Mutational and splicing landscape in a cohort of 43,000
patients tested for hereditary cancer
著者

Carolyn Horton, Ashley Cass, Blair R. Conner, Lily Hoang,
Heather Zimmermann, Nelly Abualkheir, David Burks,
Dajun Qian, Bhuvan Molparia1, Huy Vuong, Holly LaDuca,
Jessica Grzybowski, Kate Durda, Robert Pilarski, Jessica
Profato, Katherine Clayback2, Martin Mahoney, Courtney
Schroeder,

Wilfredo

Torres-Martinez,

Aaron

Elliott,

Elizabeth C. Chao and Rachid Karamjournal or publication title

npj Genomic Medicine

volume7

Article number

49

year

25 August 2022

出版社

Springer Nature, published in partnership with CEGMR,King Abdulaziz University

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URL

https://research.konicaminolta.com/jp/report/
doi: 10.1038/s41525-022-00323-y

www.nature.com/npjgenmed

BRIEF COMMUNICATION

OPEN

Mutational and splicing landscape in a cohort of 43,000
patients tested for hereditary cancer
Carolyn Horton1, Ashley Cass1, Blair R. Conner1, Lily Hoang 1, Heather Zimmermann1, Nelly Abualkheir1, David Burks1, Dajun Qian1,
Bhuvan Molparia1, Huy Vuong1, Holly LaDuca1, Jessica Grzybowski 1, Kate Durda1, Robert Pilarski1, Jessica Profato1,
Katherine Clayback2, Martin Mahoney 2, Courtney Schroeder 3, Wilfredo Torres-Martinez3, Aaron Elliott4, Elizabeth C. Chao1,5 andRachid Karam 1 ✉DNA germline genetic testing can identify individuals with cancer susceptibility. However, DNA sequencing alone is limited in its
detection and classiﬁcation of mRNA splicing variants, particularly those located far from coding sequences. Here we address thelimitations of splicing variant identiﬁcation and interpretation by pairing DNA and RNA sequencing and describe the mutationaland splicing landscape in a clinical cohort of 43,524 individuals undergoing genetic testing for hereditary cancer predisposition.

1234567890():,;

