A survey of undetected, clinically relevant chromosome abnormalities when replacing postnatal karyotyping by Whole Genome Sequencing

Accepted Manuscript A survey of undetected, clinically relevant chromosome abnormalities when replacing postnatal karyotyping by Whole Genome Sequencing Ron Hochstenbach, Ellen van Binsbergen, Heleen Schuring-Blom, Arjan Buijs, Hans Kristian Ploos van Amstel PII:

S1769-7212(18)30045-4

DOI:

10.1016/j.ejmg.2018.09.010

Reference:

EJMG 3543

To appear in:

European Journal of Medical Genetics

Received Date: 17 January 2018 Revised Date:

30 July 2018

Accepted Date: 18 September 2018

Please cite this article as: R. Hochstenbach, E. van Binsbergen, H. Schuring-Blom, A. Buijs, H.K.P. van Amstel, A survey of undetected, clinically relevant chromosome abnormalities when replacing postnatal karyotyping by Whole Genome Sequencing, European Journal of Medical Genetics (2018), doi: https:// doi.org/10.1016/j.ejmg.2018.09.010. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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A survey of undetected, clinically relevant chromosome abnormalities

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when replacing postnatal karyotyping by Whole Genome Sequencing

Ron Hochstenbach1,2, Ellen van Binsbergen, Heleen Schuring-Blom, Arjan Buijs,

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Hans Kristian Ploos van Amstel

Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, P.O. Box 85090, 3508 AB Utrecht, The Netherlands 1

corresponding author

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Present address: Dr. Ron Hochstenbach Amsterdam UMC Vrije Universiteit Amsterdam Department of Clinical Genetics De Boelelaan 1117 1081 HV Amsterdam, The Netherlands Tel +31-20-4440932 Fax +31-20-4440744 E-mail [email protected]

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Abstract

Whole genome sequencing (WGS) holds the potential to identify pathogenic gene mutations,

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copy number variation, uniparental disomy and structural rearrangements in a single genetic test. With its high diagnostic yield and decreasing costs, the question arises whether WGS can serve as a single test for all referrals to diagnostic genome laboratories (“one test fits all”). Here, we provide an estimate for the proportion of clinically relevant aberrations identified by

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light microscopy in postnatal referrals that would go undetected by WGS. To this end, we

compiled the clinically relevant abnormal findings for each of the different referral categories in

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our laboratory during the period 2006-2015. We assumed that WGS would be performed on 300-500 bp DNA fragments with 150-bp paired sequence reads, and that the mean genome coverage is 30x, corresponding to current practice. For the detection of chromosomal mosaicism we set minimum thresholds of 10% for monosomy and 20% for trisomy. Based on the literature we assumed that balanced Robertsonian translocations and ~9% of other, balanced chromosome rearrangements would not be detectable because of breakpoints in

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sequences of repetitive DNA. Based on our analysis of all 14,957 referrals, including 1,455 abnormal cases, we show that at least 8.1% of these abnormalities would escape detection (corresponding to 0.79% of all referrals). The highest rate occurs in referrals of premature ovarian failure, as 73.3% of abnormalities would not be identified because of the frequent

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occurrence of low-level sex chromosome mosaicism. Among referrals of recurrent miscarriage, 25.6% of abnormalities would go undetected, mainly because of a high proportion of balanced

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Robertsonian translocations. In referrals of mental retardation (with or without multiple congenital anomalies) the abnormality would be missed in only 0.35% of referrals. These include cases without imbalances of unique DNA sequences but of clinical relevance, as for example, r(20) epilepsy syndrome. The expected shift to large-scale implementation of WGS (“one test fits most”) as initial genetic test will be beneficial to patients and their families, since a cause for the clinical phenotype can be identified in more cases by a single genetic test at an early phase in the diagnostic process. However, a niche for genome analysis by light microscopy will remain. For example, in referrals of newborns with a suspicion of Down syndrome, 2

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karyotyping is not only a cost-effective method for providing a quick diagnosis, but also discriminates between trisomy 21 and a Robertsonian translocation involving chromosome 21. Thus, when replacing karyotyping by WGS, one must be aware of the rates and spectra of

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undetected abnormalities. In addition, it is equally important that requirements for cytogenetic follow-up studies are recognized.

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Key words: karyotyping, whole genome sequencing, diagnostic yield, chromosomal mosaicism

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Introduction Next Generation Sequencing (NGS) has great potential in diagnostic laboratories for the

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genome-wide identification of pathogenic variants (Gilissen et al., 2014; Petrikin et al., 2015; Carvill and Mefford, 2015; Stavropoulos et al., 2016; Abou Tayoun et al., 2016; Strande and Berg, 2016; Dong et al., 2016a). Whole Genome Sequencing (WGS) might identify almost all types of pathogenic variation in the genome in a single genetic test, combining the detection of

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both genomic imbalances and mutations in any gene. For example, in patients with idiopathic developmental delay, who often have multiple congenital anomalies, pathogenic copy number variations (CNVs), and gene variants, such as missense and nonsense mutations, can both be

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detected by WGS (Petrikin et al., 2015; Carvill and Mefford, 2015; Stavropoulos et al., 2016). In addition, using WGS it is possible to detect smaller CNVs compared to microarrays (Gilissen et al., 2014), and to detect balanced structural variation as well (Redin et al., 2017). Also regions of Uniparental Disomy (UPD), that can be associated with disorders of genomic imprinting, can be identified. Initial studies have shown that WGS reveals the genetic cause for the clinical

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phenotype in a broad range of referral categories (Gilissen et al., 2014; Guo et al., 2014; Baxter et al., 2015; Fonseca et al., 2015; Dong et al., 2016b; Eggers et al., 2016; McRae et al., 2017; Lionel et al., 2017; Trujilano et al., 2017).

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Still, in many countries, it is common practice to refer patients for karyotyping. Karyotyping is the analysis of chromosome number and structure by light microscopy, usually in

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metaphases of cultured, stimulated lymphocytes from peripheral blood. Karyotyping is performed in various types of referrals, such as recurrent miscarriage, disorders of sexual development, or when a person might be carrier of a chromosomal rearrangement that is present in the family (Hastings et al., 2012). Also newborn babies with a clinical suspicion of trisomy 13, 18 or 21 are often referred for karyotyping because a diagnosis can be obtained within 48 hours. However, karyotyping is labor-intensive, and requires prolonged training of laboratory staff. It also has a limited resolution for the detection of genomic imbalances, because deletions and duplications can only be seen in G-banded metaphase chromosomes 4

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when they have a size of at least 5-10 Mb. Because of these limitations, DNA-based techniques such as oligonucleotide microarray comparative hybridization and Single Nucleotide Polymorphism-based microarrays have been introduced in the cytogenetics laboratories for

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more sensitive detection of imbalances. Also, Quantitative Fluorescence Polymerase Chain Reaction (QF-PCR) has been widely applied for rapid detection of chromosomal aneuploidies.

With an ongoing decrease in the costs of DNA sequencing (van Nimwegen et al., 2016), WGS is under consideration now as an alternative, initial genetic test for many referral

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categories. A recent study, based on a series of 549 cases, including products of conception, stillbirths, and prenatal and postnatal cases, revealed that a wide range of pathogenic genomic

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imbalances can be detected using WGS (Dong et al., 2016a). In addition, breakpoints of balanced chromosomal rearrangements have been identified using low-coverage paired-end WGS (Dong et al., 2014; Suzuki et al., 2014; Liang et al., 2017; Dong et al., 2017). However, before a large-scale introduction of WGS, one would like to know which chromosomal abnormalities of clinical relevance that are detectable by karyotyping are detectable also by

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WGS, and which would escape identification. We address this question by retrospectively analyzing all 14,957 postnatal referrals for karyotyping to our laboratory during a 10-year period (2006-2015). We show that a minimum of 8.1% of the abnormal findings that are reported as clinically relevant either to the patient or to the family will go undetected by WGS.

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This number serves as a baseline rate that must be acknowledged for its effect on diagnostic yield if laboratories offering genetic services consider to implement WGS as an alternative test

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for karyotyping.

Materials and Methods Using our laboratory database, we determined for each of the major categories of referrals for postnatal karyotyping the types and numbers of clinically relevant genomic aberrations that would escape detection by WGS. The different categories for referrals for 5

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postnatal karyotyping can be found in Table 1. Almost all cases were investigated by G-banded karyotyping of short-term lymphocyte cultures from peripheral blood. In a limited number of cases also cultured skin fibroblasts were investigated, or fluorescence in situ hybridization was

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performed on uncultured interphase cells from buccal mucosa, urine sediment, and in cases of stillbirth, placenta or umbellical cord. Our investigation is based on the referrals to our

laboratory during a 10-year period, thus providing a representative overview of the diversity of referrals and their associated abnormalities. We chose the period 2006-2015 because in 2006 we had supplanted karyotyping with microarray-based aneuploidy detection in referrals of

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patients with multiple congenital abnormalities and/or mental retardation (MCA/MR). This restricted the application of light microscopy to MCA/MR patients with a normal microarray

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result but suspected of having chromosomal mosaicism. For investigating products of conception we also shifted from karyotyping to microarrays. Thus, the referral categories included in the present investigation are those for which light microscopy, not a microarraybased approach, was the optimal choice for the initial genetic test. All cytogenetic investigations were performed in accordance with European guidelines regarding the minimal

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numbers of cells investigated and the minimal resolution of chromosome banding (Hastings et al., 2012). For each category we determined the number of referrals and the numbers and types of clinically relevant, abnormal karyotypes. For estimating which abnormal karyotypes would not be detectable by WGS, we made several assumptions, as explained in detail below.

