The human brain harbors perhaps the most diverse collection of cells among any organ in the body, consisting of neurons and other cells with many different shapes and behaviors. The mechanisms that create this diversity have been a long-standing area of investigation. While neurons can become more diverse through changes in the activity of genes during development and in response to experiences, it has been speculated that genetic differences among neurons may also play a role.

The complete set of genes found in an individual is known as its genome. It is often assumed that each cell in an individual's brain has an identical genome. However, mutations accumulate in cells during the lifetime of an individual such that every brain cell may in fact contain a unique set of genetic mutations. The extent and types of such genetic mutations have only recently become accessible using techniques that can examine the genomes of single-cells. Some of these genetic differences may result from the activity of short sections of DNA called retrotransposons, which can copy themselves and move to a different place in the genome. This can introduce genetic mutations that alter how the cell works.

Multiple studies have shown that retrotransposon-related mutations are present in human brain cells. Indeed, in 2015 a group of researchers suggested that every neuron in two brain regions called the cortex and the hippocampus contains as many as 16 retrotransposon-related mutations on average, which suggests that retrotransposons may play an essential role in the healthy brain. However, these findings contrasted with previous studies that had shown much fewer mutations.

Now, Evrony, Lee et al. have analyzed the data from the 2015 study that led the previous researchers to interpret some artifacts as retrotransposon mutations. Reanalysing the data confirmed that these mutations do indeed occur; however, they are around 50 times less common than had been suggested by the earlier study.

This suggests that retrotranspons are more likely to be occasional sources of rare variation or disease, rather than essential contributors to normal brain activity in humans. Further work is needed to examine the rate of these and other types of mutations in different cell types and brain regions, and at different developmental stages. However, to ensure that these studies are robust and reliable, Evrony, Lee et al. also outline a framework to aid the design and analysis of future studies.

The mechanisms that generate the immense morphological and functional diversity of neurons in the human brain have long been a subject of speculation and controversy. The immune system, with its systematic genomic rearrangements such as V(D)J recombination, and the ordered generation of random somatic mutation coupled with a selection process, have suggested appealing analogies for generating the cellular diversity of the nervous system, and have led to searches for analogous genomic diversity in the brain (Muotri and Gage, 2006). LINE-1 (L1) elements are endogenous retrotransposons that transcribe an RNA copy that is reverse-transcribed into a DNA copy that can then insert into a novel site in the genome, creating mutations capable of disrupting or modifying the expression of genes in which they insert or neighboring genes (Goodier and Kazazian, 2008). Evolutionarily, transposon mobilization is an essential cause of the generation of species diversity (Cordaux and Batzer, 2009), so interest in possible L1 activity during brain development was spurred by the discovery that these elements can mobilize in neuronal progenitor cells (Coufal et al., 2009; Muotri et al., 2005).

The importance of any mutation process, such as retrotransposon mobilization, in generating neuronal diversity is constrained by the rate at which mutation takes place, since if a given type of mutation occurs infrequently, it is unlikely to be a useful generator of diversity. Single-cell sequencing is a powerful technology that has revealed and quantified previously unknown mechanisms of somatic mutation in the human brain, providing a first proof of principle for the systematic measurement of somatic mutation rates in any normal human tissue (Evrony et al., 2012; McConnell et al., 2013; Cai et al., 2014; Evrony et al., 2015; Lodato et al., 2015). Single-cell sequencing can therefore determine the extent to which somatic mutations diversify the genomes of cells in the brain, which is foundational to understanding their potential functional impact in normal brains and possible roles in neuropsychiatric diseases of unknown etiology (Poduri et al., 2013). L1 mobilization has been observed at low rates using indirect genetic techniques such as a transgenic L1 reporter in rodent brain in vivo (Muotri et al., 2005; 2010) and human progenitor cells in vitro (Coufal et al., 2009), while studies profiling human brain bulk DNA suggested much higher rates (Baillie et al., 2011; Bundo et al., 2014; Coufal et al., 2009). Single-cell sequencing has been proposed as the definitive method to resolve these disparate estimates (Erwin et al., 2014; Goodier, 2014).

