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\begin{document}

\title{Phasing of de novo mutations using a scaled-up multiple amplicon
long-read sequencing approach}



\author[1]{G.S. Holt}%
\author[1]{L. Batty}%
\author[1]{B. Alobaidi}%
\author[1]{H. Smith}%
\author[2]{Manon Oud}%
\author[2]{Liliana Ramos}%
\author[1]{M.J. Xavier}%
\author[1]{Joris Veltman}%
\affil[1]{Newcastle University}%
\affil[2]{Radboud Universiteit Donders Institute for Brain Cognition and Behaviour}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
\emph{De novo} mutations (DNMs) play an important role in severe genetic
disorders that reduce fitness. To better understand the role of DNMs in
disease, it is important to determine the parent-of-origin and timing of
the mutational events that give rise to the mutations, especially in
sex-specific developmental disorders such as male infertility. However,
currently available short-read sequencing approaches are not ideally
suited for phasing as this requires long continuous DNA strands that
span both the DNM and one or more informative SNPs. To overcome these
challenges, we optimised and implemented a multiplexed long-read
sequencing approach using the Oxford Nanopore technologies MinION
platform. We specifically focused on improving target amplification,
integrating long-read sequenced data with high-quality short-read
sequence data, and developing an anchored phasing computational method.
This approach was able to handle the inherent phasing challenges that
arise from long-range target amplification and the normal accumulation
of sequencing error associated with long-read sequencing. In total, 77
out of 109 DNMs (71\%) were successfully phased and parent-of-origin
identified. The majority of phased DNMs were prezygotic (90\%), the
accuracy of which is highlighted by the average mutant allele frequency
of 49.6\% and a standard error margin of 0.84\%. This study demonstrates
the benefits of using an integrated short-read and long-read sequencing
approach for large-scale DNM phasing.%
\end{abstract}%



\sloppy


\textbf{Introduction}

\emph{De novo} mutations (DNMs) arise from mutational events that occur
during gametogenesis in either parent germ cells, or postzygotically in
both somatic and germ cells of the individual carrying them. On average,
one to two DNMs can be found in the coding region of a person's genome
(Durbin et al., 2010; O'Roak et al., 2011; Xu et al., 2011). DNMs are of
particular significance due to their contribution to many diseases and
genetic disorders, notably those affecting individual fitness such as
intellectual disability and male infertility (Awadalla et al., 2010;
Veltman and Brunner, 2012; Gilissen et al., 2014; Acuna-Hidalgo et al.,
2016; Taylor et al., 2019; Oud et al., 2022). It has been shown that
approximately 80\% of DNMs are of paternal origin (Kong et al., 2012;
Goldmann et al., 2016; Yuen et al., 2016; Oud et al., 2022). A major
factor known to contribute to an increase in DMNs in individuals is
advanced parental age at the time of conception, particularly paternal
age (Kong et al., 2012; Goldmann et al., 2016). Investigating the
parental origin and timing of DNMs provides not only biological insight
into the generation and ability of these DNMs to underlie genetic
disorders, it has also been shown to be important for determining the
recurrency risk of these disorders (Campbell et al., 2014; Almobarak et
al., 2020).

Phasing analysis interrogates the diploid genome, allowing allele
separation of the parental chromosomes. This helps not only to determine
the parental origin and timing of DNMs, but is also critical to identify
compound heterozygous mutations and look into allele specific
expression, linked variants, and structural variation (Tewhey et al.,
2011; Soifer et al., 2020; Ebert et al., 2021). With short-read whole
genome sequencing (WGS) of parent-offspring trios, 15-20\% of DNMs can
be successfully phased and parent-of-origin called (Goldmann et al.,
2016). However, this percentage is expected to be even lower in whole
exome sequencing (WES). Phasing challenges can be attributed to the
limited sequencing read lengths, the presence of intronic gaps, and the
reduced amount of genetic variation in the exonic regions compared to
intronic regions (Frigola et al., 2017). By definition, germline DNMs
need to be absent in the parental somatic cells, requiring trio-based
exome or genome sequencing of parent-offspring trios for discovery. In a
next step, the parent-of-origin and zygosity of a DNM can be identified
by targeted amplification and long-read sequencing of a region spanning
the DNM as well as one or more parentally informative single nucleotide
polymorphism (iSNPs). While this appears straightforward, long-read
sequencing has both random and positional error, which may result in
false variants used for phasing, reducing reliability of downstream
analysis (Magi et al., 2018; Watson and Warr, 2019).

There are numerous methodologies to target genomic regions for
enrichment prior to sequencing, with the majority being divided into
PCR- or CRISPR-based approaches (Hafford-Tear et al., 2019; Gilpatrick
et al., 2020; Player et al., 2020). Importantly, when mapping sequence
data to the reference genome from CRISPR targeting approaches, the
off-target mapping of the sequences is several fold greater than PCR
based methods and target coverage is therefore often many factors lower
(Hafford-Tear et al., 2019; McDonald et al., 2021), and costs per target
are significantly higher. Innate challenges also exist with PCR
approaches, including the presence of inhibitors, variable target
length, optimisation time, amplification bias, and nucleotide errors
(Potapov and Ong, 2017; Shagin et al., 2017). However, despite these
challenges with long-range PCR enrichment, the approach is arguably more
effective for scaled-up targeted phasing at present.

