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Detection of structural mosaicism from targeted and whole-genome sequencing data

Structural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structural mosaic abnormalities using deviations in allele fraction and read coverage from next-generation sequencing data. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) simulations were used to calculate detection performance across a range of mosaic event sizes, types, clonalities, and sequencing depths. The tool was applied to 4911 patients with undiagnosed developmental disorders, and 11 events among nine patients were detected. For eight of these 11 events, mosaicism was observed in saliva but not blood, suggesting that assaying blood alone would miss a large fraction, possibly >50%, of mosaic diagnostic chromosomal rearrangements.


2
Genetic mutations that arise post--zygotically lead to genetic heterogeneity in an 3 organism, a phenomenon called mosaicism. The detection of mosaic mutations that 4 are small (single--base or indel) is still a great technical challenge but multi--megabase 5 ("structural") mosaic rearrangements are now routinely detected using cytogenetics 6 and microarray technology (Miller et al. 2010;Biesecker and Spinner 2013). Recent 7 single nucleotide polymorphism (SNP) microarray--based studies have demonstrated 8 that mosaic structural abnormalities are implicated in developmental disorders 9 (Conlin et al. 2010;King et al. 2015), increase in incidence with age (Forsberg et al. 10 2012), and predispose to hematological malignancies in adults (Jacobs et al. 2012;11 Laurie et al. 2012).

13
Modern SNP microarray technology is well suited for detecting mosaicism because 14 probe density is high (often above 1 million sites per genome) and probes generate 15 allele ratio data with high signal to noise ratio. SNP microarray platforms assess two 16 metrics useful for mosaicism detection: 1) b allele frequency (BAF): the fraction of 17 the alleles at a locus representing the less--common allele and 2) log R ratio (LRR): a 18 measure of copy--number, based on the log ratio of signal intensity compared to a 19 reference. These metrics are affected differently depending on the nature of the 20 structural abnormality: whereas copy--neutral (loss of heterozygosity; LOH) 21 mosaicism results in a deviation of BAF alone, copy--number (gain or loss) mosaicism 22 additionally alters the LRR. Absolute deviation from genotype--expected BAF (e.g. 0.5 23 for AB genotype), called B--deviation (B dev ), occurs in mosaic regions when the locus 24 has a mixture of genotypes from wild type and mosaic tissue. Several software tools 25 (Partek® Genomics Suite, Illumina® cnvPartition, BAFsegmentation (Staaf et al. 26 2008), and Mosaic Alteration Detection (MAD) (González et al. 2011)) harness this 27 deviation as a mosaic signal. MAD is open source and has been recently used in 28 several large SNP--based mosaicism projects (Forsberg et al. 2012;Jacobs et al. 2012;29 Forsberg et al. 2014); it identifies mosaic segments using aberrations in B dev and then 30 labels aberrant segments as copy--loss, copy--gain, or copy--neutral events based on 31 the alteration of the LRR from baseline, a deviation referred to here as copy--32 deviation, or C dev . 33 34 Developmental disorders (DD) are often caused by rare, small (SNV and indel) 35 mutations, genetic variation which is not easily captured using microarray (King et al. 36 2014). Therefore, to achieve a more comprehensive assessment of pathogenic 37 mutations, rare disease studies rely heavily on targeted sequencing of the protein--38 coding regions ('exons') of the genome, an approach called whole--exome sequencing 39 (WES) (Koboldt et al. 2013). Indeed, sequencing of the whole genome (WGS) offers 40 several advantages compared to WES, including greater breadth of the genome and 41 more consistent coverage of exons (Meynert et al. 2014). However, WGS is not 42 currently as widely used as WES for rare disease studies due to higher costs, so this 43 work focuses primarily on exome--sequencing data.

45
In addition to small--scale variation, forms of large--scale 'structural variation ', 46 including copy--number (Lee et al. 2007) and copy--neutral variation (uniparental 47 disomy (UPD)) (Yamazawa et al. 2010), are also important causes of DD. CNV burden 1 analysis of nearly 16,000 children with DD (Cooper et al. 2011) demonstrated that 2 nearly all CNVs greater than 2 Mb are likely pathogenic (odds ratios for CNVs of 1.5 3 Mb and 3 Mb were 20 and 50, respectively), and that deletion events are more often 4 penetrant than duplication events. UPD events are only present in about 1 in 3,500 5 healthy individuals (Robinson 2000), but are enriched in children with DD (King et al. 6 2014), and may result in highly penetrant imprinting disorders, recessive diseases, or 7 may be associated with chromosomal mosaicism (Eggermann et al. 2015).  clonality mosaicism is difficult to observe in karyotyping, as inspection of at least 10 9 cells is required to exclude 26% mosaicism with 95% confidence (Hook 1977), and is 10 also difficult to observe in microarray analysis, as the detection sensitivity of mosaic 11 duplications by SNP microarray with about 1 million probes for events of at least 2 12 Mb in size is limited to events of at least 20% clonality (González et al. 2011). The 13 median average clonality in recent SNP--based studies of DD for mosaic aneuploidy 14 was 40% (Conlin et al. 2010), and for mosaic structural variation (2 Mb and greater), 15 was 44% (King et al. 2015). Among children investigated with clinical diagnostic 16 testing, the frequency of autosomal mosaic copy--neutral events was 0.24% (12 in 17 5,000) (Bruno et al. 2011) and the frequency of autosomal mosaic copy--number 18 events was 0.35% (36 in 10,362) (Pham et al. 2014). Combining these frequencies 19 yields a combined frequency among cases of 0.59% of mosaic structural variation. 20 21 The detection of large--scale mutations from WES data is challenging because input 22 data are derived using sparse sampling of the genome, as targeted regions typically 23 cover only about 2% of the genome (Meynert et al. 2014), and sequence read depth 24 at exons is biased by enrichment efficiency and other factors (Plagnol et al. 2012).