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1. Sequencing method. We assumed that WGS is performed at 30x mean genome coverage on 300-500 bp DNA fragments in 2 x 150 paired-end format, as it is currently being

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offered by most providers, using Illumina HiSeq X Ten machines or equivalent (see Appendix 1). 2. Balanced Robertsonian translocations. We assumed that balanced Robertsonian

translocations are refractory to detection by current WGS technology because the breakpoints in the short arms of the rearranged acrocentric chromosomes (13, 14, 15, 21 and 22) are located in repetitive DNA sequences that cannot be unambiguously mapped on the human genome reference sequence. As expected, breaks in the short arm of chromosome 22 could not be detected in a validation study of breakpoint detection by WGS in carriers of a balanced rearrangement (Dong et al., 2014). Initial FISH studies showed that breakpoints are embedded 6

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between stretches of repetitive DNA-sequences of satellite I, II, III and IV and β-satellite (Page et al., 1996; Sullivan et al., 1996). For the common rob(13;14) and rob(14;21), that account for 75% and 10% of cases, respectively (Therman et al., 1989), it has only more recently been

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shown by FISH and PCR analysis that the breakpoints are invariably located in or close to clone CR382332, a clone containing segmental duplications (Jarmuz-Szymczak et al., 2014). In the other, less common Robertsonian translocations, breakpoint locations are variable with respect to the satellite repeat clusters, and have not been mapped at comparable precision (Page et al., 1986; Sullivan et al., 1986). However, for the purpose of our analysis, we assume that also the

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breakpoints of these less common Robertsonian translocations are not detectable by WGS. 3. Other balanced chromosomal rearrangements. We assumed that, in cases with

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another type of balanced rearrangement, 9.2% of breakpoints are not detectable by current methods for paired-end sequencing. This percentage is based on the failure to identify the breakpoints of balanced chromosomal rearrangements in 25 out of 273 patients with multiple congenital anomalies that were subjected to mate-pair sequencing (Redin et al., 2017). Also, in a smaller series of 10 balanced chromosomal rearrangements, the breakpoints could not be

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identified by WGS in one case (Suzuki et al., 2014). The reason that not all breakpoints can be mapped is the complex, repetitive architecture of the human genome. Together, repetitive DNA sequences, including LINE and SINE elements, segmental duplications, and low copy repeats, may comprise about two thirds of the human genome (de Koning et al., 2011). As shown for de

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novo, apparently balanced rearrangements in MCA/MR patients, the breakpoints were significantly enriched for repetitive DNA sequences (Redin et al., 2017), explaining why a

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fraction of short-read sequence data cannot be confidently mapped to a unique position in the reference sequence. In addition, the reference sequence still contains several gaps, unmapped sequences and inconsistently mapped sequences (Chaisson et al., 2015; Telenti et al., 2016), and breakpoints in such regions can also not be identified. We considered the number of 9.2% from the study of Redin et al. (2017) as a minimum estimate because paired-end sequencing in the vast majority of the 273 patients investigated in this study was based on large-insert longread libraries (insert sizes 2.5-3.5 kb). This enables breakpoint mapping across a larger fraction of the genome compared to short-insert (sizes 300-500 bp) paired-end sequencing (Chaisson 7

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and Tesler, 2012; Lee and Schatz, 2012). Thus, if short-insert libraries are used for WGS, it is likely that in a higher fraction of patients not all breakpoints can be detected. 4. Chromosomal mosaicism. Detection of mosaicism in clinical diagnostics is challenging

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for several reasons. First, abnormal, aneuploid cells can have different distributions in the various tissues of the patient (Fickelscher et al. 2007). Second, there may be selection against certain types of aneuploid cells in standard short term lymphocyte cultures used for

karyotyping (Menten et al., 2006; Ballif et al., 2006; Cheung et al. 2007). This can lead to underestimation of the percentage of abnormal cells, or even to failure of detecting the

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mosaicism. Third, methods of detection all have their limitations in identifying low degrees of mosaicism. Systematic validation studies of mosaicism detection were performed for SNP-

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microarrays, using bioinformatics tools that combine the logged ratio of observed probe intensity to expected intensity (Log R Ratio; LRR) with disturbances of the allelic balance of 1:1 expected for a diploid locus (B-allele frequency; BAF) (González et al., 2011). In MCA/MR patients studied in a diagnostic setting, reported detection limits using SNP-microarrays are 810% for a monosomy, 7-20% for a trisomy that introduces a third haplotype and 12-20% for a

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trisomy that does not (Conlin et al., 2010; Bruno et al., 2011; King et al., 2015). In a study of 31,717 cancer cases, gains could be reliably detected by SNP-microarrays if present in at least 20% of the cells (Jacobs et al., 2012). In contrast, there are only a few studies that have systematically assessed the sensitivity of WGS for detecting mosaic gains and losses of

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chromosomal segments or of entire chromosomes. In an in silico simulation study it was shown that at 30x mean genome coverage an optimal detection of mosaicism can be achieved by

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combining LRR and BAF, but only if the aneuploidy is present in at least 25% of cells (King et al., 2016). The method was more sensitive for mosaic copy number losses than for gains. In a WGS validation study based on clinical samples, mosaicism could be detected if present in at least 25% of cells (Dong et al., 2016a). With expected improvements of sensitivity in the future, we assumed that, for the purpose of our investigation, chromosomal losses would be detectable by WGS at 30x mean coverage if present in at least 10% of cells and gains when present in at least 20%. For simplicity, and also because short term lymphocyte cultures are likely to remain the

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standard method for obtaining metaphase chromosomes, we also assumed that the percentage

Results and Discussion for each referral category

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of aneuploid cells would not change significantly during cell culture.

For each referral category we determined the number of referrals, the number and

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types of clinically relevant, abnormal karyotypes and, based on the assumptions described above, how many of these would not be detectable by WGS at 30x mean genome coverage as

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an initial genetic test. A summary can be found in Table 1, the detailed data can be found in Appendix 2. From this, it can be determined how many of the cases with a normal WGS result would show a clinically relevant finding by karyotyping as a follow-up investigation. We also describe for each referral category how, dependent on their mechanism of origin, the chromosomal abnormalities can be recognized in WGS data by combining aberrant copy number and distorted allele frequency proportion, and which other considerations are relevant

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for selecting the most appropriate method, WGS or light microscopy, as an initial genetic test in an optimized diagnostic workflow.

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Recurrent miscarriage

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There were 5,881 referrals for recurrent miscarriage (defined as at least two

unexplained losses of a clinically recognized pregnancy) and there were 86 clinically relevant findings (see Appendix 2 for details). Balanced paracentric inversions were not considered, since there is no elevated risk for imbalance in the progeny (Gardner et al., 2012). There were 15 cases with a balanced Robertsonian translocations and one case with a translocation involving breakpoints in or adjacent to centromeres (whole-arm translocation). These 16 cases would not be detectable by WGS. The other 70 balanced rearrangements in this referral category all had two breaks each that were mapped in an euchromatic band by karyotyping at 9

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550-700 band resolution (ISCN 2016), and that in principle, would be detectable by WGS. Based on the study of Redin et al. (2017) we assumed a false-negative discovery rate of 9.2%, as discussed in detail in Assumption 3 in Materials and Methods. This implies that 6 of these 70

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balanced rearrangements would not be detectable by WGS. This gives a total of 22 undetected rearrangements (16 + 6) in this referral category, corresponding to 25.6% of the 86 clinically relevant findings and to ~0.4% of all cases.

We conclude that in unselected referrals for recurrent miscarriage about 25% of the

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clinically relevant, balanced genomic rearrangements would not be detected by WGS as an initial genetic test. Meanwhile, emerging, initial studies have demonstrated that it is possible to

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identify variants in protein coding genes that explain the recurrent pregnancy loss by Whole Exome Sequencing (WES) of both parents and the deceased fetuses (Ellard et al. 2015; Qioa et al. 2016). Thus, WGS at 30x mean genome coverage could be used as an initial test in these referrals, identifying both pathogenic gene mutations and most chromosomal rearrangements in a single experiment. However, it is still unknown to which extent WGS (or WES) of couples

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suffering from recurrent miscarriage would identify such causal variants in protein coding genes. Therefore, it still makes sense to perform karyotyping in these cases, as karyotyping has a higher sensitivity compared to WGS for the detection of those balanced chromosomal rearrangements that predispose to an elevated risk for miscarriage. The sensitivity of detection

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of euchromatic breakpoints is likely to improve by so-called “third generation sequencing” technology that generates longer sequencing reads (average length 10-15 kb) (Levy and Myers,

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2016; van Dijk et al., 2018).