This study aims to identify the DNM parent-of-origin and zygosity using
a targeted long-range PCR approach for phasing 109 distinct DNMs
previously identified in infertile men by patient-parent trio exome
sequencing (Oud et al., 2022). Targeted amplification of regions
encompassing each unique DNM is performed using an optimised long-range
PCR workflow designed to quickly increase PCR success rates and reduce
pre-sequencing base error. The combination of exome patient-parent trio
data, targeted ONT sequencing and validated DNMs are used to improve
phasing and allele frequency confidence. Critical aspects of the process
are assessed to ascertain practical application for large-scale use of
the approach, including amplification length, long-read sequencing error
rates and overall phasing performance.

\textbf{Methods}

\emph{Ethical considerations and study participants consent}

77 infertile male patients with unexplained (idiopathic) non-obstructive
azoospermia or severe to extreme oligozoospermia and their unaffected
parents were enrolled from the Radboudumc outpatient clinic and
Newcastle upon Tyne Hospitals NHS Foundation Trust (Newcastle, UK). This
study was approved by the ethics Committees/Institutional Review Boards
((Nijmegen: NL50495.091.14 version 5.0, Newcastle: REC Ref: 18/NE/0089).
Written informed consent from all patients and their parents was
obtained.

\emph{Whole Exome sequencing}

Genomic DNA was extracted from whole blood for all patients at the time
of evaluation and treatment at the fertility centre. Parental DNA was
obtained from saliva using the Oragene OG-500 kit (DNA Genotek, Ottawa,
Canada). Exomes of patient-parent trios were prepared for sequencing
using 1 \selectlanguage{greek}µ\selectlanguage{english}g of high-quality genomic DNA, which was quantified with the
Qubit dsDNA HS kit (Thermo Fisher Scientific, Waltham, MA, USA). Whole
exome enrichment was performed using Illumina's Nextera DNA Exome
Capture kit (Illumina, San Diego, CA, USA) or Twist Bioscience's Twist
Human Core Exome Kit, as per manufacturer protocols. Individual sample
libraries were then indexed with Illumina Nextera DNA UD indexes
(Illumina, San Diego, CA, USA) prior to pooling libraries together. The
final pooled library was sequenced on the NovaSeq 6000 platform
(Illumina, San Diego, CA, USA) to an average coverage depth of 72X
(Illumina Nextera kit) or 99X (Twist Bioscience's kit).

\emph{DNM/iSNP calling and DNM validation}

Sequence reads were aligned to the Human Genome Reference Assembly
GCRh37 using Burrows-Wheeler Alignment (BWA) version 0.7.17 (Li, 2013)
and indexed using SAMtools (Li et al., 2009) and Picard version 1.6
(PicardToolkit, 2019). SNVs and indels were subsequently called by the
Genome Analysis Toolkit (GATK)) HaplotypeCaller v4.1.4.1 (McKenna et
al., 2010). \emph{De novo} analysis was performed using custom-designed
in-house analysis pipelines and validation of DNMs in the proband and
parents was performed using standard Sanger Sequencing (Oud et al.,
2022). iSNP calling was also performed using an in-house pipeline, which
called proband and parent variants using Clair v2 (Luo et al., 2020),
proband variants were selected based on unique parental inheritance with
a minimum parental coverage of 5x. A single iSNP is selected for
parent-of-origin assignment, see section `Bioinformatics'

\emph{Target amplification}

In 77 infertile males analysed, 109 rare protein coding DNMs were
identified and PCR amplified targeted phasing performed, for a basic
schematic of the methods see Supplementary Figure 1. Irrespective of
known iSNP presence, primers were designed for the target regions
(determined as per Figure 1.b) of all 109 DNMs (Supplementary Table 1)
using our in-house pipeline which designs primers using Primer3 version
2.3.6 (Untergasser et al., 2012) and Human Genome Reference Assembly
GCRh37. Automated rounds of primer creation were run for each target,
selecting the most optimal primer pair based on predicted fragment size,
self-binding, hairpin factors, reverse primer to forward primer binding,
and non-specific genome binding. All expected fragment sizes were
limited to a maximum length of 12 kb for greater enrichment success and
speed, though optimisation steps were required for 50 \% of the targets
(Supplementary Figure 2).