25
Despite these limitations, exome--based software tools have been successfully 26 engineered to detect large--scale constitutive mutations, including copy--number 27 variation (Magi et al. 2013;Sathirapongsasuti et al. 2011;Krumm et al. 2012;28 Backenroth et al. 2014;Fromer et al. 2012) and copy--neutral variation (bcftools roh 29 (Narasimhan et al. 2016) and UPDio ). These tools are relatively 30 insensitive to mosaic abnormalities, however, because they typically rely on single 31 metrics, such as copy--number change (rather than copy--number and allele--fraction), 32 or on genotype, which is not well assessed in mosaic state. Specialized methods have 33 been developed for the analysis of cancer exomes where tumor and normal tissue 34 can be isolated (Lonigro et al. 2011;Amarasinghe et al. 2014) or, in the context of a 35 parent--fetus trio, for fetal DNA in maternal plasma (Rampášek et al. 2014). However, 36 a method to detect copy--number and copy--neutral mosaicism from an individual's 37 exome (or genome) is lacking, but if available, could further extend the capacity of 38 sequence--based analyses.

40
We developed MrMosaic, a method that detects structural mosaicism using joint 41 analysis of B dev and C dev in targeted or whole--genome sequencing data ( Figure 1). We 42 used simulations to demonstrate the superior performance of MrMosaic compared 43 to the MAD algorithm. We also applied MrMosaic to analyze WES data from 4,911 44 children with developmental disorders and identified 11 structural mosaic events in 45 9 individuals, 6 of whom exhibited tissue--specific mosaicism. 46 47 1 2 We developed a new computational method, MrMosaic, to detect structural mosaic 16 abnormalities from high--throughput sequence data (Methods). In summary, this 17 method identifies chromosomal segments with elevated deviations in allelic 18 proportion and copy number, relative to randomly selected sites on other 19 chromosomes from the same data ( Figure 1). Initially, measures of deviation of 1 allelic proportion (B dev ) and copy number (C dev ) are computed from the WES/WGS 2 data at well--covered known polymorphic SNVs. Whereas B dev is only assessed at 3 heterozygous sites, C dev integrates information from flanking non--heterozygous sites 4 to reduce noise. The statistical significance of the observed B dev and C dev are assessed 5 separately, using non--parametric testing, and the resultant p values are 6 subsequently combined and then segmented using the GADA algorithm (Pique--Regi 7 et al. 2008). We devised a confidence score, the Mscore, to curate putative 8 detections of mosaic segments, by integrating metrics that discriminate between 9 true positive and false positive mosaic detections (Methods). 10 11 Simulations 12 13 We performed simulations (Methods) to explore the performance of MrMosaic for 14 three different classes of structural mosaicism: gains, losses and LOH, in several 15 contexts. The variation in performance across mosaicism of different sizes, clonalities 16 and sequencing coverage is summarised in Figure 2, for both WES and WGS data.

31
Across all measured categories, mosaic duplications were more difficult to identify 32 than deletion or LOH events, especially at lower (25%) clonality (Supplementary 33 Figure  1). We suspected that the most likely explanation for this lower sensitivity is 34 that duplications result in the smallest deviation of B dev , compared with deletion and 35 LOH events (Supplementary Figure  2) and that the C dev signal is masked by sampling 36 noise at low clonality. To further explore the effect of including C dev in addition to 37 B dev , we investigated the performance of MrMosaic using B dev alone compared with 1 joint analysis of B dev and C dev . This analysis showed substantially improved detection 2 of copy--number events above lower clonality, while only a marginally decreased 3 performance of LOH detection (Supplementary Figure 3), consistent with the 4 intuition that C dev yields a valuable net signal when clonality is above the C dev noise 5 floor. 6 7 Simulation performance increased with larger event size ( Figure 2A). WES simulation 8 analysis demonstrated high area under the precision--recall curve (AUC) for all events 9 at least 10 Mb in size and at least 50% in clonality; and, for deletion and loss of 10 heterozygosity (LOH) events at least 5 Mb in size. MrMosaic performed favourably 11 compared to MAD in all measured categories. Results for WGS simulations 12 demonstrated an AUC of about 0.9 for 100 kb LOH and loss events, and greater than 13 0.95 for all megabase--size events. Larger events were assayed by more positions, 14 and whole--genome simulations interrogated nearly 50--fold more sites than exome 15 data (Supplementary Table 1).

