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Detection of De Novo Copy Number Deletions from Targeted Sequencing of Trios

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Detection of De Novo Copy Number Deletions from Targeted Sequencing of Trios bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Detection of de novo copy number deletions from targeted sequencing of trios Jack M. Fu1, Elizabeth J. Leslie2, Alan F. Scott3, Jeffrey C. Murray4, Mary L. Marazita5, Terri H. Beaty6, Robert B. Scharpf 7, Ingo Ruczinski1∗ January 23, 2018 1 Department of Biostatistics, Johns Hopkins University, Baltimore MD. 2 Department of Human Genetics, Emory University, Atlanta GA. 3 Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore MD. 4 Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, IA. 5 School of Dental Medicine, University of Pittsburgh, Pittsburgh PA. 6 Department of Epidemiology, Johns Hopkins University, Baltimore MD. 7 Department of Oncology, Johns Hopkins University, Baltimore MD. ∗To whom correspondence should be addressed: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore MD, 21205, USA. Email [email protected]. bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Abstract 2 De novo copy number deletions have been implicated in many diseases, but there is no formal method 3 to date however that identifies de novo deletions in parent-offspring trios from capture-based sequenc- 4 ing platforms. We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. 5 MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific 6 CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited 7 much better scalability, and is available as open source software at github.com/JMF47/MDTS. 8 KEYWORDS: de novo copy number variants; case-parent trios; targeted sequencing; oral clefts. bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Background 2 Copy number variants (CNVs) are a major contributor of genome variability in humans [1], and 3 frequently underlie the etiology of disease [2, 3, 4, 5, 6]. De novo CNVs, especially de novo deletions, 4 are of interest as they have the potential to play a functional role in the genesis of a disease phenotype 5 [7, 8, 9, 10]. Over the last decade, next generation sequencing (NGS) has become routine and 6 widespread [11, 12], permitting the assessment of CNVs based on hundreds of millions of short reads 7 observed in each sample. The advantages of NGS for CNV assessment compared to single nucleotide 8 polymorphism (SNP) arrays include higher and more uniform coverage, better quantitation yielding 9 more precise estimates of DNA copy number, and higher resolution for break point detection [13, 14]. 10 Computational methods to detect CNVs from NGS short reads can generally be categorized into 11 approaches based on discordant read mapping, split read mapping, read depth, de novo assembly, or a 12 combination of these approaches [15]. Due to the differences in the attempted capture, methodologies 13 for whole genome sequencing (WGS), whole exome sequencing (WES), and targeted sequencing (TS) 14 platforms differ substantially, with TS and WES platforms primarily relying on read depth [16]. 15 A large number of methods for detecting CNVs in independent samples are available for all types 16 of NGS data [17, 18, 19, 20, 21, 22, 23, 24, 25, 26]. However, there is no method to date that 17 identifies de novo CNVs in parent-offspring trios from capture-based TS and WES platforms. For 18 WGS platforms, the software TrioCNV jointly calls CNVs in parent-offspring trios [27] using a hidden 19 Markov model (HMM) with 125 possible underlying states to segment the sequencing data (5 possible 20 underlying states per sample: two-copy deletion, one-copy deletion, normal, one-copy duplication, 21 multiple-copy duplication). Its performance in TS or WES platforms however is not well described. 22 In CANOES, also HMM based, inference for de novo copy number deletions in TS and WES data is 23 obtained post-hoc by comparing single-sample derived CNV calls. For each sample of the trio, the 1 bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 observed read counts are modeled using negative binomial distributions, and the respective variances 2 are estimated using a regression-based approach based on selected reference samples [28]. However, 3 such approaches do not fully leverage the Mendelian relationship between parents and offspring to 4 delineate de novo CNVs. The loss of statistical power for delineating de novo CNVs by post-hoc 5 methods has been demonstrated previously in CNV calls from SNP array data [29, 30]. 6 The motivating example in this manuscript is a targeted re-sequencing study we recently carried out 7 in 1,409 Asian and European case-parent trios ascertained by non-syndromic orofacial cleft probands, 8 targeting 13 regions previously implicated in candidate genes and genome-wide association studies 9 (GWASs) [31]. The study successfully confirmed 48 de novo nucleotide mutations, and provided 10 strong evidence for several specific alleles as contributory risk alleles for non-syndromic clefting in 11 humans. Choosing two of these nucleotide de novo variants for functional assays, we showed one 12 mutation in PAX7 disrupted the DNA binding of the encoded transcription factor, while the other 13 mutation disrupted the activity of a neural crest enhancer downstream of FGFR2 [31]. However, 14 for the majority of trios, we were not able to identify a genetic cause underlying the proband's oral 15 cleft. Since de novo deletions have previously been shown to underlie oral cleft risk [32, 33, 34], 16 we speculated that in addition to de novo nucleotide variants, de novo deletions in the 13 targeted 17 regions also contribute to clefting for some of our trio's probands. 18 In this manuscript, we present a novel method to delineate de novo deletions from TS of trios. We 19 propose a novel capture-based definition of targets (using average read depth as the defining metric 20 for bins underlying the algorithm, instead of using a uniform number of base pairs), normalize copy 21 number counts using the entire study population, and utilize a "minimum distance" statistic based 22 on normalized read count summaries, aiming to further reduce shared sources of technical variation 23 between offspring and parents within a trio. We characterize the sensitivity, specificity, and PPV 24 of MDTS on simulated data to benchmark its performance relative to the closest existing methods 2 bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 TrioCNV [27] and CANOES [28]. We show that properly addressing the capture in TS data is 2 critical, and thus, methods specifically developed for WGS data (e.g., TrioCNV) do not perform 3 well for TS data. Compared to CNV callers designed for capture based sequencing data that do 4 not exploit the family design (e.g., CANOES), MDTS has similar sensitivity but a much lower false 5 positive rate, resulting in a much higher PPV. In the analysis of the 6.7Mb TS oral cleft data, which 6 identified one de novo deletion in the gene TRAF3IP3 (a suspected regulator of IRF6), MDTS also 7 exhibited much better scalability. 8 Results 9 The MDTS algorithm 10 Our novel MDTS introduces two novel algorithmic aspects. First, MDTS employs bins of varying 11 sizes based on read depth (Figure 1, A{D) as compared to the common standard of using uniform, 12 non-overlapping bins defined by the number of nucleotide base pairs (default in CANOES: 200bp 13 capture based data; default in TrioCNV: tiled, non-overlapping 200bp bins). Second, MDTS fully 14 exploits the trio design to infer de novo deletions (Figure 1, E{G), as compared to processing the 15 trio samples separately and carrying out post-hoc inference. To demonstrate that both of these 16 algorithmic features are important in the delineation of de novo deletions, and to quantify their 17 relative contributions to sensitivity, specificity, and PPV, we compare the default implementations 18 of MDTS and CANOES in the following section, plus MDTS based on the uniform, non-overlapping 19 200bp "probe-based" bins (MDTS:p) and CANOES based on the non-uniformly sized "MDTS bins" 20 (CANOES:b). 21 [ FIGURE 1 ABOUT HERE ] 3 bioRxiv preprint doi: https://doi.org/10.1101/252833; this version posted January 24, 2018.
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