npj Genomic Medicine (2022)7:49 ; https://doi.org/10.1038/s41525-022-00323-y

Nearly all clinically available multigene panel tests (MGPT) focus
on the analysis of protein-coding exons of DNA with little
coverage in the introns1,2. Clinically signiﬁcant variants within
the intron predominantly impact splicing; therefore, a splicing
proﬁle by RNA sequencing (RNA-seq) can improve the detectionof pathogenic variants while also providing functional evidence
for accurate interpretation of putative splicing variants3–6. However, published evidence on the utility of RNA-seq has been
limited by studies with small sample size, highly selected cohorts,
and those conducting RNA-seq in follow-up to uninformative DNA
results6–10.
In this study, 43,599 tests from 43,524 consecutive individuals
who underwent paired DNA-RNA genetic testing from March 2019
through April 2020 were eligible for analysis. A total of 15,288
reported variants, including Pathogenic (P) (n = 4565), Likely
Pathogenic (LP) (n = 565), and Variant of Unknown Signiﬁcance(VUS) (n = 10,158), were reported in 18 RNA-covered genes(Supplemental Table 1) amongst 12,859 cases. We found thatwhile missense variants were most observed (69.2% of variants),
splicing variants were not rare (6.2%), occurring more than twice
as often as copy number variations such as gross deletions/
duplications (2.8%) (Fig. 1a). The classiﬁcation of unique splicing
variants (n = 555 out of 7136 unique total variants) was
determined based on their position with respect to the exon
(Fig. 1b). A minority of exonic variants were classiﬁed as P/LP
(22.7%) and most were classiﬁed as VUS (77.3%); however, exonic
variants located at the last nucleotide of the exon were more often
P/LP (62.7%) due to their impact on splicing. With regards tointronic variants, the vast majority of variants at the wellconserved canonical acceptor and donor splice-site (positions−1, −2, and +1, +2 respectively) were classiﬁed as P/LP (95.6%).
Importantly, 68 P/LP variants located beyond the consensus
splice-site (more than 5 nucleotides from the exon) wereidentiﬁed.
There were 263 unique splicing events associated with a DNA
variant identiﬁed across the 18 genes studied. As some transcripts
can be associated with several DNA variants, these corresponded
to 516 total combinations of unique DNA variants with associatedsplicing events across individuals. The association between asplice event and a given variant was established after consideration of the percent splice index (PSI, or the relative percentage ofalternative/abnormal transcript)11, the speciﬁcity of the splice
event (i.e., absence of the event at similar PSI in healthy controls),
the reproducibility of the splice impact in additional carriers and/or by other functional assays, and the potential mechanism
underlying the variant’s impact on splicing (e.g., creation of anovel splice site, weakening of a native splice site, deletion of abranch point, or disruption of a potential exonic splicing
enhancer). Analysis of the variant distribution indicates that
deleterious splicing DNA variants result in elevated PSI levels ofabnormal transcripts compared to controls regardless of thenucleotide position (Fig. 1c, Supplementary Fig. 1). Most splicing
events associated with DNA variants resulted in partial or full exonskipping, observed in 205 individuals (Fig. 1d, e). However, intron
inclusion events were also associated with 45 variants leading topartial intron inclusion and 11 variants resulting in the inclusion of
intronic sequences into cryptic exons. Notably, variants leading toinclusion of cryptic exons are located outside of our standard
clinical reporting range for DNA MGPT and would not have been
detected without RNA-seq. We found that RNA-seq allowed forthe characterization of alternative splicing events and improved
our understanding of the various mechanisms of pathogenicity
associated with aberrant splicing.
Evidence obtained from RNA-seq impacted variant classiﬁcationin 549 individuals with potential splicing variants. The RNA impact
on classiﬁcation of potential splicing variants based on nucleotide
position is shown in Fig. 2a, b (intronic variants classiﬁed as B/LB
prior to this study that had no aberrant RNA were excluded).
Splicing variants were deﬁned as those with a deleterious impact
by in silico modeling, any intronic variant within ﬁve nucleotidesof the exon, and variants with abnormal RNA transcript detected.
RNA impacted variants include those in which RNA evidence led
to a reclassiﬁcation but exclude those in which RNA evidence wasconcordant with an existing interpretation but did not prompt
reclassiﬁcation. RNA was especially informative for upgrades of
DNA variants at intronic positions +3 or +5 (n = 33), intronic

1
Ambry Genetics. One Enterprise, Aliso Viejo, CA 92656, USA. 2Roswell Park Comprehensive Cancer Center, 665 Elm St, Buffalo, NY 14203, USA. 3Indiana University School of
Medicine, 975 W. Walnut Street, IB 130, Indianapolis, IN 46202, USA. 4Realm IDx. One Enterprise, Aliso Viejo, CA 92656, USA. 5University of California, Irvine, School of
Medicine, 1001 Health Sciences Rd, Irvine, CA 92617, USA. ✉email: rkaram@ambrygen.com

Published in partnership with CEGMR, King Abdulaziz University

C. Horton et al.

2
a.

b.
Splicing
6.2%

Likely Benign

Frameshi/Stop
16.4%
Other
5.4%
Missense
69.2%

Likely Pathogenic

Pathogenic

80
70
60
50
40
30
20
10
0

<-30
-11 to -30
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
ﬁrst nt
exoniclast nt
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11 to +30> +30

Number of Unique Variants

Gross del/dup
2.8%

VUS

Intronic posionIntronic posion

Exon

-9

-8

-7

-6

-5

-4

-3

-2

-1

Intronic posion

Last nt-10

Exonic<-10First nt

Percent Spliced Index

+1

+2

+3

d.