17
Detection performance in simulations increased between 25% and 75% clonality 18 ( Figure 2B). The WES and WGS clonality performance results were measured at 5 Mb 19 and 100 kb sizes, respectively, as events at these sizes were most sensitive to 20 changes in clonality (Supplementary Figs. 4 and 5). Previous studies of children with 21 DD have reported a median mosaicism of approximately 40% mosaicism and 22 detection performance is strong for detecting mosaicism at this clonality at the 23 studied sizes. As clonality increases, the mosaicism is present in a greater proportion 24 of cells, resulting in a greater signal of detection.

26
Simulation performance increases with respect to sequencing coverage ( Figure  2C).

27
The WES and WGS performance with respect to sequencing coverage were assessed 28 for events of 50% clonality, using 5 Mb events for the WES simulations, and 100 kb 29 events for the WGS simulations. WES simulations demonstrated a marginal 30 improvement of detection performance at higher coverage, which was notable for 31 mid--clonality gains (Supplementary Figure  4). Previous work has suggested that 75x 32 average coverage in WES data is sufficient for constitutive copy--number analysis 33 33 and these coverage simulations demonstrated that this exome coverage is also 34 sufficient for the detection of mosaic structural abnormalities. In the WGS results, 35 AUC rose dramatically between 15x and 20x for LOH and loss events and between 36 25x and 30x for gains. AUC was above about 0.9 for LOH and loss events at 30x 37 depth, a standard sequencing depth used in WGS disease studies. Nearly all 38 structural mosaic events of 100 kb and 50% clonality were detected (Supplementary 39 Figure 5) and average coverage of 20x was sufficient to detect nearly all 50% 40 clonality deletion and LOH events at 100 kb, while detection performance of gains 41 improved at 30x and 40x (Supplementary Figure 6). This improved performance as 42 coverage increases results primarily from sampling variance ('noise') decreasing 43 (correlation r = --0.95; Supplementary Figure 7), with an additional minor 44 contribution from more sites (more signals) passing the minimal depth threshold for 45 consideration (Supplementary Table 1).

47
Detections in 4911 case exomes 1 2 We generated WES data for 4,911 children with undiagnosed developmental 3 disorders. DNA was collected from either blood (n=1652), saliva (n=3246) or both 4 (n=13), and sequenced to a median average coverage of 90X. Analysis for structural 5 mosaicism identified 11 mosaic abnormalities among 9 individuals, a frequency of 6 0.18%. The detections consisted of five losses (median size: 13 Mb, median clonality: 7 46%), four gains (median size: 25 Mb, median clonality: 55%), and two LOHs (median 8 size: 50 Mb, median clonality: 26%) ( Figure 3, Table 1, Supplementary Figs. 8--18). 9 10 11 identified 10 events (King et al. 2015), while exome analysis yielded 8 events. Of the 19 two events not detected by exome but detected by SNP microarray, one of the 20 missed events was a 4 Mb duplication below 25% clonality. The other missed event 21 was an LOH event with low sequencing depth (33x, one of the lowest of our study --22 Supplementary Figure  19); low depth results in higher sampling variance and lower 23 statistical significance of deviations in allelic proportion and copy number 24 (Supplementary Figure  7). Given the high clonality (about 75%) of this event, it may 25 have been detected using constitutive (genotype--based) UPD analysis (although, as 26 paternal data were not available for this sample, it was not analysed by our trio--27 based UPD detection pipeline ). 28 29 Table 1: Detections by exome and validation by SNP microarray: The 11 mosaic abnormalities detected in the 9 samples with exome data were validated using SNP microarray chips. All exome detections were validated in at least one tissue. In the majority of cases (8 of 11), the mutation was detected in only one of two assayed tissues, and in all such cases, the mutation was detected in saliva but not in blood. Clonality was calculated from Bdev using Equation 2 (see Supplementary  Table  5) and ranged from 17% to 68%. This calculation is based on the assumption that the mosaic event is an alteration of a single allele. However, this calculated clonality is an overestimate for one of the events which was found (by previous FISH analysis 4 ) to be a mosaic tetrasomy, and two others were are suspected to also be rearrangements of multiple alleles (another gain of chromosome 12p and one gain of chromosome 18p, thought to reflect mosaic tetrasomy 18). *located in peri--centromeric region and detected during post hoc analysis.

