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Detecting Non-Allelic Homologous Recombination from High-Throughput Sequencing Data Matthew M Parks1, Charles E Lawrence1,2 and Benjamin J Raphael2,3*

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Detecting Non-Allelic Homologous Recombination from High-Throughput Sequencing Data Matthew M Parks1, Charles E Lawrence1,2 and Benjamin J Raphael2,3* Parks et al. Genome Biology (2015) 16:72 DOI 10.1186/s13059-015-0633-1 RESEARCH Open Access Detecting non-allelic homologous recombination from high-throughput sequencing data Matthew M Parks1, Charles E Lawrence1,2 and Benjamin J Raphael2,3* Abstract Non-allelic homologous recombination (NAHR) is a common mechanism for generating genome rearrangements and is implicated in numerous genetic disorders, but its detection in high-throughput sequencing data poses a serious challenge. We present a probabilistic model of NAHR and demonstrate its ability to find NAHR in low-coverage sequencing data from 44 individuals. We identify NAHR-mediated deletions or duplications in 109 of 324 potential NAHR loci in at least one of the individuals. These calls segregate by ancestry, are more common in closely spaced repeats, often result in duplicated genes or pseudogenes, and affect highly studied genes such as GBA and CYP2E1. Background detection and verification[16]. Experimental techniques Non-allelic homologous recombination (NAHR) is a bio- for discovering instances of structural variation, includ- logical mechanism for repairing broken chromosomes, ing array comparative genomic hybridization (aCGH), which results in gross genome rearrangements. A signif- SNP microarrays, and fluorescence in situ hybridiza- icant portion (approximately 10% to 22%) of all genome tion (FISH), are frustrated by repetitive regions and thus rearrangements in humans, also called structural varia- encounter considerable difficulty in detecting NAHR [16]. tions, is thought to be the result of NAHR [1-5]. Under- While validations of NAHR have been done using these standing and detecting NAHR in individuals provide techniques [3,11,17], they are not used on nearly the valuable insight for a wide variety of genomic disorders, same scale as high-throughput sequencing. New long- disease susceptibilities, and cancers [6-14]. read sequencing technologies from Pacific Biosciences Despite its importance and prevalence, NAHR is chal- [18] and Oxford Nanopore [19] are providing a more lenging to detect with either computational or experi- comprehensive view of structural variation in the human mental techniques. The difficulty stems from four crucial genome [20,21], although the high single-nucleotide error properties: (1) NAHR is mediated by highly homolo- rates and costs of these technologies have thus far limited gous repeats, (2) there are millions of repeats across their ability to detect NAHR events across many genomes. the human genome [15], (3) the breakpoints of an High-throughput genome sequencing is a popular, pow- NAHR-mediated rearrangement are always at homolo- erful, and cost-efficient source of data for learning about gous positions of homologous repeats, and (4) NAHR is genomes and inferring the variation between individu- capable of producing inversions, deletions, duplications, als. Typically, structural variations in an individual with and translocations. Detection of NAHR thus requires a respect to the reference are inferred from sequencing careful treatment of repetitive regions from the human data by the mappings of paired-end reads to the refer- genome. Currently, repetitive regions are a major weak- ence genome. There are three main signals used to detect ness of biological and computational techniques for structural variation: discordant paired reads, split reads, and read depth [22]. Whenever the former two signals *Correspondence: [email protected] are used, it is always assumed that only the discordantly 2 Center for Computational Molecular Biology, Brown University, Providence, mapped or split reads indicate a structural variation,while USA 3Department of Computer Science, Brown University, Providence, USA Full list of author information is available at the end of the article © 2015 Parks et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Parks et al. Genome Biology (2015) 16:72 Page 2 of 19 the concordant reads were correctly mapped; this is true To capitalize on this, we developed a Bayesian algorithm of BreakDancer, VariationHunter, GASV, PEMer, Pindel, that probabilistically models NAHR based on the rules HYDRA, CNVer, and GASV-proa [23-31]. But the biology of the mechanism and employs a specifically designed of the NAHR mechanism requires that NAHR break- hidden Markov model alignment algorithm to probabilis- points occur at homologous