Multiple congenital abnormalities and/or mental retardation(MCA/MR)

This is a broad referral category, including products of conception, stillbirths, and live born MCA/MR patients. The latter group includes patients with clinical symptoms of variable severity, ranging from newborns with life-threatening, multiple malformations to patients with 10

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mild mental retardation as the only clinical symptom. Referrals for Down syndrome are excluded as they are a separate category (see below). There were 10,865 MCA/MR referrals in this category. For almost all of these patients microarray analysis was performed as the initial

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genetic test, and in 1,114 cases a pathogenic imbalance was identified (10.2%). Karyotyping was done in 3,952 of the remaining 9,751 patients, because there was a suspicion of chromosomal mosaicism or as a follow-up study to clarify the chromosome structure that was associated with an abnormality identified by microarray. Also in patients with a suspicion of r(20) epilepsy

syndrome karyotyping was performed. There were 521 abnormal findings. Among these, we

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identified 14 cases that would not be detectable by WGS at 30x mean genome coverage, as outlined below (Table 2 and Table 3). First, there were 5 cases without detectable loss of single

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copy DNA sequences, such as in r(20) epilepsy syndrome (Vignoli et al., 2009; Daber et al., 2012) or r(14) syndrome (Zollino et al., 2012). Second, there were 8 cases with <20% chromosomal mosaicism for a supernumerary chromosome. These included cases with a supernumerary i(18)(p10) or i(12)(p10), or a trisomy of an entire chromosome, such as trisomy 8 or 9 in which detection by WGS is complicated because of the low levels of mosaicism

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inherent to live born individuals. In addition, an origin from maternal meiosis II in the majority of cases with +(12)(p10), +(18)(p10), +9 and +10 mosaicism, or from postmitotic nondisjunction in +8 mosaicism (Schinzel, 2001) leads to abnormal LRR but not to deviations of BAF, also complicating detection by WGS. In two cases with low level trisomy mosaicism (12% trisomy 10

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and 0.5% trisomy 22 in cultured lymphocytes), WGS would identify the abnormality because of LOH regions in the respective chromosome. Because the parents were not related, the most

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likely explanation for this finding was UPD by the rescue of a trisomy that resulted from nondisjunction during meiosis I. There was one rare case with <10% of cells with monosomy 20, reproducibly detected in uncultured blood cells (6.5%), cultured skin fibroblasts (3%) and uncultured buccal mucosa cells (1.5%) (Hochstenbach et al. 2014). This would only be possible using light microscopy.

It has been shown that de novo mutations in protein coding genes and de novo CNVs provide an explanation for the clinical phenotype in the majority of MCA/MR patients (Gilissen 11

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et al., 2014; Wilfert et al., 2017). We conclude that WGS would be the optimal, initial genetic test in MCA/MR referrals and that light microscopic analysis will add little to an expected diagnostic yield of ~40-60% by WGS (Gilissen et al., 2014; Stavropoulos et al., 2016; McRae et

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al., 2017; Lionel et al., 2017), as only 14 out of all 3,952 cases would go undetected by WGS at 30x mean genome coverage (corresponding to 0.35% of the cases in which karyotyping was performed, and to 0.13% of all 10,865 referrals in this category). However, for these rare cases,

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karyotyping remains a powerful method for obtaining a clinical diagnosis.

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Down syndrome

This is a distinct referral category because Down syndrome is the most frequent single aneuploidy in liveborn children, occurring at a rate of about 1 in 800-900 live births in Western societies (Loane et al. 2013; de Graaf et al. 2015; 2017), and also because a clinical diagnosis can be made in most cases based on a well-known, characteristic combination of specific

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symptoms. Almost all cases involve newborn children and a rapid confirmation of the clinical suspicion is required because of parental anxiety and clinical management. Of the 635 referrals, 398 (63%) had an abnormal, unbalanced karyotype (see Appendix 2 for details). Our findings are in concordance with the literature (Morris et al., 2012; Zhao et al., 2015). Among the 6

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mosaic cases, there was one with 4% trisomy 21 cells, which would not be detected by WGS. The other 5 cases had 38% to 81% trisomy-21 cells. This is in line with the generally accepted

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view that most Down patients with mosaicism are high-level mosaics (Hultèn et al., 2010). We also identified two abnormal, unbalanced, non-mosaic karyotypes not related to Down syndrome but most likely associated with the clinical symptoms. In one patient with trisomy 21 we also identified an additional abnormality (see Appendix 2). All of these abnormalities would be detected by WGS.

In summary, all but one of the abnormal findings in this referral category (99.75%) would be readily detectable by WGS at 30x mean genome coverage. At present, however, the 12

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cost of WGS, the need for a rapid diagnosis and the positive detection rate of >60% trisomy 21 cases together argue for a less expensive and quicker method. As an alternative to karyotyping, QF-PCR, Multiplex Ligation-dependent Probe Amplification (MLPA) or FISH on interphase cells

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can produce a result within one day, However, following a positive result a follow-up investigation by karyotyping is required because these methods are unable to distinguish

trisomy 21 from Robertsonian translocations involving chromosome 21. This distinction must be made to provide the parents with an estimate of the recurrence risk (Hastings et al., 2012; Gardner et al., 2012). Also, in case of a Robertsonian translocation, other family members with

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an elevated risk for having a child with Down syndrome can only be identified by karyotyping. Based on these considerations, we conclude that for this referral category, karyotyping, not

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WGS, will remain a quick and cost-effective method for providing all the information that is required for an adequate clinical management of patients with Down syndrome and their families.

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Ambiguous genitalia

There were 53 referrals. In 47 cases we received blood for karyotyping (96% of which were newborn babies) and in 6 cases a gonadal biopsy. There were 7 abnormal cases (13.2%)

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(see Appendix 2 for details). These abnormal karyotypes would all be detectable by WGS. We received gonadal biopsies from 6 patients for karyotyping. In 3 of these, biopsies were taken

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from each gonad and separately processed and karyotyped. Among these 6 patients we identified 3 abnormal results (5.7% of all referrals) (see Appendix 2), all of which would be detectable by WGS.

We conclude that, in our rather limited series, all abnormal cases would be detected by

WGS at 30x mean genome coverage and that karyotyping would add little to a normal WGS result. It has already been shown that a diagnostic yield of 35-69% can be obtained by WES in DSD patients with 46,XY karyotype (Baxter et al., 2015; Dong et al., 2016b; Eggers et al., 2016), 13

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and 60-69% in DSD patients with 46,XX karyotype (Dong et al., 2016b; Eggers et al. 2016). Thus, in this referral category, WGS would be the appropriate choice for the initial genetic test, detecting both pathogenic aneuploidies and mutations in protein coding genes in a single

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experiment.

Premature Ovarian Failure (POF)

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These are referrals of women with early onset of menopause (i.e. before the age of 36 years). Abnormal cytogenetic findings are rearrangements involving the X-chromosome that

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occur in every cell, such as X-deletions (Gardner et al., 2012), and mosaicism with 45,X cells at a higher rate than expected from the age of the patient (Devi et al., 1998; Russell et al., 2007). However, the level of 45,X mosaicism is expected to be <10% in these referrals (Devi et al., 1998) because females with a high level would be likely to show features Turner syndrome, such as short stature, leading to a referral for karyotyping earlier in life. We considered the

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level of 45,X mosaicism as abnormal when it was higher than the age-related level (as defined by Devi et al., 1998; Russell et al., 2007) at 3x Standard Deviation (SD). Among the 228 referrals, 15 abnormal cases (6.6%) were identified (see Appendix 2 for details). There were two cases of non-mosaic 45,X Turner syndrome and two cases with an X-chromosomal deletion in all cells

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investigated. All of these would be detected by WGS. There were 11 cases of 45,X mosaics, all at <10% but elevated for age. Thus, selection against 45,X cells during lymphocyte culture does

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not seem to prevent the detection of low-level mosaicism (also see Devi et al., 1998).

We conclude that, based on our limited series, the majority of abnormalities in this

referral category (11 of 15, corresponding to 73.3%) would not be detectable by WGS at 30x mean genome coverage. Pathogenic CNVs and variants in several genes have shown to be associated with POF (Chapman et al. 2015). In an initial study of a limited cohort, gene mutations were found in 2 of 12 patients with nonsyndromic POF (17%) (Fonseca et al., 2015). This suggests that also in this referral category, diagnostic yield of WGS as an initial genetic test

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would be much higher than that of light microscopy. After a negative result by WGS, karyotyping would provide additional diagnoses only by detection of low-level 45,X mosaicism

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and structural rearrangements of the X-chromosome.