Exome
Validation of the 11 mosaic abnormalities using SNP microarrays on DNA derived from both 1 blood and saliva successfully detected all abnormalities in at least one tissue (Table 1). 2 Notably, six of the seven mosaic copy--number mutations detected by MrMosaic in exome 3 data had been undetected by both clinical and high--resolution aCGH investigation of the 4 same tissue, despite most events being at least 5 Mb in size and exhibiting 50% clonality 5 (Supplementary Table 2). Examination of the raw aCGH data in one case (Supplementary 6 Figure  17) showed that only small fragments of one of the events were detected but these 7 called segments were individually much smaller than the actual event. 8 9 Detection of the mosaic events was largely dependent on the assayed tissue. Out of the 11 10 mosaic events, 3 were detected in blood and in saliva samples while the remaining eight 11 were only observed in saliva ( overlapping independent LOH events, as reported recently (Choate et al. 2015).

19
Nevertheless, despite generation and analysis of high--depth (~400x) WES data for this 20 sample, and the identification of several strong candidate genes, including CEP57 (the cause 21 of mosaic aneuploidy syndrome (Snape et al. 2011)) in the reversion--localised region, no 22 plausibly pathogenic rare (below 1% minor allele frequency) coding sequence variants were 23 identified (Supplementary Table 4).

25
We assessed the pathogenicity of the events detected in these nine children based on 26 overlap with known genomic disorders and diseases of imprinting (Supplementary 27 Material). The mosaic events identified in seven of nine children were considered definitely 28 pathogenic on the basis of being multi--megabase CNVs that overlap known genomic--29 disorder regions. The reversion mosaic event was considered indicative of a likely 30 pathogenic mutation as the presence of multiple overlapping mosaic clones suggests strong 31 and on--going negative selection against a deleterious allele. One LOH event was of 32 uncertain pathogenicity as no rare loss--of--function or functional variants were detected 33 (Supplementary Table 4).

35
Empirical evaluation of detection of mosaicism from WGS data 36 37 We selected one sample with three mosaic abnormalities detected on a single chromosome 38 to demonstrate MrMosaic performance on whole--genome sequence data and to investigate 39 the structure of the mosaic rearrangement. MrMosaic easily detected these multi--40 megabase mosaic events, found with Mscores of 36, 117, and 32. The presence of three 41 mosaic events of similar clonality on the same chromosome is suggestive of a complex 42 chromosomal rearrangement. Analysis of the WGS read pair data using Breakdancer (Chen 43 et al. 2009) identified read--pairs mapping across the centromere and evidence of a 44 breakpoint spanning from the q--arm deletion to the centromere. Ring chromosomes are 45 associated with bi--terminal deletions (Guilherme et al. 2011) and inverted duplications 46 (Knijnenburg et al. 2007) and we suspected that the underlying abnormality in this child is a 47 ring chromosome, although we were unable to access the cellular material required to 1 generate the cytogenetic data to prove this hypothesis (Supplementary Figure 21). 2 3

2
Structural mosaic abnormalities are multi--megabase, post--zygotic mutations that have 3 previously been associated with developmental disorders (Conlin et al. 2010;King et al. 4 2015). This work introduces a novel method to detect these mutations from next generation 5 sequencing data. 6 7 In an extensive simulation study we show adequate power to detect abnormalities in WES 8 and WGS data across a large, clinically relevant range of size and clonality in different types 9 of mosaic structural variation. We also compare our method to the popular array--based 10 mosaic detection method, MAD, and show a substantial boost in performance, which 11 derives primarily from the joint analysis of allelic proportion and copy--number deviations.

12
Simulation results suggested that exome sequencing data can be used to identify many of 13 the known clinical mosaic duplications involving chromosome--arm events, such as 12p and 14 18p mosaic tetrasomy as MrMosaic easily detected events of this size.

16
We used MrMosaic to uncover pathogenic structural mosaicism in a large exome study of 17 children with undiagnosed developmental disorders. Applying our method to the exome 18 data of 4,911 enrolled children, we identified nine individuals with structural mosaicism; the 19 majority of these mutations were considered pathogenic. In this WES--based analysis we 20 recovered 8 of the 10 abnormalities previously detected in a subset of 1,226 samples 21 previously analysed with SNP genotyping chip data suggesting that exome--analysis alone is 22 sensitive to detecting large--scale mosaicism. One of the missed abnormalities was likely 23 undetected because the exome data were of low depth, which increases the variance of 24 measured B dev and C dev . Most of the detected mosaic copy number abnormalities had 25 escaped detection by previous aCGH analysis. This demonstrates that detection of mosaic 26 events requires assay of tissue containing the abnormality and tailored methods with 27 sufficient sensitivity for mosaicism.

29
The overall frequency of mosaicism detected in this study, 0.18%, is lower and significantly 30 different (p < 10 --4 , binomial test) from the 0.59% structural mosaicism frequency estimated 31 from previous studies. One likely explanation for the discrepancy in these frequencies is 32 ascertainment bias, as some classes of structural mosaicism (e.g. mosaic trisomies) are likely 33 to have been diagnosed by prior diagnostic testing (e.g. karyotype or microarray) and not 34 enrolled into the DDD study. Another component of this discordance may be due to 35 decreased sensitivity, as mosaicism smaller than 2 Mb is challenging to detect by exome and 36 these small events account for ~25% (9/36) of mosaic copy number events described 37 previously (Pham et al. 2014).