DNMs without exome supported iSNPs within a 10 kb region were selected
for intronic iSNP searching within 5kb (Figure 1.b3). For this group
primers were designed in 5 kb windows around the DNM. iSNPs from the
\textasciitilde{}5 kb region were identified in the introns of parental
ONT data in the downstream bioinformatics. Long-range PCR target
enrichment was carried out using optimised running conditions of 2
separate supermixes/enzymes, BioRad iproof and TakaraBio PrimeSTAR GLX
(Supplementary Table 2). Primary steps use dilution to reduce
contaminants and inhibitors, where dilution was not enough a hotstart
supermix (Quantabio RepliQa HiFi ToughMix) was used which reduces the
need for annealing temperature specificity (Supplementary Table 2). The
number of primer pairs that required bespoke optimisation was reduced to
just 12\% of the samples by using broad and basic steps as per the
Supplementary Figure 2b. Only 5 \% of the primer pairs required
re-designing, the rest were successfully optimised across the first 5
stages in supplementary Figure 1b. Supplementary Figure 1b steps were
chosen based on simple and effective PCR optimisation that could be
easily applied on a broad scale for a large number of samples. These
measures gave a long-range PCR success rate of \textgreater{}80\%,
reducing the time required for primer specific optimisation and/or
re-design. Sample fragment sizes were confirmed using gel
electrophoresis, and quantities were measured with the Qubit dsDNA HS
kit (Thermo Fisher Scientific, Waltham, MA, USA), with the best quality
supermix enrichment for each given sample/target selected for
sequencing.

\emph{Optimising scalability}

On average 30 targets per MinION run were amplified, pooled and
sequenced. Initially, a limited number of 11 samples were barcoded and
sequenced, but a reduction in usable data was noted (Supplementary Table
3). As such, this approach was replaced with careful sample pooling
instead. The 11 samples were not re-sequenced, as the small number meant
there was still ample data per target (mean of 19 million bases per
target). Targets were checked to ensure no overlap in genome location,
and demultiplexing was performed based on region extraction of the
mapped BAM files (see methods `Bioinformatics'). Using this approach, a
loss of 17.8\% (SEM 7.2) of total basecalled yield was avoided by
bypassing barcode ligations, de-multiplexing binning issues and barcode
trimming (Supplementary Table 3).

\emph{Long-read sequencing}

The long-range PCR target enrichments of \textgreater{}20 ng were
prepared for sequencing with the ONT ligation sequencing kit
(SQK-LSK109) following the manufacturer's protocol, with adjustments for
sample type and yield. Individual sample libraries with \textless{}20 ng
were concentrated at given bead clean-up steps. For sample pooling
steps, samples were pooled in groups based on fragment size, to allow
for more accurate normalisation prior to final pooling and flow cell
loading. Prepared samples were sequenced on the MinION using the
FLO-MIN106 version 9.4.1 flow cell platform. Flow cells were run until
complete pore exhaustion, on average this was 72 hours, with minimal
refuel of flow cells performed whenever active pore percentages dropped
below 70\%, achieving up to 30 billion basecall yields per flow cell and
an average coverage depth per sample of \textgreater{}30,000x.

\emph{Bioinformatics: Basecalling, fastq clean up, and demultiplexing}

The sequence signal data in multi-fast5 format were basecalled using
Guppy version 3.4.4. The resulting fastq outputs were adapter trimmed,
low-quality reads ends trimmed (-q 10), and short reads of \textless{}30
base pairs removed using cutadapt version 2.5 (Martin, 2011). Cleaned
fastq files were mapped against Human Genome Reference Assembly GCRh37
using BWA (Li, 2013) (version 0.7.17), and sample targets were extracted
from the resulting BAM file using SAMtools (Li et al., 2009) (version
0.1.19).

\emph{Bioinformatics: Variant calling}

Variants were called using Clair v2 (Luo et al., 2020) and filter
parameters for variants required \textgreater{}20x coverage, allele
frequency 20-80\%, 96\% confidence, and allele agreement with the
validated DNM. The remaining variants were error polished using the high
base accurate WES data, removing variants that disagree with any
overlapping WES data. WES data for patients and parents was used in
filtering ONT called variants illustrated in Figure 2 and Supplementary
Tables 4 and 5. Intronic variants identified were further error polished
where the inheritance information was available from long-read parental
data.

\emph{Bioinformatics: Phasing}

To identify reliable variants, reads were split based on DNM base type,
and the remaining variants were checked for agreement with the split
reads using bamql (Masella et al., 2016). The iSNP with the greatest
combinations of split read agreement, coverage and additional supporting
sequencing data was selected for final phasing, see Supplementary Table
6 for iSNP information. This preliminary variant sorting and iSNP
selection approach provided a more reliable variant list. The next step
split the raw reads by base type at DNM and selected iSNP positions
using bamql. After this step the allele frequencies, parent-of-origin,
and timing of the DNM event (pre/post zygosity) was determined. The
parent-of-origin conclusion was re-affirmed manually in IGV by
visualising the reads containing the DNM and the chosen iSNP from the
pipeline.

Additional to manual analysis in IGV, postzygotic and prezygotic DNMs
were determined by three category assessments, those that appeared
postzygotic are detailed in supplementary Table 11. The categories are;
WES DNM base information (coverage/frequency), ONT DNM base information
(coverage/frequency), and ONT allele information (coverage and allele
frequencies). This information also helped determine background allele
error, by presenting reads in the data that represent third and fourth
allelic forms.

Allele and base error were calculated in two ways, one approach
represented total error, which was calculated for a target DNM by
combining all known false base frequencies for total base position
error, and for total allele error, combining all known false allele
frequencies. The other approach used the most prevalent base or allele
frequency that could be determined as false. Assessment of error and
noise in data helped support prezygotic and postzygotic DNM calls.