+4

+5

+6

+7

+8

+9

+10

>+10

Intronic posion

Exon

e.

Intron Inclusion (cryptic exon)

Exon 1

Exon22
Exon

Exon 3

Exon 1

Exon 2Cryptic
Exon

Exon 3Intron Inclusion(Cryptic Exon)

Exon 3

Exon 1

Exon 2Intronic

Exon 3Intron Inclusion(Partial)

Exon 3

Exon 4

Exon 1Exon
2Exon 3

Exon 4

Exon 1

Exon 3

Exon 3

Exon 4

Exon 1

Cryptic Exon

Intron Inclusion (partial)

Exon 1

Exon 2Intron 2

Exon Skipping (partial)

Exon 1

Exon 2

Exon 3

Exon 4

Splice Event

1234567890():,;

c.

Exon Skipping(Partial)

Exon Skipping (full)Exon 1

Exon
Exon22

Exon 4

Exon Skipping(Full)

Exon Skipping (full and partial)

Exon 1

Exon 22
Exon

Exon
3

Exon 4

Exon Skipping(Full & Partial)
0

50

Count

100

Fig. 1 Splicing landscape of ~43,000 patients receiving paired DNA-RNA genetic testing. a Distribution of variant type for variants withclassiﬁcation of VUS, LP, or P (N = 15,288 reported DNA variants); b Splicing variant classiﬁcation by nucleotide position (N = 555 uniquesplicing variants among 7136 unique total DNA variants); c Violin plots demonstrating the percent spliced index of splicing events associatedwith variants (P/LP/VUS) in patients (N = 516 DNA/RNA associated variants), with comparison to their median in healthy donor controls shownin Supplemental Fig. 1. Within each violin, the white dot represents the median, the boundaries of the thin black rectangle represent theinterquartile range, and the black line extending from the rectangle represents 1.5× interquartile range; d Schematic of the splicing event
types considered (gold, inclusion of intronic sequence; blue, exclusion of exonic sequence); e Distribution of splicing events associated with
reportable variants by splicing event type (N = 263 unique transcripts). Variant of Unknown signiﬁcance (VUS), Likely Pathogenic (LP),Pathogenic (P), nucleotide (nt), Percent Spliced Index (PSI), Deletion/Duplication (del/dup).
npj Genomic Medicine (2022) 49

Published in partnership with CEGMR, King Abdulaziz University

C. Horton et al.

3
b.

a.

70

Exon

25
20
15
10
5
0

60
50
40
30
20
10
0

< -30
-11 to -30
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
ﬁrst nt
last nt
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11 to +20> +10

Number of Unique Variants

30

<-30
-11 to -30
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
ﬁrst nt
last nt
+1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11 to +30> +30

Number of Unique Variants

35

Intronic posionIntronic posion

Exon

Intronic posionIntronic posion

Exon

d.

c.
A.60s
Breast ca dx 60s

A.60s>50 polyps

d.80s

A.30s
A.30s
A.30s
Colorectal ca dx 33y>20 adenomatous
10-20 adenomatous
polypspolyps

A.5y

A.6y

d.80s

A.60s

A.80s

A.90s

d.50s
Pancreatic ca dx 49y

A.30s

RNA-seq

A.60s

A.60s

A.40s

RNA-seq

Exon 4

Exon 5

Exon 4

Exon 6

Exon 5

r.422_423ins423-4002_423-3964

r.496_497ins497-2656_497-2611

f.

e.

d.80s

d.80s

A.80s

d.70s

d.60s
Breast ca dx 40s

Exon 5A.70s

d.60s
Ovarian ca dx 60s

A.60s

A.60s

A.60s

A.60s

A.30s

A.40s

A.60s
Breast ca dx 50s
A.30s
A.30s
Lobular breast ca dx 38y

A.30s

RNA-seqRNA-seq

Exon 18

Exon 19

r.8331_8332ins8331+3422_8332-3386

variants beyond 10 nucleotides from the exon (n = 24), andvariants at the last nucleotide of the exon (n = 36). Intronic
variants not impacted by RNA evidence (Fig. 2b) were more likely
to already be classiﬁed as P/LP before RNA-seq was performed.
Alterations classiﬁed as VUS >10 nucleotides from the exon had an
Published in partnership with CEGMR, King Abdulaziz University