A recent single-cell sequencing study (Upton et al., 2015) reported high rates of somatic L1 retrotransposition in the hippocampus (13.7 per neuron on average) and cerebral cortex (16.3 per neuron), and suggested that L1 retrotransposition was "ubiquitous". Such a high rate of retrotransposition could present it as a possibly essential event in neurogenesis and would have major implications for brain function. Here we describe experimental artifacts that elevated the study's apparent rate of somatic retrotransposition by >50-fold. Reanalysis of their data while filtering these artifacts generates a consensus that retrotransposition does occur in developing brain but at a much lower rate consistent with prior single-cell studies (Evrony et al., 2012; Evrony et al., 2015), thereby constraining the range of possible functional roles for retrotransposition in the brain. Our discussion of the challenges in single-cell sequencing may provide a useful framework for the design and analysis of single-cell genomics studies.

Here, we have shown that L1 mosaicism is not "ubiquitous” in the hippocampus and that somatic insertion rates in the recent paper by Upton et al. were elevated > 50-fold due to the informatic analysis and a lack of definitive validation.

Read counts of true insertions versus chimeras

To justify not using a read count filter, Upton et al. state that “in single-cell RC-seq libraries, putative chimeras are disproportionately likely to amplify efficiently and accrue high read depth” (Upton et al., 2015). In other words, they are suggesting that their method preferentially amplifies noise (chimeric sequences) instead of signal (true insertions). We could find no precedent or chemical explanation for why PCR or next-generation sequencing would preferentially amplify chimeras, since there are no sequence features distinguishing chimeras from true insertions in small DNA fragments that would cause preferential overamplification of the former in single-cell RC-seq. In fact, prior single-neuron sequencing studies and chimera rates of Illumina libraries and MALBAC show directly that chimeras are not preferentially amplified relative to true genomic sequence fragments and true insertions (Appendix 3; Figure 2—figure supplement 1B–C; Figure 3—figure supplement 1).

Indeed, the use of read counts for mutation analysis is integral to one of the prime purposes of single-cell sequencing, a technology whose development was motivated by two goals: (a) tracking which somatic mutations are present together in the same cells to enable lineage tracing; and (b) achieving higher signal to noise ratios for somatic mutations, i.e. true mutation to false-positive read count ratios. In single-cell sequencing, somatic mutations appear on average at the same signal level as germline heterozygous mutations (i.e. 50% of reads at the locus), while the fraction of false variant reads at a locus (e.g. sequencing errors, library PCR mutations, chimeras) is the same on average regardless of the number of cells sequenced. Accordingly, decreasing the number of cells pooled for sequencing increases the signal to noise ratio of somatic mutations (see Figure 5 for a simplified mathematical framework for single-cell sequencing). Therefore, calling mutations supported by only a single sequencing read is counter to a key feature and objective of single-cell sequencing. Furthermore, although Upton et al. present qPCR experiments as additional evidence for their findings, it is important that the originators of that qPCR method consider single-cell analysis as definitive (Erwin et al., 2014), and qPCR results are affected by target L1 specificity (Appendix 4).

Figure 5

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Finally, we emphasize that the bioinformatic and validation approach led to the inflated somatic insertion rate, but not the RC-seq L1 hybridization capture method itself. Our analysis suggests that RC-seq capture, if used with an appropriate single-cell amplification method, careful signal modeling based on true insertions, and rigorous PCR validation, would likely enable cost-effective, high-throughput retrotransposon profiling comparing favorably with other methods such as L1-IP.

The distribution of TSD sizes of RC-seq bulk hippocampus somatic candidates contains many large TSDs, a finding inconsistent with nearly all prior L1 research. Of the RC-seq bulk somatic candidates detected at both 5' and 3' junctions, 44%, 37%, and 30% of candidates were detected with TSDs ≥ 30, ≥ 40, and ≥ 50 bp in size, respectively. In contrast, prior L1 research has shown TSD sizes < 30 bp for nearly all insertions. The authors point to a prior study of in vitro retrotransposition in HeLa cells that found large TSDs (≥ 50 bp in 20% of insertions) (Gilbert, et al., 2005) and hypothesize the same occurred in neuronal progenitors due to chromatin properties shared with HeLa cells, such as "pervasive euchromatinization". Large TSDs greater than 50 bp have only been found at an appreciable rate in HeLa cells (Gilbert, et al., 2005; Moran, et al., 1996; Symer, et al., 2002). HeLa cells differ physiologically in many respects from normal human cells, and outside of HeLa cells, studies of inherited, de novo, and somatic insertions in every other biological context to our knowledge have found only the canonical TSD size distribution, including:

1. Known human-disease causing L1 insertions (19 insertions; TSD mean: 14 bp; range: 2–20 bp, except 4 without a TSD) (Hancks and Kazazian, 2012).

