1 and P2. Longer ROH are shown in red color. Inserted blue boxes show the genomic regions in more detail

where the disease-related gene is located.

Results for patient P2 revealed two smaller ROH

(1.8 and 0.2 Mb) in chromosomes 11 and 19

respectively (Figure 1, bottom panel). However, the

ASCC1 gene, carrying the disease-causing variant, is

located in chromosome 10. In this last example, and

considering the algorithm used, the analysis based

on the assumption of a homozygous variant located

in a ROH would clearly fail. The causal gene would

not be included in the list of candidate genes, most

likely due to the size of the actual ROH where the

genetic defect is located. In the first example, the

long ROH can be attributed to consanguinity

(although it was not formally confirmed), while in

the second case, a distant common ancestor could

explain the presence of a rare pathogenic variant in a

smaller ROH tract.

4 CONCLUSIONS

The aim of this position paper is to revisit

homozygosity mapping as an important tool for

clinical genetics in cases where a recessive disease is

suspected. We have reviewed the proposed

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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.

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APPENDIX

Appendix: Integration of homozygosity mapping in the analysis workflow of whole-exome sequencing (WES).

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