Introduction

Orchid offers whole-genome preimplantation embryo screening (PGT-WGS).  PGT-WGS enables screening for over 1200 monogenic conditions associated with birth defects and neurodevelopmental disorders, as well as hereditary cancer and ACMG secondary findings.

This raises two important questions for patients and the clinicians ordering PGT-WGS on their behalf: 

1. How often will PGT-WGS screening for an embryo return a monogenic finding?
2. How much does PGT-WGS monogenic screening reduce the risk of genetic disease? 

The purpose of this document is to explore and estimate the answers to these questions based on a review of the available literature regarding Orchid’s screening panels.  

Scope of analysis

These calculations are estimates which attempt to compile the state of current knowledge about the genetic causes of several genetic disorders and compare them to the genes and variants screened by Orchid.  This is an analytical review of existing work, not an empirical study of the historical detection rate via PGT-WGS.  Empirical results regarding Orchid’s historical detection rate will be published as a larger number of early-childhood outcomes become available.

These estimates are intended to in aggregate (1) roughly quantify for physicians and patients the fraction of embryos they will expect to report monogenic conditions.  (2) provide estimates of the broad disease reduction potential possible via PGT-WGS, when weighing testing choices. These summary numbers are not intended to be used as a firm estimate of the reduction in risk for a particular condition.

This article only attempts to estimate the PGT-WGS detection rate for single-gene disorders, microdeletions and microduplications, and does not include the rate of detection for other genetic conditions or screening: 

• This article does not include the detection of developmental conditions caused by partial or full aneuploidy which would already have been detected via standard Preimplantation Genetic Testing for Aneuploidy (PGT-A), for example, Trisomy 21.

• The computed detection rate and diagnostic yield of PGT-WGS do not include cases when known variants were targeted using preimplantation genetic testing for monogenic conditions (PGT-M).  For questions regarding PGT-M screening for a known variant, please contact genetics@orchidhealth.com.

• The detection rate only includes monogenic conditions, and does not analyze polygenic disorders (for example, Alzheimer's disease).

WGS diagnostic yield

Whole Genome Sequencing (WGS) is a powerful molecular technique that involves decoding over 99% of the DNA sequence of an individual, providing a comprehensive understanding of their genetic makeup. Typically, clinical WGS is employed to identify a genetic diagnosis for diseases in unhealthy adults. Historically, WGS is ordered when a patient presents with unexplained or complex medical conditions that conventional tests have failed to diagnose accurately.

The diagnostic yield of WGS is heavily contingent upon factors such as the patient's family history, clinical presentation including affected organs, severity of the condition, and if there is a discernible pattern that might signify a particular genetic syndrome.

Challenges when estimating diagnostic yield

The diagnostic yield of WGS even in adult populations is difficult for two reasons:

Unhealthy vs healthy populations

The majority of medical genetics research is based on unhealthy populations; the diagnostic yield in an unhealthy population will differ from the diagnostic yield in a healthy population. Multiplying diagnostic yield by the prevalence of conditions is not straightforward; estimating prevalence itself can introduce selection biases. When comparing diagnostic yield between datasets with healthy and unhealthy individuals, it's important to note that a significant portion of research focuses on populations with existing health conditions. Consequently, data on unhealthy populations inherently skews diagnostic yield higher and it cannot be directly contrasted with studies involving healthy individuals.

Inconsistency between studies

Within unhealthy populations, diagnostic yield differs from study to study. For example: on average, it is estimated that around 30% of neurodevelopmental disorders can be attributed to single gene (monogenic) disorders or small and extra missing pieces of chromosomes (copy number variants)^1–3. However, individual diagnostic yield studies report varying percentages of attributed genetic causes based on their study populations and the limitations of the genetic testing conducted. As an example, Vissers et al in 2016⁴ reported a diagnostic yield of 60% for intellectual disability whereas Stefanski et al. in 2021 ³ reported 28%.