39
In one sample we observed a gradient of mosaicism, a phenomenon likely associated with 40 mosaic reversion of a de novo mutation inducing genome instability. Analysis of the mosaic 41 LOH region with high--depth exome data did not identify a strong candidate coding variant 42 and a further WGS--based search for candidate pathogenic de novo mutations is on--going. 43 Whole genome sequencing data were generated for one individual with three mosaic 44 abnormalities on the same chromosome. Analysis of these data recapitulated the mosaic 45 events and analysis of read pair analysis identified a pericentromeric inversion and 46 1 ring chromosome.

3
As expected, whole genome analysis had superior performance compared to exome 4 analysis, which was likely due to a combination of advantages of whole--genome data, 5 including higher density of assayed sites (by nearly 50 fold) and more consistent coverage 6 across sites, compared to exome coverage, which is subject to exome bait hybridisation 7 biases. Compared to whole genome data, the exome data had higher average coverage (75x 8 to 25x) for sites within targeted regions compared to the whole genome data and while 9 simulation results showed increasing performance with higher depth sequence data, this 10 effect was outweighed by the greater density of sites in whole genome data.

12
Although the general performance of the method is adequate in many clinically--relevant 13 cases, some classes of event prove more difficult to detect. For example, low clonality 14 mosaic gains generate the smallest deviation in B dev and C dev compared to other types of 15 events, explaining their comparatively poor detection sensitivity in simulations, and the 16 failure to detect one mosaic duplication found using SNP data but not in exome data. More 17 lenient detection thresholds may be preferred to increase detection sensitivity if clinical 18 suspicion of mosaic duplication exists. Increasing the clonality of mosaicism by the biopsy of  19 affected tissue, as is performed when pigmentary mosaicism provides evidence of 20 underlying mosaicism (Woods et al. 1994), should also theoretically improve detection. 21 Given the size and clonality of the two missed events and the simulation results from whole 22 genome sequencing, both events would likely have been detected had they been analysed 23 using higher depth WES or WGS, which are likely to become more common in the future.

25
The majority of the mosaic events we observed in saliva--derived DNA were not observed in 26 blood. The samples with these abnormalities were recruited into our study because they 27 remained undiagnosed after assessment by clinical laboratories of blood--derived DNA failed 28 to detect the mosaic abnormalities we detected in saliva. DNA derived from saliva has a 29 mixed origin, mainly lymphocytes (derived from mesoderm) and epithelium (derived from 30 epiderm) (Endler et al. 1999); therefore the events detected in saliva, but not blood, are 31 believed to reflect epithelial mosaicism. There are two possible explanations for the 32 disparity in tissue distribution we observed: first, that the epithelium--derived mutational 33 events occurred late, i.e. after the differentiation of lymphocytes and epithelial cells, or 34 second, that these events occurred early, i.e. prior to the split between lymphocytes and 35 epithelial cells with subsequent removal from blood cell lineages by purifying selection. 36 Several lines of evidence suggest the second explanation is more likely: 1) existing 37 precedent, as the second phenomenon has been directly observed in Pallister--Killian 38 syndrome, where the percentage of abnormal cells decreases with age in blood but not 39 fibroblasts (Conlin et al. 2012), and tissue--limited mosaicism has been observed in mosaic 40 tetrasomies of chromosomes 5p, 8p, 9p and 18p (Choo et al. 2002) ; 2) the clonality of 41 events observed in both blood and saliva is not greater than the clonality of events in only 42 saliva, which would be expected if events seen across tissue arose earlier in development; 3) 43 both observed LOH events are shared between tissues but only 1 of 9 CNV events are 44 shared between tissues, perhaps suggesting increased pathogenicity of CNV events 45 compared to copy--neutral events, thus more likely to be negatively selected in blood. Given 46 these considerations underlying the disparity in tissue--type, and the observation that the 47 majority of observed abnormalities were detected in saliva but not blood, it is possible that, 1 compared to the sampling of saliva, the sampling of blood could lead to a substantial loss of 2 power, possibly less than 50% power, to detect pathogenic structural mosaicism, resulting 3 in missed diagnoses. Studying the saliva tissue in these children permitted the identification 4 of their mosaic abnormalities and ended for them and their families, their quest for 5 diagnosis. 6 7 Additional work is required to investigate for which developmental disorders tissue--limited 8 mosaicism is common. Another intriguing question regarding tissue distribution is the 9 relationship between clonality and pathogenicity. While mosaicism limited to a small 10 number of cells is unlikely to cause developmental disorders, it is conceivable that low--level 11 mosaicism present in a vulnerable tissue, such as white matter neurons, may have clinical 12 consequences. More work is needed to address this question, including more extensive 13 analysis of the tissue distribution of mosaicism, for example, by analysing diverse tissues 14 sampled from all three germ layers, and assays with improved resolution, allowing single or 15 oligo--cell sequencing. The availability of more sensitive detection methods will improve the 16 detection of a larger fraction of events limited to a single tissue. 17 18 Next generation sequencing, in the form of exome and genome sequencing, can be 19 harnessed to detect a wide range of mutations, including, as presented here, mosaic 20 structural abnormalities. Given that sequencing costs continue to decline and the 21 multifaceted detection capabilities of exome data, it may be that exome sequencing will 22 supersede microarray technology as a first--line test for developmental disorders. 23 Widespread incorporation of high--depth exome and whole genome sequencing will 24 revolutionise our understanding of the extent of mosaicism in the body and better define 25 the relationship of mosaicism and disease. 26 27