2. Tumor somatic insertions (1450 insertions; TSD peak: 15 bp; 98% are ≤ 20 bp) (Helman et al., 2014; Tubio et al., 2014).

3. Comprehensive profiling of population-polymorphic insertions in the 1000 genomes project (TSD peak: 15bp ± 7 (SD); nearly all between 5 and 25 bp) (Stewart et al., 2011).

4. Genome-wide analysis of all L1s in the human genome reference (TSD peak: 15 bp; nearly all < 25bp) (Kojima, 2010; Szak et al., 2002).

5. Somatic insertions in neurons: six L1 insertions in rat neuronal progenitors in vitro (Muotri et al., 2005) and the two bona fide human brain somatic insertions we have found (Evrony et al., 2012; Evrony et al., 2015) all had TSD sizes < 20 bp.

How widely and evenly the genome is amplified (breadth and uniformity) are two measures of single-cell genome quality and amplification performance that are integral to the feasibility and success of subsequent somatic mutation analyses (Leung et al., 2015). Each can be assessed at different genomic scales (i.e. resolutions) or sequence elements, for example, in large 500 kb bins, at base-pair resolution, stratified by GC-content, at retrotransposon elements, etc. (Evrony et al., 2015; Zhang et al., 2015). Breadth and uniformity of genome amplification are related and can vary significantly depending on the genomic scale or sequence element evaluated. For instance, if a hypothetical single-cell genome amplification method amplifies only one small 1 kb region every 500 kb across the genome while the remaining genome is unamplified (i.e. extreme non-uniformity), then a low-resolution analysis of breadth of amplification will show 100% genome coverage at 500 kb bins even though at base-pair resolution only 0.2% (1/500) of the genome is amplified. Such a single-cell method would be suitable for analysis of copy number variants larger than 500 kb, but would not be suitable for smaller mutations. Single-cell amplification breadth and uniformity of coverage must therefore be evaluated at genomic size scales and sequence elements relevant to the type(s) of mutation(s) being studied. We profiled the MALBAC-amplified single neurons from Upton, et al. using previously developed single-cell quality metrics (Cai et al., 2014; Evrony et al., 2015; Zhang et al., 2015), and compared them to MDA-amplified single neurons and MALBAC-amplified single cells from prior studies.

Upton, et al. state that in single-neuron RC-seq, as opposed to bulk RC-seq, chimeras are preferentially amplified to higher read counts than true insertions. They further assert the same is true generally of single-neuron sequencing, including prior studies (Evrony et al., 2012; Evrony et al., 2015). However, analysis of Upton, et al.'s single-cell whole-genome sequencing (WGS) data shows directly that the authors' single-cell MALBAC amplification and WGS library preparation methods do not preferentially create and amplify chimeras relative to true genomic fragments. Chimera rates are reflected in the fraction of discordant read pairs (DNA fragments whose sequencing reads from each end of the fragment align distantly in the genome or in wrong orientation to each other) and clipped reads (partially aligning reads, which includes reads with chimera breakpoints occurring within a read rather than between read pairs of a DNA fragment). Discordant and clipped reads in Upton, et al. single cells comprise on average only 0.9 ± 0.5% (SD) and 4.2 ± 1.7% of all reads, respectively. Similar fractions of discordant and clipped reads, 2.3 ± 1.4% and 1.4 ± 0.3%, respectively, were seen in MALBAC single cells from a prior study (Zong et al., 2012) (analysis performed in Evrony et al. (2015)). If it were true, as Upton, et al. assert, that chimeras amplify to higher levels than true genomic fragments, then these rates would have been significantly greater. The low discordant and clipped read rates in Upton, et al. single cells confirm that chimeras are a small minority of their samples' DNA fragments, and that their MALBAC and WGS library preparation methods do not preferentially amplify chimeras more than true genomic fragments to any significant degree.