Together, current whole-genome publications predominantly center on individuals with health issues, and even within these investigations, there exists a lack of consistency. This review aims to estimate the detection rate of monogenic findings within the healthy population (PGT-WGS patients not requesting targeted monogenic screening) by leveraging the knowledge acquired from studies involving unhealthy populations, while also considering the more recent data emerging from research involving healthy individuals. This approach allows us to make an informed and educated estimate of the detection rate.

Outcomes from healthy population screening 

Whole genome sequencing only became clinically available around 2014⁵ and was targeted for use to provide a diagnosis for individuals with health issues. As the cost of testing has dropped and general population interest has spiked, whole genome data is increasingly available from healthy individuals. Supported by NIH funding, Genomes2People⁶ is an example of a research team managing several NIH studies involving genetic testing in healthy populations including BabySeq⁷ and MedSeq.⁸

BabySeq

The BabySeq project is a randomized clinical trial designed to test the utility of routine genomic screening for newborns. Based on their most updated findings, 18 out of 159 infants (11.3%) who underwent sequencing were found to have unanticipated monogenic disease risk associated with childhood-onset and actionable adult-onset disease risk. These findings were not suspected based on the infant's clinical presentation or family history although the majority of the variants were inherited. The monogenic findings discovered included those related to metabolic conditions like biotinidase deficiency, cardiac diseases like dilated cardiomyopathy, and cancer predispositions like hereditary breast and ovarian cancer (e.g. BRCA2). All monogenic findings were clinically actionable, prompting referrals and medical management for the infants. Orchid’s embryo screening covers 89% of the single gene findings identified in this study. 

Other screening

• DiscovEHR: The examination of over 50,000 exomes from the DiscovEHR study aimed to assess the prevalence of clinically actionable targeted genetic variants, revealing that 3.5% of healthy individuals carried such variants.⁹
• Vassy et al., 2017 presented a pilot randomized trial investigating the effects of whole genome sequencing on primary care and health outcomes among healthy adult patients. Of the 50 healthy individuals who were sequenced, 18% had a pathogenic or likely pathogenic variant associated with monogenic disease.¹⁰ 

While the fraction of the healthy adult population with variants linked to monogenic disease varies significantly across these studies (3.5% - 18%), this provides guidance as to the fraction of embryos expected to return monogenic findings via PGT-WGS.

Apparently Healthy embryo screening

A notable percentage of embryos during an IVF cycle will have chromosomal differences identifiable via preimplantation aneuploidy testing (PGT-A). Routine embryo screening typically concludes with chromosomal analysis, except for a minority of families who opt for additional screening focused on a specific gene disorder, guided by their personal or family medical history.

Orchid’s novel technology is currently the only offering capable of screening embryos for hundreds of monogenic conditions associated with childhood-onset and actionable adult-onset disease risk, similar to those screened for in the BabySeq project.

Orchid’s PGT-WGS

Orchid's PGT-WGS uses uses whole genome technology with targeted reporting. Targeted reporting means that Orchid does not report on all 20,000+ genes in the human genome. Rather, Orchid screens a curated list of over 1200 genes known to have strong disease associations with high penetrance (individuals carrying mutations in these genes are highly likely to exhibit symptoms related to the associated condition). Orchid also only reports pathogenic or likely pathogenic variants using ACMG classification within genes specified in these gene panels.