Child with short stature (<-3 SD)

These are referrals of, in most cases, prepubertal girls with short stature (<- 3 SD). There

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were 693 referrals of which 24 (3.5%) had an abnormal finding (see Appendix 2 for details). Four of the 24 abnormal cases (16.7%) were 45,X mosaics with <10% of abnormal cells, and

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these would not be detected. In 8 other cases (33.3% of abnormals) the underlying karyotype may not have been revealed at sufficient precision by WGS. Among these, there were 4 cases with 45,X/46,X,i(X)(q10) mosaicism without evidence for the presence of 46,XX cells. These would all have been identified as abnormal using WGS because of the underrepresentation of DNA sequences from the short arm of the X chromosome. However, it is likely that by WGS

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alone, a distinction between 45,X/46,X,i(X)(q10) and 45,X/46,X,del(X)(p11.2) cannot be reliably made in all cases, because i(X)(q10) chromosomes are of two distinct types, with different mechanism of origin. More than 90% of i(X)(q10) chromosomes are in fact idic(X)(p11.2) chromosomes. These originate from a single ancestral X-chromosome during either maternal or

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paternal meiosis by non-allelic homologous recombination (NAHR) between inverted repeat sequence clusters in Xp11.2 (Lorda-Sanchez et al., 1991; Wolff et al., 1996; Scott et al., 2010).

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This results in identical DNA sequences in each half of the idic(X)(p11.2). If investigated by WGS, 45,X/46,X,idic(X)(p11.2) cases would be hard to distinguish from 45,X/46,X,del(X)(p11.2). In cases in which the long arms are each derived from one of the maternal X-chromosomes WGS could, in principle, provide such a distinction. However, it is unclear how current algorithms for detection of copy number variation in WGS data deal with such diverse situations. In addition, the percentages of 45,X and 46,X,idic(p11.2) cells can be more accurately determined by karyotyping (or FISH) compared to WGS at 30x mean genome coverage.

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Similarly, in 4 other cases with a structurally abnormal X chromosome, including a r(X), a del(X) and two der(X)t(X;Y)(p22.3;q12) cases, WGS is able to detect imbalances of the X chromosome but would not identify the underlying structure. For example, in a case in which

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equal proportions of 45,X and 46,X,r(X)(p22.11q21.2) cells were found, WGS would detect the imbalance at each terminal end of the X chromosome, but would reveal the structure of the abnormal r(X) only if the junction between short and long arms is not within repetitive DNA sequences. Also, mosaicism with a low proportion of 46,XX cells in a 45,X background cannot be proven by WGS at 30x mean coverage. Because in such cases the presence of 46,XX cells makes

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a critical difference in genetic counseling (Gardner et al., 2012), it is important that the clinician is presented with accurate and complete information about the karyotype. These examples

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demonstrate the limitations of WGS in revealing the underlying structure of an abnormal karyotype.

We conclude that, in referrals of children with short stature, 16.7% of the clinically relevant cases would not be detected by WGS at 30x mean genome coverage because of a low

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level (<10%) 45,X mosaicism. In another 33.3% (8 of 24) of cases, WGS would detect the aneuploidy but would not give all the information that is required for adequate genetic counseling and clinical management. It is likely that WGS will become the standard, initial genetic test in children with short stature, revealing both pathogenic CNVs and variants in

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protein coding genes (Dauber et al., 2014). In an initial study, a causative gene variant was detected by WES in at least 5 out of the 14 patients studied (36%) (Guo et al., 2014). This

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suggests that in referrals of short stature the diagnostic yield of WGS as an initial genetic test would be much higher than that of light microscopy. Thus, after a negative WGS-result, a follow-up investigation by karyotyping would provide a diagnosis in additional cases with lowlevel 45,X mosaicism. It would also reveal the structure of an abnormal X-chromosome when Xchromosomal aneuploidy has been detected by WGS.

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Girl with suspicion of Turner syndrome

Among the 310 referrals there were 20 abnormal cases (6.5%) (see Appendix 2 for

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details). The types and proportions of abnormal findings are in accordance with the literature (Wolff et al. 2010). In 2 of the abnormal cases (10.0%) there was <10% 45,X mosaicism, which would not have been detected. Also the 5 cases with 45,X/46,X,i(X)(q10) karyotype would have been detected (see above), but a case with 93% 45,X cells and 7% 46,X,r(X)(p11.22q24) cells

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would most likely not have been discriminated from non-mosaic 45,X.

We conclude that 10.0% of abnormalities (2 out of 20) are low level 45,X mosaics that

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would not be detectable by WGS. Similar to referrals of patients with short stature, WGS would detect structural abnormalities of the X chromosome, but not in all cases their underlying structure. Finally, structural abnormalities of the X chromosome that are present in a low proportion of cells, as illustrated by a case with <10% mosaicism for a r(X) chromosome, are undetectable as well. Conversely, if a WGS result would be indicative of non-mosaic 45,X in a

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Turner syndrome patient with signs of virilization, good clinical practice would require that a more sensitive method is used to search for the presence of Y-chromosomal material. There are strong indications that viable, apparently non-mosaic 45,X patients originate from a 46,XX or 46,XY embryo by mitotic loss. As a result, they are mosaics with a 46,XX or 46,XY cell line that is

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undetectable in cultured blood lymphocytes, but that served to rescue the patient during the embryonic phase, most likely because of its presence in the placenta (Hook and Warburton

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2014). When 46,XY cells are detected there is an increased risk of gonadoblastoma (Wolff et al., 2010; Ackermann and Bamba, 2014). Because WGS at 30x mean genome coverage has limitations in the detection of low level mosaicism (<25%) of 46,XY cells, more sensitive methods such as FISH on uncultured lymphocytes should be employed. PCR-based methods are not recommended because of a high rate of false-positive results (Wolff et al., 2010; Ackermann and Bamba, 2014). Thus, also in some cases of a straightforward diagnosis of 45,X Turner syndrome, WGS is unable to give all the information required for an adequate clinical management of the patient. The added value of karyotyping in cases with a negative WGS

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result, similar to referrals of short stature, is the detection of low-level 45,X mosaicism and the

Boy or male with suspicion of Klinefelter syndrome

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underlying structural basis of X-chromosomal aneuploidy.

There were 253 referrals. An abnormal karyotype was detected in 45 cases (17.8%) (see Appendix 2 for details). The majority of the abnormal karyotypes were non-mosaic 47,XXY

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males (80.0% of abnormalities). This is in line with the literature (Frühmesser and Kotzot, 2011). These would all have been detected by WGS, but are of two different types with respect to the

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number of X-chromosomal haplotypes. When the 47,XXY karyotype is the result of a segregation error during meiosis I there are two different haplotypes for the X-chromosome. This is the case in ~50% of patients with a paternal X and Y chromosome and ~25% with two different maternally-derived X chromosomes. In the remaining ~25% of patients there is only one X-haplotype, due to a maternal meiosis II error or to a postzygotic mitotic error (Thomas

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and Hassold, 2003). In these latter cases only an increased dosage of X-specific sequence reads would lead to the identification of the 47,XXY male karyotype by WGS. There were 2 cases with <20% 47,XXY mosaicism in a 46,XY background that would go undetected by WGS (4.4% of abnormal findings). Also 2 additional cases with low level mosaicism of 48,XXXY and 49,XXXXY

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cells in a 47,XXY background would not have been discriminated from nonmosaic 47,XXY (4.4%

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of abnormal findings).

Also the variant Klinefelter karyotypes in our cohort, if present in a non-mosaic state,

can be recognized in WGS data. There was one case with a non-mosaic 48,XXYY karyotype. Because 96% of such patients have two distinct X-chromosomes that originate either from the two parents or from nondisjunction during maternal meiosis I (Hager et al., 2012), these abnormal karyotypes will be identified by WGS because of the increased dosage of X-specific sequence reads and the presence of two alleles for X-linked SNPs. Although the two Y chromosomes are identical, the increased relative number of Y chromosomal sequence reads 18

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will lead to a correct identification of the 48,XXYY karyotype. About 4% of 48,XXYY patients have two identical X-chromosomes, either from nondisjunction during maternal meiosis II or from a mitotic error during early embryogenesis (Hager et al., 2012). Only an increased

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abundance of sequence reads from both the X and Y chromosomes would make these cases stand out from a normal 46,XY karyotype. In theory, this should also be possible. There was one case with a non-mosaic 49,XXXXY karyotype. Although a limited number of cases has been studied, the X chromosomes in such patients result from successive nondisjunction events during maternal meiosis I and II (Lorda-Sanchez et al., 1992; Leal et al., 1994). Thus, following

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WGS at 30x mean genome coverage in such patients, X-linked SNPs would show two alleles at equal frequencies, as in 75% of 47,XXY cases. Discrimination between non-mosaic 49,XXXXY and

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47,XXY karyotypes would be based on the relative dosage of X versus Y and of X versus autosomal sequence reads. Also a single Klinefelter patient with a

46,X,der(X)t(X;Y)(p22.33;p11.2) karyotype with the SRY gene being present on the der(X)t(X;Y) chromosome (also known as a ‘46,XX male’, see Frühmesser and Kotzot, 2011) could, in principle, be detected by WGS due to the presence of Y chromosome-specific DNA sequences

remains to be proven.