To provide an assessment of basecalling background error that is allele
relevant, we ignored checking every base within every target as that
would be unnecessarily extensive and time consuming. Instead, we used
the highest false base percentage of each iSNP used in phasing each
target and calculated the mean false base percentage (Supplementary
Table 7). Furthermore, the false base could be qualified as it had
parental data support. A quality assessment of the bases for each target
within the BAM files was performed using Picard
`QualityScoreDistributions' (PicardToolkit, 2019).

\emph{Results}

Whole exome sequencing (WES) of 77 infertile males and their unaffected
parents identified 109 rare \emph{de novo} mutations (DNMs), all of
which were independently validated by Sanger sequencing. Accurate
phasing and parent-of-origin calling of the DNMs requires DNA molecules
spanning a parentally informative single nucleotide polymorphism (iSNP)
and the DNM (Figure 1.a). As such, the ability to call the
parent-of-origin is primarily reliant on read lengths. This is notable
in the WES data, where only 8\% of DNMs could be phased as most iSNP
were located \textgreater{}300 bp away from the DNM (Figure 1.b1 and
Table 1).

\emph{Target amplification groups}

For phasing, all DNM regions were targeted with long-range PCR and
sequenced using ONT long-read sequencing. Primer pairs were designed for
targeted long-read phasing of the 109 targets (Supplementary Table 1).
The fragment size was capped at \textasciitilde{}10 kb to simplify PCR
optimisation. Unfortunately, amplification success was still impacted by
target length within the 10 kb range, with ideal lengths found to be
\textless{}4kb (Supplementary Figure 2.a). PCR optimisation steps were
required for 50\% of all targets (Supplementary Figure 2.b). The
amplification size had no impact on error or quality in base calls or
allele assignment (Supplementary Figure 3 and Supplementary Table 7).
Importantly, for 71\% of cases an iSNP was identified in the available
trio-based WES data within 10 kb of the DNM (Figure 1.b1 \& 1.b2). For
this group of DNMs, phasing can be done by targeted long-read sequencing
of the proband only, since the iSNP is already typed in patients and
their parents.

Cases where an iSNP could not be found in the coding region had primers
designed to cover 5 kb regions around the DNM position for parent and
proband samples (Figure 1.b3). We chose the 5 kb region based on the
analysis of 4344 DNMs identified in 53 whole genome sequenced
individuals/children (Smits et al., 2022). This revealed the presence of
at least 1 iSNP within 5 kb of any given DNM in 81\% of cases
(Supplementary Tables 8 and 9). In the end, we obtained long-read
sequencing data with iSNPs for 77 out of 109 DNMs selected (71\%,
Supplementary Table 8 and 10).

\par\null

\emph{Variants, filtering, error polishing, and phasing}

PCR targeted long-read sequencing of the successfully phased 77 DNMs
produced an average allele coverage of 35,430X, with a mean background
noise of 4\% at iSNP positions (see Supplementary Table 7 and methods
section `Bioinformatics' for background noise calculation). This
increased coverage compared to alternative targeting methods, WES or WGS
is expected to help with error reduction and mosaicism detection (Wright
et al., 2019). The DNMs, which were initially identified with short-read
WES and validated with Sanger sequencing, were used to anchor the
preliminary phasing of long reads. This anchoring approach groups
long-reads by the base information at the DNM position. After variant
calling was performed (methods section `Bioinformatics'), homozygous
variants were removed and heterozygous variants were checked and
filtered based on agreement with the DNM grouped reads. All remaining
variants from the long-read sequencing approach were error polished and
filtered using WES and parental ONT data (Figure 2 and Supplementary
Tables 4 and 5). Following this, iSNPs were identified from the
remaining variants. The iSNP with the greatest confidence (coverage,
supporting data, DNM allele agreement) was selected for phasing. When
phasing the reads based on the DNM and selected iSNP, additional alleles
were allowed for the DNM in case of a postzygotic event, but these were
screened for credible biological relevance, i.e. the DNM wild type (wt)
would have to match the iSNP of the DNM alt. Importantly, this anchored
approach filtered out 10\% more presumably falsely called variants in
comparison to the standard filtering of variants based on quality
criteria, sequencing coverage and consensus (see Supplementary Table 4).
Small indels made up on average 25\% of the false positives, and on
average 94\% of all indels detected in the long read sequencing data
were likely false positives (see Supplementary Table 5, and for an
example of a false indel see Supplementary Figure 7). Because of this
high error rate for indel calling, we decided to remove all indels
without supporting data available, which is noted in the illustration of
our approach (Figure 2).