 The genes screened are organized into four panels:

• The American College of Medical Genetics and Genomics (ACMG) secondary findings (v3.1):
  ACMG recommends reporting pathogenic and likely pathogenic variants in 78 genes that are associated with known, potentially serious conditions.
• Genes known to cause monogenic neurodevelopmental disorders
• Genes known to cause monogenic birth defects
• Genes known to be associated with hereditary cancer

Adopting a restricted panel (targeted reporting) is not new; the Genomes2People’s PeopleSeq Consortium published a paper describing a comparable methodology. In this paper, a comprehensive list of 6,145 genes known to be associated with diseases was compiled, drawn from sources like the Human Gene Mutation Database, OMIM, and ClinVar. Then, employing strict criteria and incorporating computationally gathered evidence, the list was refined to a more restricted set of 3,929 genes exhibiting stronger disease associations.¹¹ Similarly, the BabySeq project curated a list of 954 genes based on clinical validity including age of onset and penetrance.¹²

Orchid adopted a similar approach, curating genes that have definitive evidence of strong disease association as reported by academic geneticists at Clinical Genome¹³ and which are currently offered on commercial panels for diagnostic testing for neurodevelopmental disorders, birth defects and hereditary cancer. 

Estimating detection rates for Orchid’s PGT-WGS

To estimate the rate at which embryos screened via PGT-WGS may return monogenic findings for a condition, we estimate 4 factors:

1. The fraction of live births exhibiting the condition.
2. The fraction of those instances for which a specific genetic cause can be identified.
3. The fraction of those genes that are on Orchid’s screening panels.  For the purposes of this estimate, the relative contribution of individual genes to disease diagnoses is not weighted. 
4. The fraction of those mutations screened and detected by Orchid’s PGT-WGS screening.
   

ACMG Secondary Findings

Secondary findings are genetic variants within genes that are returned and reported during clinical exome or genome sequencing, beyond the primary findings related to the indication for testing. The genes are associated with various phenotypes related to cancer, cardiovascular disease, metabolic disorders, etc. where knowledge of a pathogenic/likely pathogenic variant could lead to medical management or interventions aimed at preventing morbidity/mortality. Several studies of healthy cohorts consistently demonstrate a prevalence in approximately 3% of the population of secondary findings in ACMG panels:

• 2.7% Customers of Blueprint Genetics who opted into whole exome screening received secondary findings.¹⁴
• In the eMERGE cohort, 3.02% of samples had secondary findings (approximately 80% of which are in genes on the ACMG panel). Of these, the most common findings were associated with cancer (1.38%), cardiovascular diseases (0.87%), and lipid disorders (0.50%).¹⁵
• 3.5% of individuals in the DiscovEHR study (n = 50,726), a healthy population, had deleterious variants in the 76 gene ACMG panel.⁹

Orchid’s PGT whole genome includes the 78 genes on ACMG secondary findings version 3.1 list.

Birth defect gene panel

Birth defects represent a wide range of developmental conditions.  Many, but not all, birth defects can be linked to a specific genetic cause.  This analysis focuses on several of the most common categories of birth defects; however, less common conditions not analyzed here may also be identified via PGT-WGS.

Congenital Heart Defects

The most common congenital anomaly is congenital heart defects. Approximately 1.2 to 3 per 1,000 live births have a CHD associated with a monogenic syndrome.¹⁶ Orchid’s panel covers approximately half of the genes included in the 2021 study.

Lethal Skeletal Dysplasia

One study of lethal skeletal dysplasias in South America finds the prevalence as approximately 1.6 per 10,000 births.¹⁷ Nearly all of these are caused by monogenic mutations, of which Orchid’s birth defect panel covers ~50%.

Congenital Anomalies of the Kidney and Urinary Tract (CAKUT)

CAKUT is present in approximately 1% of all live births, and known genetic causes (rare CNVs and point mutations) of CAKUT account for approximately 20% of cases.¹⁸ Orchid’s testing panel covers approximately half of these known genetic causes, for a total of approximately 1 in 500 embryos. 

Inborn Errors of Metabolism 

Orchid’s PGT-WGS covers known inborn errors of metabolism, which occur in approximately 30 per 100,000 live births in California.¹⁹  Orchid covers ~90% of the genes discussed in the cited study.

Hearing Loss

Roughly 1 in every 1,000 newborns is born with profound hearing loss, and in approximately half of these cases, an underlying genetic cause can be identified.²⁰  Orchid covers ~70% of the genes discussed in the 2006 overview of monogenic hearing loss.