Extensive quantitative analyses of chimeras in prior single-cell studies show that chimeras occur at significantly lower rates than non-chimeric fragments also in the MDA method more commonly used for single-cell amplification (Evrony et al., 2012; Evrony et al., 2015). Only 0.4% of DNA fragments in MDA-amplified single cells are MDA chimeras, reflecting an MDA chimera rate of ~1.2 per 100 kb of amplified DNA (Evrony et al., 2015). An additional 2-3% of fragments in single-neuron WGS samples are chimeras created during sequencing library preparation, reflected in 1.9 ± 0.5% and 0.8 ± 0.2%, discordant and clipped reads, respectively (Evrony et al., 2015), which could be mitigated by alternative library preparation methods (Quail et al., 2008). Single-neuron WGS (Evrony et al., 2015) has no additional steps in which chimeras could form other than MDA and WGS library preparation. This study further presented MDA chimera spatial distributions that elucidate their mechanisms of formation and aid computational filtering. The above analyses and others (Lasken and Stockwell, 2007) have therefore shown unequivocally that chimeras are a minority of reads in single-cell sequencing methods generally (i.e. the number of bases amplified between chimera events is on average much larger than the size of sequenced DNA fragments).

Additionally, both Upton, et al. and prior single-neuron sequencing studies (Evrony et al., 2012; Evrony et al., 2015) show unequivocally that true L1 insertions appear at higher read counts than chimera calls. In prior studies, the score distributions (reflecting read counts) of gold-standard KNR insertions in single cells have a unimodal distribution at high scores (i.e. at high read counts), clearly separable from the remaining calls concentrated at lower scores (reflecting chimeras) (Evrony et al., 2012, Figure S4B; Evrony et al., 2015, Figure 2B). The KNR read count distributions of both Upton, et al. and these prior studies show the same (Figure 2A; Figure 2—figure supplement 1A–C), contradicting Upton, et al.'s suggestion that chimeras appear at higher read counts than true insertions. Therefore, in these methods and in single-cell sequencing generally, insertion calls supported by single reads would have negligible probability of being true insertions.

Upton, et al. further support their claim that single-neuron sequencing preferentially amplifies chimeras by remarking on the low PCR validation rate among high-scoring final L1-IP candidates of Evrony et al. (2012) (low specificity) as an indication that many true candidates at low scores are being missed (low sensitivity). This confuses specificity with sensitivity: low specificity among candidate mutations does not imply low sensitivity for true mutations. L1-IP's sensitivity is high, in fact significantly higher than RC-seq, as measured using the gold-standard KNR insertion reference (Figure 3A; Figure 3—figure supplement 1) as well as single-copy (heterozygous and hemizygous) KNR and human genome reference insertions, the most stringent performance measure (Evrony et al., 2012; Evrony et al., 2015). L1-IP's specificity performance is due to the very small fraction of chimeras that are at higher scores outnumbering the very small number of true somatic insertions, highlighting the challenge of chimeras in single-cell retrotransposon analysis in the setting of low retrotransposition rates. Notably, the challenge of computational specificity was readily overcome with a definitive 3' PCR validation approach with near-perfect sensitivity and high specificity, followed by full-length validation with perfect specificity (Evrony et al., 2012). We emphasize that as in any signal-noise problem where true events are distributed at higher signal than false events, lowering a threshold to include more true events at lower scores would only decrease specificity by including a relatively greater number of false-positive calls– i.e. the proportion of chimeras among candidates called with a lower threshold can only be greater than the proportion among candidates called with a higher threshold. Additional PCR validation illustrates this: 24 randomly selected L1-IP candidates supported by one read and 24 candidates randomly selected without any read count filter all failed PCR validation (Supplementary file 1, sheet "L1-IP | low-read-count"), illustrating the need for signal modeling based on read counts and other parameters using a gold-standard true mutation reference.

A subsequent single-neuron WGS method was motivated precisely to increase computational specificity by its ability to detect most insertions at both 5’ and 3’ junctions (Evrony et al., 2015). In fact, the four highest scoring candidates found by this method were bona fide somatic insertions, i.e. greatly improved specificity while retaining high sensitivity. For this reason single-cell whole-genome sequencing coupled with full-length validation is the definitive, (albeit a costly) method for the study of somatic retrotransposition. It is also able to profile multiple retrotransposon types (Alu, L1, SVA) as well as other types of somatic mutation (structural variants, single-nucleotide variants) in one experiment. Targeted methods such as L1-IP and RC-seq if used with an appropriate single-cell amplification method such as MDA, careful signal modeling based on true insertions, and rigorous PCR validation, are cost-effective alternatives for high-throughput studies focused on one type of retrotransposon across many cells.