\emph{Validation of the phasing approach using WES data}

The parent-of-origin for the DNMs was initially assessed from the
short-read WES data. Short-reads, 100bp in length, encompassing DNM and
iSNPs in the WES data were available for 14 of the 109 DNMs, but only 9
(8\%) (Figure 1.b1) had acceptable coverage (\textgreater{}10x per
allele, Table 1). These DNMs were also target amplified and sequenced
using ONT long-read sequencing to provide a control cohort for our
long-read phasing method. For all 9 DNMs with acceptable coverage in the
WES data, the parent-of-origin assignment agreed between the two
approaches and the DNM allele frequencies obtained were comparable
(Table 1). Interestingly, this was also true for the 5 WES phased
samples with limited sequencing coverage, suggesting that the stringent
requirement of \textgreater{}10x coverage for WES phasing could be
reduced to as little as 4x. For both short and long-read sequencing, 11
out of 14 DNMs showed allele frequencies around 50\% (+/- 10\%), with no
significant third allelic form observed (supplementary Table 11),
indicating that these were germline DNM events. Of the three DNMs
deviating from prezygotic allele frequencies, two were determined as
postzygotic (SORCS2 and C10orf71), and one (SIGLEC10) may result from
allelic sequencing bias (supplementary Table 12). Coverage of the ONT
long-read sequencing for SIGLEC10 was significantly reduced compared to
average ONT sequencing coverage, and while both WES and ONT allele data
showed DNM allele frequency deviations from 50\%, no significant third
allelic form was observed. In addition, the DNM base frequencies of both
WES and ONT data were within the prezygotic range of 50\% +/- 10\%.

For the DNM affecting the C10orf71 gene in patient 01247, the \emph{de
novo} mutated allele (T-A) had a much lower allele frequency of 9\% and
17\% when detected with both WES and ONT approaches respectively, with
similar percentages observed in DNM base frequencies. Apart from the wt
allele and the DNM allele, we clearly observed a third allelic form that
represented the wt version of the DNM allele. The presence of this third
allelic form suggests that the DNM likely occurred as a postzygotic
event. The postzygotic mutation is observed in both the WES and ONT data
in this case. Postzygotic mutations can, however, be missed when WES has
low coverage, as seen for the DNM in SORCS2 in patient 01209. The
postzygotic DNM in SORCS2 presents an average discrepancy of 15\% from
prezygotic norms in the WES base frequencies. A third allelic form is
not shown in the WES allele data due to only having 1x coverage. Greater
base discrepancies are observed in the ONT base frequencies, with an
average deviation from prezygotic norms of 38\%, and with several
thousand times more coverage in the ONT data, a wt of the DNM allele is
observed at 18\%.

\emph{WES vs ONT DNM allele frequency comparison and parent-of-origin}

In total, 77 DNMs of 109 DNMs (71\%) were phased and the
parent-of-origin determined, with 64 of these (83\%) being of paternal
origin. Across the 77 phased DNMs there was a DNM allele frequency range
of 6\% to 78\% in the ONT data and 15\% to 71 \% in the WES DNM base
data, clearly showing the presence of postzygotic and prezygotic
mutations. From these 77 DNMs, our targeted long-read sequencing
approach identified 61 clearly prezygotic DNMs, with a possible third
allelic form found in the remaining 16 DNMs. The separation of pre and
postzygotic DNMs can be established more clearly through a combined
analysis of their base frequencies, allele frequencies, DNM wt allele
frequency, and background sequencing error (see Supplementary Table 11).
By doing this we confirmed 69 prezygotic DNMs and 8 postzygotic
mutations, as depicted in Supplementary Table 11 and 12, and
Supplementary Figure 4 and 5. On average a third of allelic data is
identified as false in our ONT approach (Supplementary Table 7), and
that impacted the allele frequency significantly. After removal of these
false allelic data, we noticed that the average ONT allele frequency for
prezygotic DNMs were around 50\% with an average of 49.6\%, with a
standard error margin (SEM) of 0.8\% (Supplementary Table 13 and
Supplementary Figure 4). For germline DNMs, we expect the mutant allele
frequency to be approximately 50\%. shows that the majority of all ONT
DNM allele frequencies were around 50\% with the prezygotic average of
49.6\% (SEM 0.8\%). While raw allele frequencies were expectedly much
worse than DNM base frequencies, the polished allele frequencies are
slightly more accurate compared to the WES DNM and ONT DNM prezygotic
nucleotide base frequencies of 48.6\% (SEM 1.0\%) and 48.8\% (SEM
0.8\%), respectively (Supplementary Table 13). As expected, postzygotic
frequencies of mutant alleles deviated significantly from this, with an
average of 15.9\% and a standard error margin of 2.4\% in ONT allele
data, though the frequencies of postzygotic mutations is less consistent
in WES data, where the postzygotic frequency average was 28.8\% (SEM
4.9\%) (Supplementary Table 13).

The parent-of-origin was also investigated within the pre and
postzygotic fractions, where we observe the prezygotic paternal
preference of 83\% and the postzygotic paternal origin of 62\% (5 out of
8), see Supplementary Figure 5. Classification of postzygotic DNMs was
determined as per Supplementary Table 11 and a detailed IGV illustration
is depicted in Figure 3. All postzygotic calls were corroborated by the
DNM base frequencies in the short-read and long-read data, and further
supported by all identified allele frequencies and the degree of error
in target data (see Supplementary Table 7 and 11).