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that originate from the region immediately proximal to the pseudoautosomal region 1, but this

Several other variant karyotypes have been reported in patients with Klinefelter

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syndrome (Frühmesser and Kotzot, 2011) but have not been detected in our study. For example, we did not find a case with a non-mosaic 48,XXXY karyotype. Again, there is limited

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information on the origin of the X chromosome in such patients, but in most cases that were studied all X chromosomes either came from the mother (Lorda-Sanchez et al., 1992) or from the father (Leal et al., 1994). In theory, the non-mosaic 48,XXXY karyotype would be recognizable in WGS data and can be discriminated from non-mosaic 47,XXY and 49,XXXXY by the relative dosage of X-specific sequence reads. It is clinically important to make such distinctions based on accurate determination of the karyotype (Tartaglia et al., 2011). In addition, there was no patient with a non-mosaic 47,XY,+i(X)(q10) karyotype, a rare abnormality among Klinefelter patients (Frühmesser and Kotzot, 2011). In theory, this 19

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abnormality would be detectable in WGS sequence data because of the additional dosage of DNA sequences from the long arm of the X chromosome. Also Klinefelter patients with a structurally abnormal, supernumerary X or Y-chromosome in all cells would be detectable by

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WGS.

We conclude that 8.9% of abnormal karyotypes would not be detected by WGS at 30x mean genome coverage in patients suspected to have Klinefelter syndrome. The added value of karyotyping after a negative WGS result would be the identification of low-level 47,XXY

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mosaicism against a 46,XY background. Karyotyping will also detect low-level 48,XXXY and 49,XXXXY mosaicism against a 47,XXY background. These distinctions are clinically relevant

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(Tartaglia et al., 2011). In 47,XXY Klinefelter patients, detection of 46,XY mosaicism higher than expected from the patient’s age is of relevance for genetic reproductive counseling, because even males with 6-7% 46,XY metaphases in cultured lymphocytes are more likely to have sperm in the ejaculate (Lenz et al., 2005; Garcia-Quevedo et al., 2011). In several types of other abnormal results, it remains to be demonstrated whether WGS is suitable to identify the

reliably.

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precise nature of the abnormal karyotype and to provide all clinically relevant information

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Male with infertility/subfertility electable for ICSI

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There were 1,047 referrals, of which 57 (5.4%) were abnormal (see Appendix 2 for details). This is concordant with the literature (Tuerlings et al., 1998; Gekas et al., 2001; Foresta et al., 2005). Because these males were referred for karyotyping during adulthood, the proportion of non-mosaic 47,XXY cases among the abnormal findings was smaller than in referrals for Klinefelter syndrome (26.3% versus 80.0%), and conversely, the proportion of mosaics with <20% 47,XXY cells in a 46,XY background was larger (26.3% versus 4.4%). These low-level mosaics would not have been detected based on WGS at 30x mean genome coverage.

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Also 3 cases with <10% mosaicism of 45,X cells in a 46,XY background would not have been detected (5.3% of abnormal findings).

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Balanced chromosomal rearrangements are found at an elevated frequency in males with reduced fertility, as has been repeatedly shown in several studies (Tuerlings et al., 1998; Gekas et al., 2001; Foresta et al., 2005). As in comparable cohorts from the literature, 15.8% of the males with an abnormal karyotype (9 of 57 cases) had a balanced Robertsonian

translocation. As discussed above, these would not be detected by WGS. Another 15.8% of

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males (9 of 57) with an abnormal result had a balanced, reciprocal translocation or an inversion. We assume that in one of these not all breakpoints would be detectable by WGS (cf. Redin et

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al., 2017). There were 4 cases with a structurally abnormal Y chromosome as the explanation for the infertility, all of which would have been detected by WGS.

We conclude that 49.1% of the abnormal findings (28 of 57) in this referral category would not be detectable by WGS at 30x mean coverage. It is likely, however, that WGS as an

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initial genetic test will lead to a higher diagnostic yield in referrals of reduced male fertility than karyotyping. Based on family studies, autosomal recessive mutations have been estimated to underlie more than half of the cases of male infertility (Lilford et al., 1994). Genetic studies in model organisms indicate that up to 1500-2000 distinct, recessive genes can be mutated to

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cause male infertility, with each gene being responsible for only a small fraction of cases (Hackstein et al., 2000; Matzuk and Lamb, 2008). Thus, it can be predicted that WGS will lead to

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the identification of homozygous or compound heterozygous pathogenic mutations. Proof-ofprinciple for detection of autosomal recessive mutations in referrals for male infertility by WES has already been provided several years ago (Zariwala et al., 2013; Moore et al., 2013; Onoufriadis et al., 2014), and clinical validation studies are emerging (Oud et al., 2017). The major added value of karyotyping after a normal result by WGS would be limited to the identification of low-level 47,XXY mosaicism, that occurs at a frequency of ~1.5% in this referral category, and to the detection of balanced Robertsonian translocations, occurring at ~1%.

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Family member has chromosomal rearrangement

This referral category includes family members of patients with a chromosomal

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rearrangement. For example, if a derivative chromosome is found in a child with MCA/MR, one of the parents could be carrier of the corresponding balanced rearrangement. There were 1,905 referrals, with 282 carriers being identified (see Appendix 2 for details). There were 33 carriers of a balanced Robertsonian translocation (11.7% of carriers). WGS is not able to detect

balanced Robertsonian translocations, as discussed earlier. There were 150 carriers of a

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balanced, non-Robertsonian, reciprocal translocation or of a balanced inversion (53.2% of carriers). Again, as discussed earlier, the vast majority of these carriers would be identified by

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WGS, especially since data interpretation would be guided by the available cytogenetic interpretation of the rearrangement. The remaining 99 carriers (35.1%) concerned parents of a child in which a pathogenic imbalance had been identified using microarrays. Also in these cases, WGS will correctly identify almost all carrier parents, because analysis will be based on a priori knowlegde of the abnormality in the child at the level of chromosome banding. However,

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current guidelines (Claustres et al., 2014) state that the carrier status of the parents must be investigated by karyotyping and FISH. This has two reasons. First, the parent may have a low level mosaicism (<25%) of a supernumerary marker that, in the child, is present in all cells. Second, one of the parents can be carrier of a balanced insertional translocation that is

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associated with the imbalance in the child. Such rearrangements occur in about 2% of cases (Nowakowska et al., 2012), and, in rare cases, can even occur when the imbalance in the child

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has resulted from NAHR between low copy repeats (Carelle-Calmels et al. 2009). Because balanced insertional translocations confer a high risk of recurrence (Gardner et al., 2012), it is essential not to miss their identification. As shown by Redin et al. (2017) and as discussed above, current short-read NGS methods cannot detect all euchromatic breakpoints. Therefore, replacing light microscopy by WGS as a method for carrier identification is awaiting appropriate validation studies. This also applies to the introduction of so-called “third generation sequencing” technology, enabling longer read lengths (Levy and Myers, 2016; van Dijk et al., 2018). This may considerably improve the sensitivity for detecting euchromatic breakpoints. 22

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We conclude that at least 33 of the 282 carriers would not be identified by WGS (11.7%). This is a minimum estimate, because, as shown by the study of Redin et al. (2017),

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9.2% of rearrangements with breakpoints in the euchromatin will not be detected. It is likely that the fraction of undetected rearrangements among the 249 other carriers would be less than 9.2% because the chromosomal band position of the breakpoints is known, but how much

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would remain undetected awaits further study.

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General Discussion

Large-scale introduction of WGS as the initial genetic test will be feasible in the near future because a wide range of pathogenic genome alterations can be identified across a patient’s entire genome in a single test. Also, decreasing costs (van Nimwegen et al., 2016) and an increased efficiency of interpretation of genomic variants as more genomic variants are

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being deposited in curated databases (Rehm, 2017) will contribute to the implementation of WGS. Compared to traditional light microscopy, diagnostic yields of WGS will be much higher across a wide spectrum of referrals. This has already been established in referrals for intellectual disability and neurodevelopmental disorders (Gilissen et al., 2014; Petrikin et al.,

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2015; Carvill and Mefford, 2015; Stavropoulos et al., 2016; McRae et al. 2016; Lionel et al., 2017). Also in other referral categories that, at present, are investigated by karyotyping and

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FISH in most laboratories, diagnostic yields by WGS are likely to be much higher, as shown, for example, for DSD (Baxter et al., 2015; Dong et al., 2016b; Eggers et al., 2016), POF (Fonseca et al., 2015), and short stature (Guo et al., 2014). Also in referrals of male infertility this is likely to be the case (Oud et al., 2017). WGS also holds additional, novel diagnostic potentials because knowledge about the significance of genome architecture for the regulation of gene transcription is rapidly growing (Kloosterman and Hochstenbach, 2014; Scacheri and Scacheri, 2015). For example, WGS can reveal structural variations in the non-coding part of the genome that are associated with Mendelian disorders, such as deletions and duplications of enhancers 23

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that regulate gene transcription. This has been shown, for example, for upstream enhancers of the IHH gene (Will et al., 2017). Also, structural variation in intragenic enhancers may be associated with genetic disorders (Cinghu et al., 2017). In addition, WGS can identify disruptions

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of chromatin boundary elements, as has been shown for different types of limb malformation disorders (Lupianez et al., 2015). Thus, WGS will become an affordable tool for first-tier clinical genome investigation.