Upton et al. (2015) employ a quantitative PCR (qPCR) method to measure somatic L1 copy number in in the brain. Their qPCR results show relative differences of ~10-30% in somatic L1 copy number among hippocampi of four normal individuals and an individual with Aicardi-Goutieres syndrome (Upton et al., 2015; Figures 4D–E). This qPCR method has also been used in prior studies, with results showing variable increases (between 5% and up to 45%) in somatic L1 copy number in brains of normal human individuals, in particular the hippocampus, as well as brains of schizophrenia patients (Baillie et al., 2011; Bundo et al., 2014; Coufal et al., 2009). However, there are known limitations of the qPCR method discussed by its originators (Erwin et al., 2014; Reilly et al., 2013), namely its use of exogenous LINE-1 DNA to calibrate L1 copy number quantification. Furthermore, as discussed below, the qPCR assay may lack specificity for active L1 elements, which would impact its findings.

Below is an alignment of the qPCR assay (ORF2 #1) primers and TaqMan probe to the L1Hs and L1Pa2-5 consensus sequences from RepeatMasker (asterisks denote bases identical across all sequences) (Appendix 4—figure 1). Note that only the 3rd base from the 5' end of the qPCR forward primer distinguishes L1Hs elements from older L1Pa elements (bold blue). The reverse primer and probe do not distinguish between the different L1 subfamilies' consensus sequences.

Appendix 4—figure 1

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PCR specificity depends significantly more on the 3' ends of primers than their 5' ends, because DNA polymerase can begin replication of the template even when only a 3' portion of the primer is hybridized (Bru et al., 2008; Stadhouders et al., 2010). In other words, DNA polymerase depends on the 5' end of the primer only to the extent that it aids in hybridization and stability of the 3' end. In this case, the 3'-most 20 bp of the forward primer match equally well to the L1Hs and L1Pa consensus sequences. Therefore, in order for the PCR reaction to specifically and preferentially amplify L1Hs and not L1Pa elements, the stringency of the PCR reaction would need to be calibrated to allow amplification only when at least the 3'-most 21bp match to the template while not allowing amplification when only the 3'-most 20bp match to the template (i.e. a single base mismatch stringency for the 3^rd base from the 5' end of the 23 bp forward primer). Such a calibration was not performed by Upton et al. and achieving such a precise PCR stringency/specificity is unlikely using this primer design (Bru et al., 2008). A prior study recognized the need for L1Hs specificity in this assay and sequenced amplicons from the qPCR reaction, finding that most amplicons corresponded to L1Hs rather than L1Pa (Coufal et al., 2009; table S3). However, this result may be because the primers are incorporated into the amplicons during PCR such that the diagnostic base discriminating between L1Hs and L1Pa would be lost and replaced by the primer sequence.

Therefore, the absolute number of non-L1Hs elements amplified by the assay depends on whether the PCR reaction can discriminate templates matching the 3^rd base of the forward primer (i.e. matching the L1Hs-specific nucleotide). If the PCR reaction only amplifies templates matching this diagnostic base, 214 L1 elements are predicted to be amplified per the UCSC In-Silico PCR tool, most of which (88%) are L1Hs and the remainder L1Pa2-4. However, without the specificity provided by the 3^rd base of the forward primer, 5,166 L1 elements are predicted to be amplified from the human genome, i.e. ~24 times as many L1 elements, of which only 527 (10%) are L1Hs, and the remainder are 1136 (22%) L1Pa2, 2047 (40%) L1Pa3, 1203 (23%) L1Pa4, 178 (3%) L1Pa5, and other older L1 elements. Moreover, the ORF2 #1 Taqman probe matches the majority (71%) of these elements with no mismatches and nearly all (95%) with 1 mismatch. As a result, most of the L1 elements amplified and quantified by the qPCR assay may be older inactive L1Pa elements rather than retrotranspositionally active L1Hs elements.