positions of homologous tically compare reads among repetitive sequences. We repeats, making it highly likely that paired-end reads gen- applied this model to potential NAHR events among low- erated from NAHR breakpoints are mapped concordantly copy repeats (LCRs) contained in a database of segmental to the original repeats, albeit with a small number of mis- duplications [15] and performed a Bayesian statistical matches. Thus, the very biology of NAHR implies that inference on the occurrence of NAHR-mediated rear- NAHR will very often go undetected by algorithms that rangements in human genomes, focusing on deletions and rely on discordant or split reads, that is NAHR will be duplications. Our model contains several key advances in largely undetectable by most existing structural variation the analysis of read data in the context of rearrangements detection algorithms. Indeed, 93% of the NAHR break- due to NAHR, including a principled analysis of read points we find are supported by ≤2 discordant paired-end depth inside repeats and the consideration of all possi- reads, meaning that these breakpoints are systematically ble mapping locations for every read. These features allow ignored by most alignment-based algorithms. Also, many us to detect hitherto unreachable rearrangements in the structural variation algorithms that utilize the read-depth human genome. signal, such as CNVnator and Event-wise Testing [32,33], We analyzed publicly available, low-coverage Illumina are limited in their ability to detect copy number variants paired-end sequencing data for 44 individuals from the in repetitive regions due to mapping quality thresholds 1000 Genomes Project using our model. Due to the repet- and selection of a mapping for reads with multiple possi- itive nature of these regions and the limitations of exper- ble alignments. imental validation technology mentioned above [16], we Directly modeling NAHR offers major advantages over restricted our calls to a reliable subset of 1,043 called generic predictions of structural variants from read data. NAHR events using a separate statistical test. Nearly The ‘rules of NAHR’ [11,34,35] have been studied and all of our calls are novel when compared against sev- are well characterized [6-8,36], and provide a structured eral recent structural variation validation studies. Most of framework for detecting NAHR from short reads that is these called NAHR events are identified in only a subset consistent with the biological mechanism. They deter- of individuals (median of five individuals per locus with mine where NAHR rearrangements may occur, what types a call), and the called NAHR events show dependence of of rearrangements are possible, and the exact location NAHR on ancestry, providing further evidence in support and sequence composition of the breakpoints. This has of the calls. We also assess the impact of NAHR on several major implications for the analysis of sequencing data: highly studied genes, and we draw additional inference on the information provided by the rules of NAHR allows us general characteristics of NAHR. to construct, fully and exactly, hypothetically rearranged This paper is organized as follows. First, we review the genomes from which all reads were theoretically gen- mechanism of NAHR in detail, highlighting characteris- erated concordantly, thus bypassing entirely the notion tics that will be crucial to our mathematical model. Then, of discordant read mappings. Further, the characteris- we apply a probabilistic framework to the mechanism tics of NAHR and repeats indicate a natural way to and sketch the model. We then present the results of our evaluate read depth, freeing our model from relying on model for a set of individuals, and discuss the biological arbitrary bins and sliding windows. Thus, founding our significance of our results. An implementation of the algo- model on the rules of NAHR provides a novel approach rithm described in this study, detect-NAHR, is freely to high-throughput sequencing analysis for structural available at [37]. variation. Data generated from repetitive regions require Mechanism of non-allelic homologous recombination extremely careful analysis, since, by definition, there is Here we briefly review the NAHR mechanism in very little signal to distinguish highly homologous repeats. humans. More detailed reviews can be found in [6,7]. Any computational model must be sufficiently sensitive Allelic homologous recombination (AHR) repairs double- to recognize such subtle differences in signal and, further, stranded breaks (DSBs) in chromosomes by using the accumulate these differences to make inference informed allele on the sister chromatid as a template. This mech- by the entirety
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