Neurodevelopmental disorders gene panel

Orchid’s Neurodevelopmental Disorders Panel analyzes genes and copy number variants that are associated with syndromic and non-syndromic neurodevelopmental disorders, including but not limited to Autism Spectrum Disorders (ASD), intellectual disabilities, and epilepsy. To provide a comprehensive analysis of neurodevelopmental disorders, a curated list of over 200 relevant genes were compiled based on existing data and reviewed by a group of experts in the field of genetics (clinical domain group). The majority of genes included on this panel are syndromic causes of neurodevelopmental disorders, meaning that a neurodevelopmental disorder is one feature in a large underlying genetic syndrome. Orchid additionally analyzes 50+ regions where microdeletions/microduplications (copy number variants) are known to be associated with neurodevelopmental disorders.

Single gene disorders

A 2020 study by López-Rivera et al uses computational methods to estimate 0.33% of all births carry a de novo mutation in one of 101 genes associated with neurodevelopmental disorders.²¹ Orchid’s screen covers 79 of these 101 genes (however, Orchid’s neurodevelopmental panel covers 149 genes not included in this study).

Copy number variants (CNVs) 

In a study of 12,252 mother-father-child trios from the Norwegian Mother, Father, and Child Cohort Study,² about 1 in 200 children were born with a deletion or duplication in one of the genomic regions known to be associated with a neurodevelopmental disorder such as 1q21.1. These 13 deletions and duplications ranged from 0.22 to 5.81 megabases long, of which 48 out of 59 (81%) were over 0.4 megabases long. Orchid’s whole genome PGT can detect >99% of copy number variants of this size within targeted regions.

Hereditary cancer gene panel

Roughly 10% of cancers are estimated to have a genetic basis.²²  However, a large proportion of the genes screened on Orchid’s hereditary cancer gene panel (25%) overlap with genes on the ACMG secondary findings panel.  To avoid double-counting conditions associated with these genes, diagnostic yield for the hereditary cancer panel is not included here.

Screening limitations of PGT-WGS

Orchid’s whole-genome screening does not detect all variants on screened genes. Certain classes of variants are not screened, notably but not limited to large insertions and deletions and variants within homopolymer regions.  The ability to detect variants on genes with corresponding pseudogenes is also limited. Screened variants are also subject to an analytical sensitivity of approximately 98%. More details about coverage and limitations are available online.²³ For additional questions please contact genetics@orchidhealth.com.

Combined, we estimate a detection rate of 95% for pathogenic or likely pathogenic variants described in the above analysis.

Summary

Combining the results for the above panels, and factoring in screening limitations, suggests that PGT-WGS will return monogenic findings for roughly 3.7-3.9% of screened embryos (Table 1). This estimate falls within the 3.5-18% findings rate found by healthy adult WGS studies. Approximately 3% of these monogenic findings are expected to be inherited, while around 0.7-0.9% are likely to arise de novo (new) in an embryo.

These estimates are based on the literature available as of September 2023. As additional results are available and birth outcomes become available from children screened via PGT-WGS, this document will be updated with empirical results alongside the literature analysis presented here.

Table 1. Estimate of Orchid’s PGT-WGS detection rate for several monogenic disorders. 

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References

1. Miller, D. T. et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764 (2010).

2. Smajlagić, D. et al. Population prevalence and inheritance pattern of recurrent CNVs associated with neurodevelopmental disorders in 12,252 newborns and their parents. Eur. J. Hum. Genet. 29, 205–215 (2021).

3. Stefanski, A. et al. Clinical sequencing yield in epilepsy, autism spectrum disorder, and intellectual disability: A systematic review and meta-analysis. Epilepsia 62, 143–151 (2021).

4. Vissers, L. E. L. M., Gilissen, C. & Veltman, J. A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9–18 (2016).

5. van El, C. G. et al. Whole-genome sequencing in health care. Eur. J. Hum. Genet. 21, S1–S5 (2013).