\emph{Discussion}

In this study we optimized and applied a multiplexed long-read
sequencing approach which makes use of high-quality short read exome
data to perform routine phasing of \emph{de novo} mutations. We report
the phasing results of 77 DNMs from 64 of our 77 patient-parent trios
linked to male infertility, achieving successful phasing for 71\% of the
109 DNMs investigated using this long-read targeted approach. In
contrast, only 9 of these DNMs (8\%) could be reliably phased based on
short-read WES data alone.

Short-read exome sequencing has become an increasingly common tool in
research and diagnostic of genetic disease, with patient-parent
trio-based sequencing routine for the detection of DNMs. With only 8\%
of DNMs being phasable in our cohort of 77 patients when using
short-read WES alone, it is clear that an alternative approach is needed
to determine the parent-of-origin and timing of DNMs. Our method uses
the long-range PCR with standard optimisation steps to achieve the
simplest and quickest large-scale success. PCR is a simple and standard
wet lab practice, providing greater enrichment and target specificity
than any alternative target-based approach. The sequencing of target
enriched long DNA strands with ONT allowed us, in most cases, to acquire
10s of thousands of times coverage per target, with many targets run per
flow cell, supporting the projects scale demands. To overcome challenges
with error and postzygotic mutations we used the WES data and Sanger
validated DNMs to polish the variant analysis, which limited
computational demand as no complex algorithms were required, and
processing could be quick.

DNMs are known to arise from mutational events occurring during
gametogenesis, predominantly during spermatogenesis rather than during
oogenesis, which is assumed to be associated to the scale of male gamete
production and failure of DNA repair mechanisms which lead to the
increased opportunity for mutational events to occur (Aitken \& Baker,
2020; Evenson et al., 2020; Gr\selectlanguage{ngerman}égoire et al., 2013; Haldane, 1947; Kong
et al., 2012). Previous literature has shown that DNMs occur on the
paternal allele approximately 80\% of the time (Kong et al., 2012;
Goldmann et al., 2016; Yuen et al., 2016). In agreement with this
literature, 83\% of all phased DNMs in this study were determined to be
of paternal origin.

Parent-of-origin and zygosity information adds another layer to our
understanding of potential disease-causing variants. This is important
when investigating genetic diseases, especially those that likely have
complex and varied mechanisms. In our cohort of 77 patients, 51 patients
were confirmed to suffer from non-obstructive severe oligospermic or
azoospermic phenotypes. In the original publication related to this work
(Oud et al., 2022), we showed that 6 out of the 8 likely causative DNMs
identified in these patients were of paternal origin (Supplementary
Table 8 and 14, Supplementary Figure 6). This suggests that DNMs with a
deleterious effect on the health of an individual can escape negative
selection in the paternal germline.

Accurate detection of the DNM allele frequency is critical to
differentiate prezygotic from postzygotic mutational events, important
in clinical settings for estimating the recurrence risk (Almobarak et
al., 2020; Scanga et al., 2021). Our approach yielded a highly accurate
allele frequency average of 49.6\% in the prezygotic mutations, with an
SEM of 0.84\% (Supplementary Table 13). Though similar accuracy may be
achievable with more computationally demanding methods, the strength of
our method lies in utilizing the WES data and DNM validation practices
commonly available. This shows that bioinformatic cleaning and more
complex haplotype processing steps are unnecessary, with accurate
results achievable through simple DNM and DNM-anchored iSNP selection.
In total, 8 of the 77 phased DNMs were classified as postzygotic events
(10\%), largely in agreement with current literature results of 6.5\% to
10\% (Acuna-Hidalgo et al., 2015; Ye et al., 2018; Sasani et al., 2019),
supporting the validity of our method. Interestingly, while there was
significant correlation between WES and ONT postzygotic base/allele
frequencies, 25\% of the postzygotic DNMs could not be determined from
WES DNM base frequencies. This demonstrates the importance of combining
phasing analysis with deep coverage long-read sequencing to further
characterise the timing of DNMs. As can be expected for postzygotic DNMs
(Girard et al., 2016), we see less paternal bias even though our numbers
are small (5 out of 8 postzygotic DNM are paternal, 62\%).