Exon 11

Exon 10

r.1565_1566ins1565+609_669+1566-1_1566-24

associated RNA variant, but RNA and/or other clinical evidence
was not sufﬁcient for reclassiﬁcation to P/LP. In addition to splicing
variants, interpretation of gross duplications is also aided by RNAseq by determining if a duplication occurs in tandem with theoriginal sequence, as detailed previously12 and reinforced within
npj Genomic Medicine (2022) 49

C. Horton et al.

4

Fig. 2 Identiﬁcation of clinically signiﬁcant splicing variants. a Classiﬁcation by nucleotide position of splicing variants whose classiﬁcation
was impacted by RNA (classiﬁcation as assessed without RNA evidence differs from classiﬁcation with RNA evidence, N = 549 variants);
b Classiﬁcation by nucleotide position of splicing variants with no RNA impact (classiﬁcation did not depend on RNA evidence, N = 523
variants); c–f Representative deep-intronic variants. Pedigrees in panels c–f describe personal and family cancer/polyp history. Sashimi plots inpanels c–f include RT-PCRseq and capture RNA-seq data and represent relevant alignments and exon junction-spanning reads as arcs withread counts displayed. Alternative splicing events were absent in healthy controls. c Case description of an individual with likely pathogenic(LP) variant APC c.423-3958C > T. Familial testing identiﬁed the variant in the proband’s sister and father. Sashimi plots depict the inclusion of acryptic exon caused by this variant. d Case description of an individual with pathogenic variant ATM c.497-2661A > G. Familial testing has notbeen performed. Sashimi plots depict the inclusion of a cryptic exon caused by this variant. e Case description of an individuals with LPvariant BRCA2 c.8332-3384A > T. Familial testing has not been performed. Sashimi plots depict the inclusion of a cryptic exon caused by thisvariant. f Case description of an individual with LP variant CDH1 c.1565 + 672_1566-23del2827. Familial testing identiﬁed the variant in all
siblings. Sashimi plots depict intron retention events caused by the deletion.