6. Genomes to People – Research In Translational Genomics and Health Outcomes. https://www.genomes2people.org/.

7. The BabySeq Project – Genomes to People. https://www.genomes2people.org/research/babyseq/.

8. The MedSeq Project – Genomes to People. https://www.genomes2people.org/research/medseq/.

9. Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).

10. Vassy, J. L. et al. The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients: A Pilot Randomized Trial. Ann. Intern. Med. 167, 159–169 (2017).

11. Lazo de la Vega, L. et al. A framework for automated gene selection in genomic applications. Genet. Med. Off. J. Am. Coll. Med. Genet. 23, 1993–1997 (2021).

12. Ceyhan-Birsoy, O. et al. A curated gene list for reporting results of newborn genomic sequencing. Genet. Med. Off. J. Am. Coll. Med. Genet. 19, 809–818 (2017).

13. Rehm, H. L. et al. ClinGen — The Clinical Genome Resource. N. Engl. J. Med. 372, 2235–2242 (2015).

14. Brunfeldt, M., Kaare, M., Saarinen, I., Koskenvuo, J. & Kääriäinen, H. Opt-in for secondary findings as part of diagnostic whole-exome sequencing: Real-life experience from an international diagnostic laboratory. Mol. Genet. Genomic Med. 11, e2180 (2023).

15. eMERGE Clinical Annotation Working Group. Frequency of genomic secondary findings among 21,915 eMERGE network participants. Genet. Med. Off. J. Am. Coll. Med. Genet. 22, 1470–1477 (2020).

16. Falsaperla, R. et al. Monogenic Syndromes with Congenital Heart Diseases in Newborns (Diagnostic Clues for Neonatologists): A Critical Analysis with Systematic Literature Review. J. Pediatr. Genet. 10, 173–193 (2021).

17. Barbosa-Buck, C. O. et al. Clinical epidemiology of skeletal dysplasias in South America. Am. J. Med. Genet. A. 158A, 1038–1045 (2012).

18. Sanna-Cherchi, S., Westland, R., Ghiggeri, G. M. & Gharavi, A. G. Genetic basis of human congenital anomalies of the kidney and urinary tract. J. Clin. Invest. 128, 4–15 (2018).

19. Waters, D. et al. Global birth prevalence and mortality from inborn errors of metabolism: a systematic analysis of the evidence. J. Glob. Health 8, 021102 (2018).

20. Bayazit, Y. A. & Yilmaz, M. An overview of hereditary hearing loss. ORL J. Oto-Rhino-Laryngol. Its Relat. Spec. 68, 57–63 (2006).

21. López-Rivera, J. A. et al. A catalogue of new incidence estimates of monogenic neurodevelopmental disorders caused by de novo variants. Brain J. Neurol. 143, 1099–1105 (2020).

22. Tsaousis, G. N. et al. Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations. BMC Cancer 19, 535 (2019).

23. Orchid | Report Panels. https://www.orchidhealth.com/panels.

Appendix

Appendix A

Genes identified via a literature review as causal of the condition in question.  Genes marked in bold are not included on Orchid’s standard screening panels. The inclusion of specific genes was recorded as of the writing of this document, and may not reflect Orchid's current panel coverage. For specific questions about Orchid's current gene screening panels, contact genetics@orchidhealth.com.