2
MrMosaic 3 4 Implementing mosaic detection requires generating an input file and executing the 5 algorithm; the latter consists of several steps: statistical testing, segmentation, filtering, and 6 results visualisation. 'BAF' is used below as an alias for 'non--reference proportion'. The input 7 data for MrMosaic consist of genomic loci with measured B dev values, C dev values, and 8 genotypes, stored in a tab--delimited file. The loci selected were di--allelic single--nucleotide 9 polymorphic (1%--99% MAFs among European individuals in the UK10K 42 project) autosomal 10 positions. For exome analysis, only loci overlapping targeted regions of the exome design 11 were used. At these loci, B dev and C dev values were calculated as described in the following 12 two paragraphs. 13 14 B dev values were generated using the following method: the identity of the alleles at each 15 locus is extracted using fast_pileup function in the perl module Bio::DB::Sam (Stajich et al. 16 2002), using high--quality reads (removal criteria: below base quality Q10, below mapping 17 quality Q10, improper pairs, soft--or hard--clipped reads) and BAF was calculated as the 18 number of reference bases divided by the total of reference bases and non--reference bases.

19
Heterozygous sites were defined as loci with a BAF between 0.06 and 0.94, inclusive. The 20 B dev is calculated at heterozygous sites as the absolute difference between the BAF and 0.5. 21 Only loci with sufficient read coverage (at least 7 reads) are used for analysis.

23
C dev values were generated using the following method: read depths from each target 24 region was collected, the log 2 ratio for that target region was calculated by comparing its 25 read depth to a reference read depth, where the reference value was defined as the median 26 read depth among the distribution of read depths at that target region from dozens of 27 highly correlated samples. This log 2 ratio was normalised based on several covariates 28 pertaining to each target region (covariates included were: GC--content, hybridisation 29 melting temperature, delta free energy ). Lastly, using the Aberration 30 Detection Algorithm v2 (ADM2) method by Agilent® a final error--weighted value, is 31 produced, which we use as the C dev value.

33
The statistical testing step of the MrMosaic algorithm begins by data smoothing, using a 34 rolling median (width of 5) across heterozygote and homozygous sites, so as to utilize the 35 depth information in homozygous sites to reduce variance. From this point forward, only 36 heterozygote sites are considered, as mosaic abnormalities do not affect B dev of homozygous 37 loci. Statistical testing assesses whether a given locus is significantly deviated from the B dev 38 and C dev means given the null hypothesis of no chromosomal abnormality. At every 39 heterozygote site we compute two Mann Whitney U tests, one for B dev and one for C dev , 40 testing the alternative hypothesis that the distribution of the metric in the neighborhood of 41 the chosen site is greater (has a higher median rank) than the distribution of the 42 background. We use 10,000 randomly selected sites, from all autosomes excluding the 43 current chromosome, as the background population. In order to account for non--uniform 44 spacing of the data points we apply a distance--weighted resampling scheme, to down--45 weight distant points from the chosen site. The tri--cube distance, inspired by Loess 46 smoothing, was chosen as a decay function for the resampling weights and considers data 47 points up to 0.5 Mb upstream and downstream of the given position. An equal number of 1 data points is then sampled around the chosen site and from the background (n=100) and 2 the Mann--Whitney U test is performed. Finally, we combine the p values of the two 3 statistical tests (one for B dev and C dev ) for every position using Fisher's Omnibus method.

5
The segmentation step operates on the combined p value generated above. Segmentation is 6 performed using the GADA algorithm (González et al. 2011), using the parameters values as 7 follows: SBL step: maxit of 1e7; Backward Elimination step: T value of 10 and MinSegLen 8 value of 15. This step generates contiguous segments of putative chromosomal 9 abnormalities. Segments in close proximity (within 1Mb) that show the same signal 10 direction (loss, gain, LOH) are merged to reduce over--segmentation.

12
The filtering step is required to assess which of the segments generated above are likely 13 reflective of true mosaicism. While testing MrMosaic in exome simulation analyses we 14 observed that true--positive detections (those overlapping simulated events) tended to be 15 larger (greater number of probes) and have stronger evidence of deviation (GADA 16 amplification value) than putative segments that did not overlap simulated regions (i.e. 17 false--positive, spurious calls) . We captured these two features 18 in a scoring metric calculated from the cumulative empirical distribution functions for 19 'number of probes' and 'GADA amplification value' of false--positive segments, and assessed 20 the composite probability that a given segment comes from these distributions, such that: 21 Mscore = abs(--log 2 (x) + --log 2 (y)) where x and y refer to these empirical cumulative 22 distribution functions. Thus, the Mscore is a quality--control metric derived by combining the 23 size and signal--strength of detections. We used the Mscore to filter those events least likely 24 to represent false positives. We selected events with an Mscore of 8 or greater for analysis 25 because we observed that this appeared to provide a good balance between sensitivity and 26 specificity (Supplementary Figure 24).