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However, despite the high diagnostic yield of WGS, the question arises whether the abnormalities that can be detected by light microscopy are detectable also by WGS. In this

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study we determined the rate and types of abnormal karyotypes that cannot be identified using low-coverage WGS in postnatal referrals. Using a representative sample of 14,957 postnatal patients that were referred to our laboratory for karyotyping during a 10-year period, our survey (summarized in Tables 1, 2, and 3) shows that in a minimum of 118 patients (representing 8.1% of the abnormal, clinically relevant results, and 0.79% of all referrals) the genetic cause of the clinical symptoms would not be detected using low-coverage (30x), short-

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insert paired-end WGS. We believe that this is an accurate minimum estimate because of realistic underlying assumptions. First, we assumed that in cases of chromosomal mosaicism there is a detection limit of 20% abnormal cells for trisomy and 10% for monosomy. These provide minimum estimates of the numbers of cases that are not detected by WGS

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because in silico simulation studies (King et all., 2016) and a prospective study of 549 clinical samples investigated by low-pass WGS (Dong et al., 2016a) indicate that mosaicism is

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detectable if present in at least 25% of cells. On the other hand, WGS may detect mosaicism in cases where karyotyping would not. This is because WGS is based on DNA extracted from uncultured cells, thereby avoiding potential selection against abnormal, aneuploid cells during culturing of lymphocytes for karyotyping. This phenomenon has been well documented in several sporadic cases involving autosomal aneuploidies (Menten et al., 2006; Ballif et al., 2006; Cheung et al., 2007). However, systematic validation studies to assess the extent of this problem are not yet available. Second, we assumed that in 9.2% of cases, the breakpoints of balanced chromosomal rearrangement would not be detectable. This is based on the largest 24

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study to date (including 273 patients) in which WGS was employed for identification of breakpoints (Redin et al., 2017). Thus, the majority of the abnormal karyotypes that cannot be detected by low-pass WGS are cases of low-level chromosomal mosaicism and cases with

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balanced rearrangements that have breakpoints in repetitive DNA sequences that cannot be mapped to a unique position in the human genome reference sequence. In a few other,

exceptional, cases WGS would not detect the abnormality because there is no imbalance, as in r(20) epilepsy syndrome. Thus, we envisage WGS as a promising “one test fits most”, not as

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“one test fits all”.

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The rates at which clinically relevant abnormal results remain undetected vary widely between the different referral categories (Table 1). The rate is low in MCA/MR referrals (0.35% of referrals), because non-mosaic unbalanced karyotypes prevail (Hochstenbach et al., 2009) that will not escape detection by WGS, and this is also the case in referrals for Down syndrome (0.14% of referrals). The highest rates are found in referrals for POF and male infertility, as lowlevel mosaicism of the sex chromosomes is a frequent finding in these categories. In POF

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referrals, 73% of the abnormal results (corresponding to 4.8% of referrals) were mosaics with <10% 45,X cells but at a higher rate than expected from the age-related loss of the X chromosome. This is inherent to this referral category because the reason for karyotyping is not

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related to clinical signs of Turner syndrome, such as short stature, but to early menopause in a woman with minimal signs of Turner syndrome (Homer et al., 2010). Among males with reduced fertility, 31.5% of abnormal cases (corresponding to 1.7% of referrals) have

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undetectable, low-level mosaicism. Again, this is inherent to this referral category, because the reason for karyotyping is reduced fertility in an otherwise normal male.

In referrals of Down syndrome, suspicion of trisomy 13 or 18, and ambiguous genitalia,

which usually concern newborn children, there is a need for a rapid result because of parental distress and clinical management. Although, in principle, WGS can give a result 26 hours after the blood sample was drawn (Miller et al., 2015, it is more cost-effective to perform a rapid, targeted molecular test in such referrals. In case of a positive result, karyotyping is required in 25

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cases of Down syndrome and trisomy 13 to discriminate a true trisomy from an unbalanced Robertsonian translocation. This serves to determine the recurrence risk and, if desired, to

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identify family members who may be carriers.

Conversely, when WGS is used as a routine first-tier test in a pediatric setting and

pathogenic CNVs or gene mutations have not been found, several other genetic causes for the clinical phenotype cannot be excluded. As shown here, some of these can be readily identified using karyotyping. A typical example is r(20) epilepsy syndrome, a focal epilepsy with onset in

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childhood. Patients have a characteristic electroencephalogram and most of them show normal development until seizures start at an average age of 7-9 years (Daber et al., 2012). WGS is

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unable to detect the genomic aberration because the r(20) does not have terminal deletions, as detected by microarray or FISH using DNA-probes specific for the subtelomere regions of the short and long arm of chromosome 20 (Daber et al., 2012). Such a copy-number neutral r(20) chromosome can be efficiently detected by karyotyping. In addition, the degree of mosaicism in blood is correlated to the age of onset of epilepsy (Vignoli et al., 2009; Conlin et al., 2011) and

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can also be precisely determined by karyotyping. Therefore, karyotyping is the method of choice when there is a clinical suspicion of r(20) epilepsy syndrome. Similarly, when there are asymmetric physical features or uneven skin pigmentation, a suspicion of chromosomal mosaicism is justified (Biesecker and Spinner, 2013; Hochstenbach et al., 2014) and karyotyping

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should be instigated, preferably also in tissues other than blood. In the cohort chosen for our survey, we detected several cases of rare chromosomal mosaicism at <10% of cells, such as

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trisomy 8, trisomy 9, and monosomy 20. Also, imprinting disorders (not discussed in this cohort) may not be detectable by WGS, requiring the application of methods that determine the methylation status of specific genomic regions. Finally, it is equally important that requirements for follow-up studies by karyotyping and FISH are recognized when there is an abnormal WGS result, in line with current guidelines for abnormal microarray-based findings (Hastings et al., 2012; Nowakowska et al., 2012). A transition from light microscopy to WGS should involve a comprehensive analysis of cost-effectiveness and effects on heatlh care in general. A framework is being developed to this 26

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end, including all relevant aspects such as the costs of WGS, its impact on the allocation of finite health care resources, and its effect on the well-being of patients (Payne et al., 2018). Recent estimates of the cost per sample of WGS in a clinical setting are €1411 (Plöther et al., 2017),

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€1669 (van Nimwegen et al., 2016), and €1845 (Tsiplova et al. 2017). These estimates include the cost of building and operating a bioinformatics infrastructure, which range from €135 to €224 per sample. Also, WGS may be more readily automated compared to light microscopy. Therefore, it may be more effectively used as a high-throughput technique. This may lead to lower costs per sample in the near future. Large-scale sequencing projects are currently being

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undertaken, such as the Genomics England 100 000 Genomes Project (Turnbull et al., 2018),

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and will enable an initial evaluation of the impact of WGS on health care.

An additional complexity of WGS is that the frequency of “secondary” findings (also called “unsolicited” findings) is much higher compared to kayotyping. WGS can disclose clinically relevant genomic variants that are unrelated to the initial indication for genetic testing, such as variants of reduced penetrance for life-threatening conditions, pathogenic

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variants of late onset diseases, and carrier status for autosomal recessive disorders. Laboratories must implement procedures to manage secondary findings, and practical and ethical issues for doing so are under debate (Saelaert el., 2018). For example, efforts are being undertaken to define minimum lists of “medically actionable” genes. Secondary variants in

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these genes that have to be reported are expected to occur in 1.2% to 3.4% of cases (Dorschner et al., 2013; Amendola et al., 2015; Kalia et al., 2017; Tang et al., 2018). However, depending on

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the stringency of variant interpretation and the type of cohort under investigation, a frequency of 8.8% has been reported as well (Lawrence et al., 2014). Secondary findings will add to the complexity of both reporting and genetic counseling and may add substantially to the costs of WGS.

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Concluding remarks Our survey of postnatal referrals during a 10-year period reveals the rates and types of genomic abnormalities that can be identified using light microscopy. WGS has an

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unprecedented resolution and diagnostic yield (Gilissen et al., 2014; Petrikin et al., 2015; Carvill and Mefford, 2015; Stavropoulos et al., 2016; Trujilano et al., 2017; Lionel et al., 2017)

surpassing that of karyotyping by far. However, WGS, as it is currently performed, based on 300-500 bp insert sizes at 30x mean genome coverage, has inherent limitations, and would miss

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a minimum of 8.1% of cytogenetically detectable abnormalities (corresponding to 0.79% of all referrals, see Table 4 for overview). Thus, in referrals where such aberrations can be expected

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genome analysis by light microscopy will remain an effective method to provide clinicians with all the relevant information for the management of patients and their families. We conclude that WGS is destined to become the ultimate genetic test in diagnostic genome laboratories, and implementation of long-read sequencing may overcome some of the current limitations (Levy and Myers, 2016; van Dijk et al., 2018). At the same time, however, laboratories face the

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challenge to accommodate, at an acceptable cost, a declining number of referrals for light microscopy, while also maintaining the expertise of technicians and staff members involved

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(Hochstenbach et al., 2017).