Monogenic neurodevelopmental disorders

75/101 genes screened (74%)

ADNP, AHDC1, ALG13, ANKRD11, ARID1B, ASXL1, ASXL3, AUTS2, BCL11A, BRAF, CASK, CDK13, CDKL5, CHAMP1, CHD2, CHD4, CHD8, CNKSR2, CNOT3, COL4A3BP, CREBBP, CSNK2A1, CTCF, CTNNB1, DDX3X, DNM1, DYNC1H1, DYRK1A, EEF1A2, EHMT1, EP300, FOXG1, FOXP1, GABRB2, GABRB3, GATAD2B, GNAI1, GNAO1, GRIN2B, HDAC8, HNRNPU, IQSEC2, ITPR1, KANSL1, KAT6A, KAT6B, KCNH1, KCNQ2, KCNQ3, KDM5B, KIAA2022, KIF1A, KMT2A, KMT2D, MECP2, MED13L, MEF2C, MSL3, MYT1L, NAA10, NFIX, NSD1, PACS1, PDHA1, POGZ, PPM1D, PPP2R1A, PPP2R5D, PTCH1, PTEN, PTPN11, PUF60, PURA, RBFOX2, SATB2, SCN1A, SCN2A, SCN8A, SET, SETD5, SLC35A2, SLC6A1, SMAD4, SMARCA2, SMARCC1, SMC1A, SNAP25, STXBP1, SUV420H1, SYNGAP1, TBL1XR1, TCF20, TCF4, TRIM71, TRIO, UPF3B, USP9X, WAC, WDR45, ZBTB18, ZC4H2

Congenital heart defect

20/46 genes screened (43%)

ACVR2B, ARHGAP31, BRAF, BRAS, CBL, CFC1, CHD7, DLL4, DOCK6, EK1, EOGT, EVC1, EVC2, FBN1, HDAC8, HOXD13, HRAS, INVERSINA, JAG1, KDM6A, KMT2D, KRAS, LEFTY A, MAP2K1, MAP2K2, MLL2, NF1, NIPBL, NODAL, NOTCH2, NRAS, PAX2, PTPN11, RAD21, RAF1, RBPJ, RIT1, SALL4, SEMA3E, SHOC2, SMC1A, SMC3, SOS1, TBX5, TFAP2B, ZIC3

CAKUT

16/49 genes screened (33%)

ACE, AGT, AGTR1, CHD7, CHRM3, CREBBP, CRKL, DHCR7, DSTYK, EYA1, FGF20, FGFR1, FOXP1, FRAS1, FREM1, FREM2, GATA3, GDNF, GLI3, GPC3, GREB1L, HNF1B, HPSE2, JAG1, KAL1, KDM6A, KMT2D, LRIG2, MNX1, NIPBL, NOTCH2, NRIP1, PAX2, PBX1, REN, RET, ROBO2, SALL1, SALL4, SEMA3A, SIX1, SLIT2, SOX17, SRGAP1, TBC1D1, TBX18, TRAP1, VANGL1, WNT4

Inborn errors of metabolism

19/21 genes screened

ACADM, ARG1, ASAH1, ASS1, BTD, CBS, FUCA1, GAA, GALT, GLA, GNPTAB, GNPTG, IVD, MAN2B1, MUT, PAH, PCCA, PCCB, PEX, PHYH, SMPD1

Monogenic hearing loss

28/39 genes screened

CDH23, CLDN14, COL11A1, COL11A2, COL2A1, COL4A3, COL4A4, COL4A5, DIAPH1, EDN3, EDNRB, EYA1, EYA4, GJB2, GJB3, GJB6, KCNE1, KCNQ4, KVLQT1, MITF, MYH9, MYO15, MYO6, MYO7A, OTOA, OTOF, PAX3, PCDH15, POU3F4, POU4F3, SLC26A4, SLUG, SOX10, STRC, TCOF1, TECTA, USH1C, USH2A, USH3

Lethal skeletal dysplasia

14/23 genes screened (61%)

CEP120, CFAP410, COL11A1, COL11A2, COL1A1, COL1A2, COL2A1, DYNC2H1, DYNC2I, DYNC2I2, DYNC2LI1, DYNLT2B, FGFR3, FLNB, IFT122, IFT140, IFT172, IFT52, IFT80, IFT81, KIAA0586, KIAA0753, NEK1, SLC26A2, SLC2A2, SOX9, TRAF3IP1, TRIP11, TTC21B, WDR19

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