28
The visualisation step generates a detection table and detection plots. The detection table  29 consists of mosaic abnormalities detected and contains the following data: chromosome, 30 start_position, end_position, log2ratio_of_segment, bdev_of_segment, clonality, type, 31 number_of_probes, GADA_amplification, p_val_nprobes, p_val_GADA_amplification, 32 Mscore. Event clonality was calculated by assessing the type of mosaic event based on LRR 33 and converting the b dev value to clonality based on the type of event (Supplementary 34 Table 9 ). The detection plots are png files showing the loci and BAF and C dev data for each 35 chromosome in which a mosaic abnormality is detected, as well as a genome--wide lattice 36 plot using the data for all chromosomes.

38
MrMosaic is primarily written in the R language, available as an open source tool at 39 https://github.com/asifrim/mrmosaic. The algorithm can be used in multi--threaded mode to 40 facilitate whole genome analysis. Analysis of a single whole exome using a single thread was 41 completed in 15 minutes when tested using a single core of an Intel Xeon 2.67Ghz processor 42 and 500 Mb of RAM. Whole genome analysis using 24 cores required 30 Gb of RAM and 7 43 hours. Whole genome analysis can be substantially shortened if the number of sliding 44 windows is reduced or the window size is increased.

46
Simulating Mosaicism 1 2 We devised a series of simulation experiments to assess MrMosaic performance for various 3 events, across type (LOH, gains, losses), clonalities, sequencing depths, platforms (whole--4 exome (WE) and whole--genome (WG)) and to compare performance to the MAD method. 5 We compared performance to a modified version of MAD we adapted to enable more 6 flexible execution in a parallel--computing environment, but identical with respect to 7 statistical methods. 8 9 The simulation method consisted of these steps: (1) loci selection, (2) calculating depth at 10 these loci, (3) parameter space and number of trials, (4) adjusting read depth in simulated 11 regions, (5) calculating final real depth, (6) selecting sites based on minimum depth, (7) 12 calculating relative copy--number, (8) assigning genotypes, (9) calculating the BAF for each 13 site, (10) calculating performance. Steps 1--3 differed between the WES and WGS 14 simulations and are described first below. The remaining steps 4--10 were executed 15 consistently for WES and WGS simulations and are described next.

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For WES simulations, loci selection (1)  For WGS simulations, the loci selection (1) was based on di--allelci single nucleotide 31 polymorphic (1% --99% European MAFs from the 1000 genomes project (Abecasis et al. 32 2012) May--2013 release) autosomal positions. To calculate depth at these loci (2), we 33 calculated a scaling factor for each locus based on the median read depth of the first two 34 median absolute deviations of the distribution of coverage for that site seen across 2,500 35 low--coverage samples in the 1000 genomes project (Abecasis et al. 2012 The remaining simulations steps 4--10 described below were performed consistently for WES 46 and WGS simulations. For each simulation a single mosaic event was introduced into each 47 simulation trial. The adjustment of read depth in simulated regions (4) was performed using 1 a scaling factor based on the type and clonality of the simulated event, , while sites not 2 overlapping copy--number simulated events would not undergo this scaling step 3 (Supplementary Table 4). To calculate the final simulated read depth (5) for each site 4 ( ! ), we sampled from a Poisson distribution with ! equal to the scaled read depth. Only 5 positions with a final read depth (6) of at least 7 were included for analysis. Relative copy--6 number (7) was defined as log 2 of the ratio of the final read depth to the baseline read 7 depth. 8 9 The assignment of genotypes (8) (AA, AB, or BB) at each position i was randomly determined 10 based on the site's minor allele frequency, which was used in a multinomial function with 11 probabilities corresponding to Hardy Weinberg--assumed genotype proportions (p 2 , 2pq, q 2 ). 12 To calculating the BAF for each heterozygote at site i (9), we adjusted the expected 13 heterozygote proportion of 0.5 with respect to the chosen event type and clonality, and 14 sampling from a binomial distribution given this adjusted proportion and the simulated read 15 depth at i. BAFs for homozygote reference (AA) and non--reference (BB) sites were chosen 16 by sampling from a binomial distribution with p=0.01 or p=0.99 respectively and the 17 simulated read depth at i.

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MrMosaic and MAD were applied on the simulated WES and WGS samples generated by the 20 above procedure and performance was measured using precision--recall metrics (10). A 21 'success' in a trial was considered a detection overlapping the simulated mosaic event.

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Precision was calculated as the number of successes divided by the number of detections.