Conflicts of interest

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There is no conflict of interest

Acknowledgments

We are indebted to the technicians of our laboratory for expert skills in clinical

cytogenetics, and to Lars van der Veken for expert microarray analyses. We thank Martin Elferink, Wigard Kloosterman, Marco Koudijs, Martin Poot, Stef van Lieshout, Ies Nijman and Daoud Sie for discussion.

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Table 1. Numbers of abnormalities undetectable by WGS in referrals for postnatal karyotyping to our laboratory during 2006-2015

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------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------referral reason (subcategories/explanation) number of number of undetectable by WGS referrals abnormal results ----------------------------------------------------------------(percentage number percentage percentage of referrals) of abnormal of total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------recurrent miscarriage (couples who had at least two 5,881 86 (1.5%) 22 25.6% 0.37% unexplained miscarriages) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------multiple congenital abnormalities and/or mental retardation 3,952 521 (13.2%) 14 2.7% 0.35% (products of conception, stillbirths, suspicion of trisomy 13 or trisomy 18, all other postnatal patients, from newborns to adults, including cytogenetic follow-up of abnormal microarray results) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Down syndrome (suspicion of trisomy 21, usually in newborns) 635 398 (62.7%) 1 0.25% 0.14% ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------disorders of sexual development child (usually newborn) with ambiguous genitalia 53 7 (13.2%) 0 0.0% 0.00% 11 73.3% 4.82% woman >36 years of age with POF 228 15 (6.6%) child with short stature (<-3 x SD) 693 24 (3.5%) 4 16.7% 0.57% girl with suspicion of Turner syndrome 310 20 (6.5%) 2 10.0% 0.65% boy or male with suspicion of Klinefelter syndrome 253 45 (17.8%) 4 8.9% 1.58% ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------reduced male fertility (infertile/subfertile male electable for ICSI) 1,047 57 (5.4%) 27 47.4% 2.58% ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------carrier of chromosomal rearrangement (family member has Down 1,905 282 (14.8%) 33 11.7% 1.73% syndrome; family member has other chromosomal rearrangement) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------total 14,957 1,455 (9.7%) 118 8.1% 0.79% ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Note 1 We did not include referrals of postnatal follow-up of abnormal prenatal findings in our survey

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Table 2. Fourteen MCA/MR referrals in which WGS would not detect the genomic aberration -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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referrals in which WGS is not chromosomal number of major reason why WGS added value of light microscopy the optimal, initial genetic test abnormality cases identified is not the optimal test {additional, supporting evidence for abnormal karyotype} ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------cases without detectable r(14) mosaicism 2 there may be no loss of single if there is no imbalance, only karyotyping can show 1 genomic imbalance copy DNA at terminal ends ; ring that a r(14) is there, karyotyping also reveals the level fusion in repetitive DNA sequences of mosaicism 3

r(20) formed by direct telomere if there is no imbalance, only karyotyping can show 2 to telomere fusion ; ring fusion that a r(20) is there; karyotyping also reveals the level in repetitive DNA sequences of mosaicism ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------cases with low degree trisomy 8 4 mosaicism 10%, 13%, 16% and 17% investigating a large number of uncultured cells by FISH 3 of mosaicism trisomy 9 1 mosaicism 19% investigating a large number of uncultured cells by FISH +i(12)(p10)

2

mosaicism 0.5% and 10%

discriminates +i(12)(p10) from translocations involving 12p

+i(18)(p10)

1

mosaicism 6%

discriminates +i(18)(p10) from other rearrangements involving 18p

TE D

M AN U

SC

r(20) mosaicism

4

AC C

EP

monosomy 20 1 mosaicism 3% (in cultured fibroblasts) {6.5% in uncultured blood using interphase FISH } ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Notes 1 in 6 out of 27 cases with r(14) mosaicism there was no detectable loss of DNA from chromosome 14 by microarray (Zollino et al., 2012) 2 in r(20) syndrome absence of terminal deletions was shown by microarray or FISH using subtelomere-specific DNA probes or TTAGGG telomere probes (Daber et al., 2012) 3 mosaicism with >25% abnormal cells is detectable at 30x mean genome coverage (Dong et al., 2016; King et al., 2016) 4 this case was published by Hochstenbach et al. (2014)

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Table 3. Summary of clinically relevant abnormal karyotypes undetectable by WGS, identified in our laboratory by light microscopy during 2006-2015

3,952

521

53

7

Premature Ovarian Failure

228

15

child with short stature

693

24

Turner syndrome

310

20

Klinefelter syndrome

253

45

reduced male fertility

1,047

ambiguous genitalia

1 mosaic trisomy 21 0

11 mosaicism 45,X/46,XX

TE D

398

57

no detectable loss of unique DNA sequences in r(20) no detectable loss of unique DNA sequences in r(14) +i(12)(p10) in <20% of cells +i(18)(p10) in 6% of cells mosaicism <20% mosaicism <20% mosaicism in 4% of cells

<10% mosaicism of 45,X cells

4 mosaicism 45,X/46,XX

<10% mosaicism of 45,X cells

2 mosaicism 45,X/46,XX

<10% mosaicism of 45,X cells

2 mosaicism 47,XXY/46,XY 1 mosaicism 48,XXXY/47,XXY 1 mosaicism 49,XXXXY/47,XXY

<10% mosaicism of 47,XXY cells <10% mosaicism of 48,XXXY cells <10% mosaicism of 49,XXXXY cells

EP

635

AC C

Down syndrome

M AN U

3 r(20) epilepsy syndrome 2 r(14) syndrome 2 Pallister-Killian syndrome 1 +(18)(p10) mosaicism 5 mosaic trisomy 8, 9 1 mosaic monosomy 20

SC

MCA/MR

RI PT

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------referral category number number of number of clinically relevant reason for missing of referrals abnormals abnormals not detectable by WGS diagnosis using WGS ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------recurrent abortion 5,881 86 15 Robertsonian translocations breakpoints in repetitive DNA sequences in short arms 1 whole arm translocation breakpoints in alpha satellite repeats 6 other, balanced rearrangements breakpoints in “euchromatic” repetitive DNA sequences

15 patients with Klinefelter syndr. 3 45,X/46,XY patients 9 Robertsonian translocations

46,XY with <20% mosaicism of 47,XXY cells 46,XY with <10% mosaicism of 45,X cells breakpoints in repetitive DNA sequences

family member has 1,905 282 33 Robertsonian translocations breakpoints in repetitive DNA sequences in short arms chromosomal rearrangement ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------total 14,957 1,455 118 (8.1% of referrals with abnormal karyotype) -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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Table 4. Situations in which karyotyping is an effective method in postnatal referrals -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------referral category

motivation other than cost-effectiveness

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------speed, detection of structural rearrangements

newborns with ambiguous genitalia

RI PT

newborns with clinical suspicion of Down syndrome/trisomy 21, trisomy 13 or trisomy 18

speed, detection of low-level mosaicism

clinical suspicion of r(20) epilepsy syndrome based on EEG

absence of genomic imbalance detectable by WGS

SC

clinical suspicion of chromosomal mosaicism based on skin pigmentation anomalies or body asymmetry

detection of low-level mosaicism in uncultured cells insertion translocation in parent is possible

premature ovarian failure

4.8% of referrals are mosaics with <10% 45,X cells

M AN U

identification of chromosomal rearrangement underlying a pathogenic genomic imbalance identified by WGS

clinical suspicion of Klinefelter syndrome reduced male fertility family member has Robertsonian translocation

TE D

family member has other chromosomal rearrangement detectable by karyotyping or FISH

1.6% of referrals are mosaics with <20% 47,XXY cells 1.7% of referrals have low-level 47,XXY or 45,X mosaicism breakpoints located in repetitive DNA sequences breakpoints may be in repetitive DNA sequences

AC C

EP

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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Appendix 1

RI PT

Table A1. Providers of WGS for clinical samples1 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------

SC

provider platform coverage website ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Bejing Genomics Illumina HiSeq X Ten 30x http://www.bgi.com/services/genomics/whole-genome-re-sequencing/human/ Institute Illumina HiSeq X Ten

30x

http://genomics.broadinstitute.org/products/whole-genome-sequencing

Centogene

Illumina HiSeq X Ten

40x

http://www.centogene.com/analytical-services/whole-genome-sequencing-with-centogenome.html

Genewiz

Illumina HiSeq X Ten

30x

http://web.genewiz.com/X-Ten-Web-Human.html

Omega Bioservices

Illumina HiSeq 2500

30x

http://omegabioservices.com/index.php/next-gen-sequencing/whole-genome/

Novogene

Illumina HiSeq X Ten

30~50x

https://en.novogene.com/next-generation-sequencing-services/human-genome/ whole-genome-sequencing-service/

TE D

M AN U

Broad Institute Genomic Services

Veritas Genetics Illumina HiSeq X Ten 30x https://www.veritasgenetics.com/mygenome -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

AC C

EP

Note 1 Many other providers can be found via https://www.scienceexchange.com/services/whole-genome-seq

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Appendix 2 - clinically relevant abnormal karyotypes for each referral category