this dataset via four gross duplications that were reclassiﬁed from
VUS to P/LP. Incorporation of RNA-seq led to an absolute increasein the positive yield of 0.2% (n = 87 individuals) and absolutedecrease in VUS rate of 0.7% (n = 305 individuals).
We utilized RNA-seq to identify novel deep intronic P/LP
variants without the need for DNA sequencing of the entire intron.
DNA variants were only reported in this region when there was anabnormal RNA detected. This allows for increased detection ofclinically relevant variants without the substantial VUS burden that
results from DNA-only sequencing of intronic regions. We foundthat 12.3% (n = 45) of individuals with intronic P/LP variants hadvariants located >10 nucleotides from the exon (frequency of one
per 968 patients tested), and 7.7% (n = 28) had variants located>20 nucleotides from the exon (one per 1554 patients tested). The
clinical history and RNA-seq data for selected individuals withdeep intronic variants are shown in Fig. 2c–f. These variants were
all predicted by SpliceAI13 to strengthen relatively weak cryptic
donor or acceptor splice sites within the intron. The APC c.4233958C > T LP variant (Fig. 2c) converts C to T in the +6 position ofa cryptic splice donor site, which strengthened the cryptic site andled to the inclusion of a cryptic exon. The ATM c.497-2661A > G
pathogenic variant (Fig. 2d) resulted in a cryptic exon inclusion
through the substitution of A to G in the −1 position of a newly
created acceptor site. Similarly, the BRCA2 c.8332-3384A > T LP
variant (Fig. 2e) led to the inclusion of a cryptic exon due to a
substitution of A to T in the +2 position of a newly created splicedonor site. The CDH1 intronic deletion (Fig. 2f) likely removed thebranch point of the native acceptor, which is predicted to render
the native acceptor site unrecognizable to the spliceosome. Thisdeletion also decreased the distance between two preexisting
deep intronic cryptic acceptor sites and the 5′ splice donor site.
These cryptic acceptor sites were then utilized, causing two
different partial intron retention events. Conﬁrmatory analysis via
Sanger sequencing or breakpoint analysis was performed for deep
intronic variants (Supplemental Fig. 3a–e). These cases, as well asothers with deep intronic variants, represent potential false
negative results, as the alterations would not have been detectedwith DNA MGPT sequencing alone.
Here we demonstrate in a large clinical diagnostic cohort of
43,524 individuals that paired DNA- and RNA-seq detects
pathogenic variants that impact splicing, including deep-intronic
alterations, resulting in the identiﬁcation of additional 87
individuals with a clinically actionable result. RNA-seq also
provided additional evidence for more accurate interpretation of
splicing variants, resolving VUS identiﬁed in 305 individuals in thiscohort alone. Note that results from this study may haveunderestimated its clinical impact, which can extend well beyond
the probands described here, since some of these patients’families are now eligible for cascade testing. In addition, because
reclassiﬁed variants may have been detected in previous cases or
may be seen in future unrelated individuals, these can have adownstream impact which was also not quantiﬁed in this study. In
summary, our results highlight the importance of RNA-seq to
npj Genomic Medicine (2022) 49

improve identiﬁcation of high-risk individuals that would havebeen missed by DNA-only diagnostic approaches.
METHODS
The authors conﬁrm that we have complied with all ethical regulations andthat this study was carried out in accordance with the recommendations of
the Western Institutional Review Board (Puyallup, Washington), which has
granted an IRB waiver stating “research does not include human subjects”
based on federal regulation 45 CFR 46.102(f) and associated guidance
entitle, “Guidance on Research Involving Coded Private Information or
Biological Specimens”, determining coded private information or biological
specimens would not be considered to involve human subjects. All
patients described were evaluated by genetic counselors and provided
informed consent for testing. Individuals who declined participation in deidentiﬁed research were excluded from this study.
Paired DNA and RNA sequencing workﬂow is depicted in Supplemental
Fig. 2. Genomic DNA was isolated from patient’s whole blood or saliva
using Qiasymphony (Qiagen). Isolated DNA quantity and quality wasassessed using absorbance at 260 nm. A total of 1 μg of genomic DNA wasused as input into library prep for dual index sequencing on illumina
platforms using a commercially available kit (KAPA Biosystems, Roche).
Brieﬂy, DNA was sheared enzymatically, end-repaired, and ligated tostandard illumina dual index adapters (IDT). After subsequent library
ampliﬁcation (10 cycles), libraries were pooled together at equal
concentrations and target enriched using hybrid capture. Customdesigned biotinylated probes (IDT X-Gen Lockdown) covering the coding
regions of tested cancer predisposition genes were hybridized over nightto capture libraries and captured with streptavidin beads (LifeTechnologies). Captured libraries were subsequently ampliﬁed and prepared forsequencing on NextSeq 500 or NovaSeq using the SP ﬂow cell (illumina).
Initial data processing and base calling is performed using the NextSeq
Control Software (NCS) and RRTA 2.4.11 (Real Time Analysis NCS v2.0.2.1).
Sequences are aligned to hg19 reference genome and variants are calledusing the third-party software Genome Analysis Toolkit (GATK) followed byannotation via an internally developed pipeline. While DNA sequencing isroutinely performed through a minimum of 30 nucleotides of each intron,
masking is applied to variants generated by the bioinformatics pipeline
based on the genes included in the test ordered and analytical reporting
range (>5 nucleotides beyond each coding exon). Variants with a Q score≤30 and an allele fraction <10% are ﬁltered out. Regions with <20×coverage on NGS are followed up with Sanger analysis. Variants in regions
complicated by pseudogene interference, variant calls not satisfying depthof coverage and variant allele frequency quality thresholds, and potentiallyhomozygous variants are veriﬁed by Sanger sequencing. Single nucleotide
variants and small insertions/deletions (≤3 nucleotides) that have an allele
frequency of >35% and 100× coverage are not veriﬁed by Sanger
sequencing. Large deletions and duplications are detected using a
combination of a read-depth based machine learning method and splitread method, and/or targeted microarray or MLPA as needed.
Total RNA was isolated from an additional patient specimen (blood, PAXtube) using standardized methodology and quantiﬁed as described
previously4. Brieﬂy, total RNA is fragmented and undergoes ﬁrst strand
synthesis using random hexamers, with subsequent ribosomal RNA depletion(Kapa Biosystems, Roche). After second strand synthesis and ampliﬁcation oflibraries ligated with standard illumina dual index adapters. Sequenceenrichment of the targeted coding exons and adjacent intronic nucleotides iscarried out by a hybrid-capture methodology using long biotinylated
oligonucleotide probes followed by a subsequent library ampliﬁcation and
Next-Generation sequencing (NextSeq 500 or Novaseq SP ﬂowcell, illumina).
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C. Horton et al.