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Recall was defined as the proportion of trials with a success.

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Description of Samples & Sequencing 26 The samples used in this analysis derived from the Deciphering Developmental Disorders 27 study, a proband two--parent trio--based investigation of children with undiagnosed 28 developmental disorders from the UK and Ireland (King et al. 2015;Firth and Wright 2011;29 Wright et al. 2014;Fitzgerald et al. 2014). DNA was extracted from blood and saliva and was 30 processed at the Wellcome Trust Sanger Institute by array CGH and exome sequencing. 31 There were 4,926 DNA samples analysed in this study from 4,911 children, as some children 32 were analysed using both blood and saliva. The majority, 3,260 of 4,926 (66%) of the DNA 33 samples were extracted from saliva.

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DNA was enriched using a Agilent® exome kit, based on the Agilent Sanger Exome V3 or V5 36 backbone and augmented with 5 Mb of additional custom content (Agilent Human All Exon 37 V3+/ V5+, ELID # C0338371). An 'extended target region' workspace was defined by padding 38 the 5' and 3' termini of each target region by 100--bp yielding a total analyzed genome size 39 of approximately 90 Mb. Sequencing was performed using the Illumina® HiSeq 2500 40 platform with a target of at least 50x mean coverage using paired--end sequence reads of 41 75--bp read--length. Measured exome coverage ranged from 14x to 155x with a mean of 69x 42 (Supplementary Figure 24). Alignment to the reference genome GRCh37--hs37d was 43 performed by bwa version 0.5.9 ) and saved in BAM--format files (Li et al. 44 2009). 45 46 1 chip analysis showed mosaicism was present in saliva but absent in blood. These two exome 2 samples and the exome sample with suspected revertant mosaicism were processed 3 separately from the exome experiment described in the previous paragraph. For these three 4 exomes, the Agilent Sanger Exome V5 target kit was used, and sequence depth ranged from 5 387x -- 455x coverage (reads = {465,522,627, 483,098,826, 549,766,632} * 75bp read--length 6 / 90e6 target--region--size). The sample with suspected underlying mosaic reversion had 7 549,224,891 QC--passed & mapped reads, and 57,165,328 duplicates, and therefore had a 8 mapped read coverage of 410x ((549,224,891--57,165,328)) * 75 / 90e6). 9 10 For the sample for which whole genome sequencing data were generated, sequencing was 11 performed using an Illumina® X--Ten sequencing machine. Library fragments of 450--bp 12 insert--size were used and paired--end 151--bp read--length sequence reads were generated. 13 Alignment to the reference genome GRCh37--hs37d was performed by bwa mem 47 version 14 0.7.12. Average coverage was calculated using samtools flagstat as the number of QC--15 passed mapped--reads without duplicates using 151 bp read--lengths in a 3Gb genome: 16 (616,151,282 -124,325,581) * 151 / 3e9 = 24.8x. Rearrangement analysis was carried out 17 using Breakdancer v1.0 (Chen et al. 2009).

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Additional filtering implemented in addition to Mscore quality score 20 21 Some events with very high Mscores appeared to represent real, but constitutive, 22 abnormalities. There were two failure modes we identified: constitutive duplications and 23 homozygosity by descent (HBD). Constitutive duplications genuinely produce strong signals 24 in MrMosaic, but also constitutive deletion and ROH events may produce putative 25 detections if individual probes had mapping artefacts that resulted in spurious signals. We 26 used bcftools roh (manuscript in preparation) to identify and filter HBD regions and flagged 27 as suspicious events with greater than 25% reciprocal overlap with CNVs detected through 28 constitutive copy--number detection. In addition, we observed several recurrent putative 29 detections, especially prevalent in pericentromeric and acrocentric regions that appeared 30 spurious on the basis of inconsistencies between BAF and LRR, and we filtered such 31 systematic errors by filtering putative mosaic events seen in more than 2.5% of samples. 32 33 SNP genotyping chip validation 34 35 Illumina® HumanOmniExpress--24 Beadchips (713,014 markers) were used. Illumina 36 GenomeStudio software was used to generate log R ratio and BAF metrics and Illumina® 37 Gencall software was used to calculate genotypes. Structural mosaic detection was 38 performed using MAD (González et al. 2011). Initial mosaic events were merged if events 39 were within 1 Mb, and were the same type (loss, gain, or LOH) of mosaic event. Results 40 were plotted using custom R code. 41 42

DATA ACCESS 1 2
MrMosaic source code can be found here: 3 https://github.com/asifrim/mrmosaic 4 5 The complete raw exome sequencing data is publicly available on the European Genome--6 phenome Archive (EGA) after Data Access Committee (DAC) approval. The study accession 7 ID is: EGAS00001000775. 8 9 ACKNOWLEDGMENTS 10 11 This study relied on the generous participation of DDD patients and their parents. We are 12 grateful to the DDD informatics and HGI pipeline staff for generating the data, DDD 13 laboratory staff for sample handling, and Sanger genotyping core for running the validation 14 arrays