Table A2.1. Recurrent miscarriage -------------------------------------------------------------------------------------------------------------------------------------------------------year

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

total

RI PT

--------------------------------------------------------------------------------------------------------------------------------------------------------

the karyotype was t(9;18)(p12;p11 or q11)

Table A2.2. Suspicion of Down syndrome

M AN U

1

SC

number 632 665 706 633 641 524 497 551 450 582 5,881 clinically relevant 6 14 7 9 7 13 13 8 5 4 86 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------reciprocal transloc. 4 11 4 5 6 12 11 7 5 3 68 Robertsonian transloc. 2 1 3 3 1 1 2 1 0 1 15 pericentric inversion 0 1 0 1 0 0 0 0 0 0 2 1 whole arm transloc. 0 1 0 0 0 0 0 0 0 0 1

EP

TE D

------------------------------------------------------------------------------------------------------------------------------------------------------------------------year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 79 83 52 73 57 65 56 70 49 51 635 abnormal 48 54 29 52 40 31 37 47 29 33 398 % of abnormal 60.3 65.4 55.1 71.8 69.9 48.4 66.7 66.7 59.2 64.7 63.0 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------Abnormal karyotypes total % of total % of abnormal ------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 379 59.69 % 95.23% trisomy-21 in all metaphases 2 mosaic trisomy-21 6 0.94% 1.51% Down syndrome due to Robertsonian translocation 11 1.73% 2.76% 3 other clinically relevant unbalanced karyotype 2 0.31% 0.50% ------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 including one case with an additional imbalance [47,XX,der(16)(pter->p11.2::q22.1->q24.3::q22.1->p11.2::q24.3->qter),+21 ] 2 percentages trisomy-21 cells were, in ascending order: 4%, 38%, 46%, 53%, 72% and 81% 3 46,XX,der(12)t(4;12)(p16.2;p13.32) and 46,XX,inv dup del(5)(:p13.2->p15.33::p15.33->qter)

AC C

Table A2.3. Ambiguous genitalia

------------------------------------------------------------------------------------------------------------------------------------------------------------------------year 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 5 5 3 9 13 3 11 4 53 % abnormal 40 20 0 0 15 0 18 0 13 number of abnormal results 2 1 0 0 2 0 2 0 7 of which 100% 46,XY in phenotypic female 1 0 0 0 2 0 1 0 4 1 45,X[22]/46,X,r(Y)(p11.32q12)[13] 1 0 0 0 0 0 0 0 1 2 45,X[29]/46,XY[3] and 45,X[24]/46,XY[8] 0 0 0 0 0 0 1 0 1 del(15)(q11q13) Prader-Willi syndrome 0 1 0 0 0 0 0 0 1

-----------------------------------------------------------------------------------------------------------------------------------------1 2

the r(Y) carried the SRY gene as shown by FISH findings in left and right gonad, respectively, of same patient

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Table A2.4. Premature Ovarian Failure (POF)

SC

RI PT

------------------------------------------------------------------------------------------------------------------------------------------------------------------------year 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 27 32 31 27 36 32 20 23 228 % abnormal 7.4 9.4 3.2 7.4 2.8 3.1 20 4.3 6.6 number of abnormal results 2 3 1 2 1 1 4 1 15 of which 10%< 45,X <100% 0 0 0 0 0 0 0 0 0 0%< 45,X <10% 1 3 1 2 1 0 2 1 11 100% 45X 0 0 0 0 0 0 2 0 2 del(X)(q26) 0 0 0 0 0 1 0 0 1 del(X)(q27.3) 1 0 0 0 0 0 0 0 1 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Table A2.5. Girl with short stature (-3 SD)

EP

TE D

M AN U

------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 2 3 2011 2012 2013 2014 2015 total year 2008 2009 2010 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 80 116 83 88 74 97 77 78 693 % abnormal 5.0 6.9 6.0 1.1 2.7 1.0 1.3 2.6 3.5 number of abnormal results 4 8 5 1 2 1 1 2 24 of which 100% 45,X 0 3 1 0 0 0 0 1 5 10%< 45,X <100% 2 3 2 0 0 0 0 0 7 0%< 45,X <10% 1 1 0 0 0 1 0 1 4 45,X/46,X,i(X)(q10) 1 0 1 0 2 0 0 0 4 45,X[66]/46,X,r(X)(p22.11q21.2)[65] 0 0 0 1 0 0 0 0 1 46,X,der(X)t(X:Y)(p22.3;q12) 0 1 1 0 0 0 0 0 2 46,X,del(X)(p21.1) 0 0 0 0 0 0 1 0 1 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 2010 incidental finding 46,XX,t(4;8)(q12;p21.3) 2 2011 incidental finding 46,XX,dup(16)(q11.2q12.1)pat 3 2015 incidental finding 46,XX,der(7)t(6;7)(p25.3;p22.3)mat

AC C

Table A2.6. Girl with suspicion of Turner syndrome

-----------------------------------------------------------------------------------------------------------------------------------------1

year 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 50 32 62 29 31 40 32 34 310 % abnormal 4 15.6 3.2 3.4 3.2 12.5 15.6 0 6.5 number of abnormal results 1 5 2 1 1 5 5 0 20 of which 100% 45,X 1 2 1 1 0 3 3 0 11 10%< 45,X <100% 0 0 0 0 0 0 1 0 1 0%< 45,X <10% 0 0 0 0 1 0 1 0 2 45,X/46,X,i(X)(q10) 0 3 1 0 0 1 0 0 5 45,X[123]/46,X,r(X)(p11.22q24)[9] 0 0 0 0 0 1 0 0 1 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 2015 incidental finding: balanced Robertsonian translocation

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Table A2.7. Boy or male with suspicion of Klinefelter syndrome

SC

RI PT

------------------------------------------------------------------------------------------------------------------------------------------------------------------------year 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 40 24 34 32 31 28 34 30 253 % abnormal 25.0 12.5 11.7 25.0 9.7 21.4 17.6 20.0 18.2 number of abnormal results 10 3 4 8 3 5 6 6 45 of which 100% 47,XXY 7 3 3 8 1 5 4 5 36 20%< 47,XXY <100% 0 0 0 0 0 0 0 0 0 0%< 47,XXY < 20% 2 0 0 0 0 0 0 0 2 idic(Y) or i(Yp) or del(Y) 1 0 0 0 0 0 0 0 1 100% 47,XYY 0 0 0 0 1 0 0 0 1 100% 48,XXXY 0 0 0 0 0 0 0 0 0 100% 48,XXYY 0 0 0 0 0 0 0 1 1 100% 49,XXXXY 0 0 0 0 0 0 1 0 1 47,XXY/48,XXXY/49,XXXXY/46,XY* 0 0 0 0 1 0 1 0 2 46,X,der(X)t(X;Y)(p22.33;p11.2).ish (SRY+) 0 0 1 0 0 0 0 0 1 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

M AN U

*the karyotypes were 47,XXY[102]/48,XXXY[6]/46,XX[3]/49,XXXXY[3]/46,XY[19] and 47,XXY[113]/48,XXXY[2]/49,XXXXY[2]/46,XY[14]

Table A2.8. Male with infertility/subfertility electable for ICSI

-----------------------------------------------------------------------------------------------------------------------------

AC C

EP

TE D

year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 total -----------------------------------------------------------------------------------------------------------------------------------------------------------------------number 108 88 88 131 159 121 101 101 84 66 1047 % abnormal 2.7 1.1 2.3 7.6 5.7 7.4 11.9 5.0 4.8 4.5 5.4 number abnormal 3 1 2 10 9 9 11 5 4 3 57 of which 100% 47,XXY 1 0 0 2 2 1 3 3 1 2 15 20% < 47,XXY <100% 0 0 0 1 0 0 0 0 0 0 1 0% < 47,XXY <20% 0 1 0 2 3 4 3 0 1 1 15 0% < 45,X <10% 1 0 0 1 0 0 0 0 1 0 3 idic(Y) i(Yp) del(Y) 0 0 0 1 1 1 0 1 0 0 4 Roberts. transl. 0 0 1 2 3 1 2 0 0 0 9 rec. balanced transl. 1 0 0 1 0 1 3 0 0 0 6 balanced inversion 0 0 1 0 0 1 0 1 0 0 3 47,XY,+mar 0 0 0 0 0 0 0 0 1 0 1 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Table A2.9. Carriers of a chromosomal rearrangement ------------------------------------------------------------------------------------------------------------------------------------------------------------------------1 year 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 total ------------------------------------------------------------------------------------------------------------------------------------------------------------------------number 200 227 176 176 249 213 180 185 156 143 1905 number abnormal 31 31 22 20 33 30 35 20 33 27 282 % abnormal 15.5 13.7 12.5 11.4 13.3 14.1 19.4 10.8 21.2 18.8 14.8 of which Roberts. transl. 4 1 4 2 3 6 1 6 2 4 33 rec. bal. transl. 19 17 9 8 10 14 18 9 17 12 133 bal. inversion 3 4 2 1 2 1 1 1 1 1 17 others 5 9 7 9 18 9 15 4 13 10 99 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------

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