Because qPCR measures relative changes in copy number, targeting of mostly inactive L1Pa elements would in turn cause a decrease in sensitivity for detection of L1Hs copy number changes. Additionally, any purported relative increase in copy number indicated by the assay would mean more dramatic absolute increases in L1 copy number, because relative copy number increases are measured relative to the total number of L1 elements amplified by the assay. For example, without specificity for L1Hs elements, even a modest 5% relative copy number increase in this assay would correspond to ~258 (5% * 5,166) additional L1Pa/L1Hs insertions per cell. And the 30-40% relative copy number differences shown by the assay in prior studies (Baillie et al., 2011; Upton et al., 2015) would reflect differences of ~1550–2070 L1Pa/L1Hs insertions per cell. qPCR estimates of hundreds to thousands of additional L1Pa/L1Hs insertions per cell are biologically unlikely due to the immense mutational burden implied and are inconsistent with even the highest prior estimates of somatic retrotransposition. Furthermore, bulk tissue is profiled by this assay, so the above rate would be revised even further upwards for neurons if retrotransposition were occurring at higher rates in neurons than other cell types.

In light of the above, the qPCR and RC-seq results illustrated in Figures 4D–E of Upton, et al. (2015) are inconsistent. Comparing individuals CTRL-36 and CTRL-55 in Figure 4E shows ~45 and ~15 somatic insertions, respectively, per individual by RC-seq, while the L1 qPCR assay shows a difference of ~30% in L1 copy number between the individuals. However, a 30% relative difference in L1 copy number measured by the qPCR assay would be equivalent to an implausible absolute difference at least one order of magnitude larger than that measured by RC-seq; i.e. >300 and perhaps up to ~1500 somatic insertions depending on the number of L1 elements amplified by qPCR.

There are inevitable random and frequent systematic sources of error inherent to qPCR, including sample preparation, pipetting, and instrumentation (Fernandez-Jimenez et al., 2011; Kitchen et al., 2010; Weaver et al., 2010; Whale et al., 2012). Robust detection of small (< 10%) relative copy number differences is challenging (Weaver et al., 2010). Even with sufficient replicates to eliminate random error, systematic error sources may remain. One systematic error may be differential extraction of normalizer genes’ DNA (alpha-satellite DNA) relative to L1 DNA from tissue samples; similar biases have been observed in a mitochondrial DNA qPCR assay (Guo et al., 2009).

Finally, the large L1 copy number differences suggested by Upton et al.'s qPCR data are unlikely to be explained by non-retrotransposition mediated processes such as structural variation or aneuploidy, as this is inconsistent with recent studies showing that large genome-wide copy number variation and aneuploidy are infrequent enough to account for such large L1 copy number changes, at least in cortical neurons (Cai et al., 2014; Evrony et al., 2012; McConnell et al., 2013). Even if an improved L1 qPCR assay addressing the above issues were designed, it would still be unable to differentiate changes in L1 copy number due to retrotransposition from those not due to retrotransposition (Reilly et al., 2013).

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Author details

1. Gilad D Evrony

   1. Division of Genetics and Genomics, Manton Center for Orphan Disease, Boston Children's Hospital, Boston, United States
   2. Howard Hughes Medical Institute, Boston Children's Hospital, Boston, United States
   3. Department of Neurology, Harvard Medical School, Boston, United States
   4. Department of Pediatrics, Harvard Medical School, Boston, United States
   5. Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   GDE, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Eunjung Lee

   Competing interests

   The authors declare that no competing interests exist.

2. Eunjung Lee

   1. Department of Biomedical Informatics, Harvard Medical School, Boston, United States
   2. Division of Genetics, Brigham and Women's Hospital, Boston, United States

   Contribution

   EL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Gilad D Evrony

   Competing interests

   The authors declare that no competing interests exist.

3. Peter J Park

   1. Department of Biomedical Informatics, Harvard Medical School, Boston, United States
   2. Division of Genetics, Brigham and Women's Hospital, Boston, United States

   Contribution

   PJP, Conception and design, Drafting or revising the article

   For correspondence

   peter_park@hms.harvard.edu

   Competing interests

   The authors declare that no competing interests exist.

4. Christopher A Walsh

   1. Division of Genetics and Genomics, Manton Center for Orphan Disease, Boston Children's Hospital, Boston, United States
   2. Howard Hughes Medical Institute, Boston Children's Hospital, Boston, United States
   3. Department of Neurology, Harvard Medical School, Boston, United States
   4. Department of Pediatrics, Harvard Medical School, Boston, United States
   5. Broad Institute of MIT and Harvard, Cambridge, United States

   Contribution

   CAW, Conception and design, Drafting or revising the article

   For correspondence

   Christopher.Walsh@childrens.harvard.edu

   Competing interests

   The authors declare that no competing interests exist.

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