5
RNA calls detected on RNA-seq were conﬁrmed via RT-PCRseq if thetotal coverage for an associated splicing event (PSI denominator) is <500X.
In addition, RNA calls with conﬂicting data, inconsistency in PSI among
carriers, or any other quality concerns were also conﬁrmed by RT-PCRseq.
For RT-PCRseq, total RNA is converted to complementary DNA (cDNA) byreverse transcriptase polymerase chain reaction (RT-PCR) using a one-step
approach with custom-designed primers for the target region (Superscript
IV one-step RT-PCR kit, Thermo). Primer sequences are available uponrequest. RT-PCR amplicons are then library prepped for standard illumina
paired end sequencing on MiSeq (illumina) using a commercially available
kit (Kapa Hyper Plus Kit, Roche).
RNA samples passed sequencing quality control if the percentage of
Q30 bases >75%, mean base quality >30, percentage of perfect index>85%. Reads from samples passing QC were aligned using STAR 2.0 (CITE).
An additional QC threshold was applied where ≥85% of exons from the 18
genes have average coverage ≥50×. DNA variants were evaluated for
association with abnormal splicing events. Percent Spliced Index (PSI) andits comparison with a control pool of 345 healthy donors were calculated
as previously described1,4. Relative to the canonical RefSeq transcript
isoform annotation, the PSI value was deﬁned as the number of reads
supporting the alternative splicing event divided by the number of all
reads in the region covering splicing event. Bar plots and box plots weregenerated using the ggplot2 package (v3.1.1) from R v3.6.1 with default
settings. Violin plots were generated using Seaborn (v.0.11.0) within
Python v.3.8.3. 5 types of splicing events were considered: Exon Skipping
Full (ESF; skipping of at least one full exon), Exon Skipping Partial (ESP; i.e.,an alternative 5′ or 3′ splice site that results in exclusion of part of an exon),
Exon Skipping Full and Partial (ES; a combination of at least one ESF and
ESP), Intron Inclusion Partial (IP; i.e., an alternative 5′ or 3′ splice site that
results in inclusion of intronic sequence ﬂanking the exon), and Intron
inclusion Cryptic (IC; i.e., a cryptic exon). Technical limitations in our assay
may have prevented the detection of full intron retention (IR) events.
Association of a splicing event with a DNA variant was evaluated manually
by a team of variant assessment scientists. The 18 genes included in RNA
sequencing analysis pipeline are: APC, ATM, BRIP1, BRCA1, BRCA2, CDH1,
CHEK2, MLH1, MSH2, MSH6, MUTYH, PALB2, PMS2, PTEN, NF1, RAD51C,
RAD51D, TP53. Reference isoforms are listed in supplemental table 1.
Additional considerations: (i) with regards to blood versus tissue samples,
the assay has been previously calibrated to identify abnormal splicing inblood by contextualizing putative pathogenic splicing events withpreviously identiﬁed P/LP known to affect splicing4,5, additionally thestrength of the evidence applied to RNA data for DNA variant curations
varies based on blood/tissue expression and alternative splicing identiﬁed
in controls; (ii) the assay has been previously calibrated to identify NMDtargeted transcripts in blood by contextualizing the PSI of putative
pathogenic splicing events versus the PSI of transcripts identiﬁed inindividuals heterozygous for P/LP variants known to affect splicing and tobe targeted by nonsense mediated mRNA decay (NMD)4,5. Analyzes ofallele skewing using SNPs within exons to indirectly assess NMD is also
used depending on SNP availability. Sashimi plots depicted in ﬁgures only
include reads in the PSI calculation for each abnormal splicing event
described.