We here use a standard PCR amplicon targeting approach with long-read
sequencing, rather than CRISPR-Cas targeting. Despite CRISPR-Cas
recently becoming a choice method for long-read targeted sequencing
(Hafford-Tear et al., 2019; Liu et al., 2019; Gilpatrick et al., 2020;
McDonald et al., 2021), the large number of targets and small target
sizes in our cohort would make CRISPR-Cas complex and costly. Standard
PCR targeting is optimal for routine application that does not require
methylation data, read lengths greater than 10-20 kb, or directly
representative read counts (Aird et al., 2011). While the CRISPR-Cas
approach can have a 10-100 fold enrichment of the target region compared
to standard low coverage long-read WGS, it still results in 95.4 \%
off-target sequencing (Gilpatrick et al., 2020). This off-target
sequencing issue significantly limits the number of samples that can be
run per flow cell, and only a single sample can be run if demultiplexing
is based on the genomic position of the target. The reverse is seen when
comparing this to the standard amplicon approach used herein, where
dozens of samples were run per flow cell and no off-target mapping was
identified. Based on using the optimal CRISPR-Cas approach of 2-3 gRNAs,
and taking into account the reduced sample number per flow cell,
CRISPR-Cas methods also have \textgreater{}40 fold increase in cost per
target. Nonetheless, CRISPR-Cas target enrichment shows great promise,
and will likely be the best approach for targets larger than 10-20 kb.
Despite not observing more basecalling error from PCR extension in
targets of greater sizes, it is worth considering the potential
increases in base error and bias from PCR approaches which would
compound the lower accuracy inherent to long-read sequencing. Our data
supports the importance of minimizing target region sizes when
performing PCR based amplification for targeted sequencing, especially
when performing primer optimisation for \textgreater{}100 bespoke primer
pairs. Limiting target sizes will reduce labour intensive PCR
optimisation and though not observed in our study it may also reduce
base error from PCR fidelity issues. We should, however, be mindful that
for 11\% of the DNMs studied no iSNPs were found within the 5kb window,
so minimizing the target region can also negatively impact phasing. For
another 18\% of DNMs, however, the sequencing data was of insufficient
quality for phasing purposes, so clearly a balance must be found between
sequencing quality and target size.

Since ONT released the MinION platform in 2014, there have been
extensive leaps in advancing both the chemistry and the bioinformatic
tools. This has resulted in raw base accuracy moving from as low as
\textasciitilde{}60\% (Loman and Watson, 2015) to the current 92-97\% in
the 9.4.1 flow cell chemistry used in this investigation. It should be
noted that further increases in accuracy have also been suggested in
recent flow cell chemistry, such as the release this year of the R10.4.1
flow cell. Bioinformatic tools that include the variant caller `Clair',
used here, have also shown increased confidence in variant calls but are
thought to be reaching their limit, with greater confidence requiring
significant alternative algorithms or improvements in chemistry (Luo et
al., 2020). Despite the bioinformatic improvements in base calling and
variant calling, we observe that the accuracy of long-read data on
long-range PCR products still causes far greater false positives than
WES short-read data. After filtering ONT variants by read depth and
quality scores, our anchored approach filtered an additional 50\% of the
remaining variants on average. If false variants that were missed prior
to our anchored filtering approach were included in the phasing process
it is likely some targets would be phased incorrectly or not phased at
all. Many phasing tools such as `whatshap' carry out phasing with the
understanding that variants within the vcf file are correct, so the
removal of false variants is important.

Our study provides an approach for accurately phasing and
parent-of-origin calling DNMs in a set of 77 patients. To our knowledge
this is the first time that phasing of DNMs has been investigated on
this scale using long-range PCR targeted ONT sequencing, where each
sample has a uniquely specific target. We optimized the method for
efficiency and streamlined the laboratory and computational pipelines
for processing large numbers of DNMs for detailed phasing analysis. We
incorporate additional short-read sequencing patient-parent trio data
and Sanger validated DNMs that are commonly available from DNM discovery
pipelines like ours. This approach enabled us to improve DNM phasing and
postzygotic calling. This data-supported and anchored phasing approach
can be of great use in both research and diagnostic settings where DNMs
are routinely studied and interpreted.

Authorship

Study was designed by J.A. Veltman and G.S. Holt. G.S. Holt performed
primer design, some primer optimisation, ONT sequencing, bioinformatics,
data analysis, manuscript curation. L. Batty performed primer
optimisation, ONT sequencing and DNM Sanger validation. B. Alobaidi
performed DNA extractions, WES sample prep, some PCR optimising, some
ONT sequencing, and DNM Sanger validation. H. Smith performed DNM
selection and disease causative classifications of DNMs. M.S. Oud
performed DNM selection, manuscript review and disease causative
classification of DNMs. L. Ramos provided clinical data. M.J. Xavier
performed DNM identification in WES data and manuscript review. J.A.
Veltman manuscript review. All authors contributed to the final paper.

Acknowledgements

We appreciate the participation of all patients and their parents in
this study. We thank Arron Scott and Bryan Hepworth (Newcastle
University) for technical support, the genomics core facility for whole
exome sequencing (WES), the Bioinformatic support unit (BSU) for initial
WES processing. This project was funded by an Investigator Award in
Science from the Wellcome Trust (209451) to J.A.V.

Conflict of Interest Statement

\emph{The authors declare no competing interests}

Data Availability Statement

\emph{Further data is available upon request from authors}

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\emph{Tables}

\textbf{Table 1 Comparison of WES and targeted long-read phasing.} All
targets have at least 1 read covering the DNM and iSNP in the WES data.
Quality of WES phasing is decerned by the coverage cut-off of
\textgreater{}10x coverage per allele. Green bases and alleles represent
wt, and red bases and alleles depict the mutant. The given iSNP base
pair distance is in relation to the DNM position.

\emph{Figures}

\textbf{Figure 1} \textbf{(a)} Pedigree chart illustrating the bases of
an iSNP and a DNM. To phase a DNM and determine the parent-of-origin,
single reads must span from the DNM to the iSNP without fragmentation.
The distance between these two points of interest dictates the
feasibility of phasing the target region with short or long read
sequencers. PCR amplification followed by long read sequencing allows
target read lengths of up to \textasciitilde{}10,000 bp.
\textbf{(b)}Illustration of the three categories in which targets are
grouped and phased. The grey boxes upon the thin line in each category
(1, 2, and 3) represent existing exome read coverage. Each category is
based on the distance between the iSNP and DNM, which determines whether
it can be phased with only the existing short-read WES trio data (1),
WES and long-read proband data (2) or WES and long-read trio data (3).
The percentage of samples that fit each category is highlighted in blue.

\textbf{Figure 2} \textbf{Using validated DNM for an anchored phasing
pipeline.} Pipeline overview for phasing, including illustration of
variant strength and filtering. Reads are split by their Sanger
validated DNM nucleotide, then homozygous variants are identified in the
two groups. These variants are compared to additional supporting
sequencing data and prioritised for phasing based on their primary,
secondary, tertiary, and unvalidated categories. These categories are
based on the extent of the variant validation from WES and ONT Trio
data. Small deletions that have no alternative supporting data are
removed due to their high false call rate. An iSNP is selected based on
the sequencing support category and used to phase the original proband
ONT mapped reads, after which the allele frequencies, zygosity, and
parent-of-origin are determined.

\textbf{Figure 3} \textbf{Example of the postzygotic DNM in C10orf71
visualized in IGV} . The iSNP and DNM base associated (coloured) counts
and percentages are displayed at the top of the plot as a descriptive
extension of the coverage bar. This postzygotic DNM is observed in the
ONT and WES allele data and is supported by the discrepancies in the DNM
positional base frequencies, while the iSNP base frequencies remain
close to the expected 50:50 ratio. The IGV visual of the reads presents
them grouped by iSNP base type. This read view illustrates a third
allelic form in the reads that matches the base and iSNP combination
expected, had the DNM not occurred (C-A 33\% in ONT, 41\% in WES).

\emph{Supplementary Tables}

\textbf{Supplementary Table 1} DNM target region primer list.

\textbf{Supplementary Table 2} Long-range PCR reaction mixes and running
conditions.

\textbf{Supplementary Table 3} Base loss breakdown of the 11 barcoded
samples after demultiplexing and barcode removal.

\textbf{Supplementary Table 4} Point mutation breakdown of different
filtering steps.

\textbf{Supplementary Table 5} Indel breakdown of different filtering
steps.

\textbf{Supplementary Table 6} Selected iSNP information for each
target.

\textbf{Supplementary Table 7} DNM target region breakdown of data error
and quality.

\textbf{Supplementary Table 8} Data overview. Summary tables of
supplementary data.

\textbf{Supplementary Table 9} Dataset from Smits et al., 2022 used to
calculate the percentage of DNMs that have an iSNP within 5kb ranges.

\textbf{Supplementary Table 10} Failed target sequenced samples.

\textbf{Supplementary Table 11} Data analysis for determining the
post-zygotic DNMs.

\textbf{Supplementary Table 12} Phasing and parent-of-origin
determination in WES and ONT datasets.

\textbf{Supplementary Table 13} Mutant base and allele frequencies with
associated pre/post zygotic classifications.

\textbf{Supplementary Table 14} DNMs in this dataset with previous
causative classifications for male infertility (Oud et al., 2022).

\emph{Supplementary Figures}

\textbf{Supplementary Figure 1 Basic overview of the long-read targeted
phasing approach} . Red boxes highlighting failed sample outcomes.

\textbf{Supplementary Figure 2 Long range PCR performance and
optimisation.} \textbf{(a)} change standard to rapid. Percentage
comparison of first-time primer success using our standardised approach
and primer success after optimisations at increasing target sizes
(kb).\textbf{(b)} Table of optimisation categories, displaying total
target amplification success rates at each stage.

\textbf{Supplementary Figure 3 a)} Log10 scatter plot of target
amplification sizes in relation to the percentage of false allele
coverage of each target and the percentage of false iSNP/DNM base
coverage of each target. The trend of the data is highlighted by the
logarithmic line of best fit. \textbf{b)} Log10 scatter plot of target
amplification sizes in relation to the percentage of bases with quality
score \textless{}5 (see method section `Bioinformatics'). The trend of
the data is highlighted by the logarithmic line of best fit.

\textbf{Supplementary Figure 4} Violin plot comparing the WES DNM
nucleotide base frequencies, ONT DNM nucleotide base frequencies, ONT
DNM allele frequencies, ONT DNM prezygotic and postzygotic allele
frequencies.

\textbf{Supplementary Figure 5} Pre and post zygotic illustrative
breakdown of the 77 DNMs that could be phased, including the parental
origin of the DNMs.

\textbf{Supplementary Figure 6} Stacked percentage plot looking at
parent-of-origin in relation to likelihood of causality.

\textbf{Supplementary Figure 7 IGV illustration of a DNM showing a
comparison of WES and ONT data.} Using long-read targeted sequencing,
the \textasciitilde{}1700 bp region that these variants span was
sequenced and visualized in IGV. Approximately 15000 reads are phased in
the ONT data, and the quality and coverage differences between those
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