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ACKNOWLEDGEMENTS
We thank the patients and clinicians who made this work possible, especially Katelyn
Breen, MS, Katherine Clayback, MS, Martin Mahoney, MD, Alexa Prose, MS, Esther
Rhei, MD, Courtney Schroeder, MS, and Wilfredo Torres-Martinez, MD. We also would
like to thank Eric Frank and Fernando Alonso for their IT support. No grant funding
was utilized in this study.

AUTHOR CONTRIBUTIONS
Conceptualization: R.K., C.H., E.C., H.L., J.P., and A.E.; Methodology: B.C., H.Z., N.A., andR.K.; Validation: B.C., C.H., L.H., H.Z., J.G., and K.D.; Formal analysis: A.C., B.M., C.H., D.B.,
D.Q., and H.V.; Investigation: B.C., C.H., H.Z., J.G., K.D., and N.A.; Resources: A.E., C.S.,
K.C., M.M., R.K., and W.T.; Data curation: C.H., L.H., H.Z., J.G., and K.D.; Writing—Original: C.H. and R.K.; Writing—Review and edit: A.C., E.C., B.C., H.L., H.Z., J.G., K.D.,
N.A., R.K., and R.P.; Visualization: A.C., B.C., B.M., C.H., and R.K.; Supervision; E.C., H.Z.,
and R.K.; Project administration: C.H. and R.K.

Reporting summary
Further information on research design is available in the Nature Research
Reporting Summary linked to this article.

DATA AVAILABILITY
The DNA and RNA sequencing sequence data that support the ﬁndings of this study
have been deposited in NCBI Sequence Read Archive (SRA) with the submissionaccession number PRJNA863325. The remaining data that support the ﬁndings of thisstudy are available on request from the corresponding author with reasonable
patient privacy restrictions.

CODE AVAILABILITY
All analyses conducted are outlined in the Methods section, including software
version(s) used. Example code is available upon request.

Received: 6 January 2022; Accepted: 10 August 2022;

Published in partnership with CEGMR, King Abdulaziz University

COMPETING INTERESTS
Paid employee at Ambry Genetics at the time this study was conducted: A.C., B.C.,
B.M., C.H., E.C., D.B., D.Q., H.L., H.V., H.Z., J.G., J.P., K.D., L.H., N.A., R.K., and R.P. Paid
employee at REALM IDx at the time this study was conducted: A.E. No competinginterests to declare: C.S., K.C., M.M., and W.T.

ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41525-022-00323-y.
Correspondence and requests for materials should be addressed to Rachid Karam.
Reprints and permission information is available at http://www.nature.com/
reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claimsin published maps and institutional afﬁliations.

npj Genomic Medicine (2022) 49

C. Horton et al.

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npj Genomic Medicine (2022) 49

Published in partnership with CEGMR, King Abdulaziz University