Rare Diseases

Expanding the Phenotype of Spinal Muscular Atrophy

September 19, 2018 by dkoboldt 1 Comment

One of the major challenges of rare disease genomics is the availability of patient samples. The disorders being studied are, by definition, rare in the human population, and the variants that cause them (usually) rarer still. When it comes to identifying and establishing truly novel disease genes — genes that have not yet been associated with disease in humans — the burden of proof is fairly high. One strategy for providing such proof is to show that the same variant, or variants in the same gene, segregate with disease in multiple unrelated patients.

Of course, you have to find those patients first.

This should be increasingly possible in an era of widespread exome and genome sequencing. Especially among researchers who are willing to collaborate and share data.

Gene and Variant Matching Services

A number of services have emerged that aim to connect patients, families, and researchers interested in the same candidate gene in similar phenotypes.

The Matchmaker Exchange (above) acts as a centralized portal to query across GeneMatcher, DECIPHER, myGene2, and other such portals. Also, research groups such as the Centers for Mendelian Disease Genomics maintain up-to-date lists of the phenotypes and genes they’re actively pursuing.

I was surprised to learn, while attending the Family Genomic Studies Workshop at OSU this week, that many researchers working on rare diseases weren’t aware of these resources. That’s why I’m highlighting them here, briefly. What I’d really like to discuss today, however, is the power of old-fashioned communication and collaboration.

Shoe leather, in other words.

Making the Most of ASHG

The American Society of Human Genetics annual meeting takes place next month in San Diego. The abstracts are now online. Most attendees probably won’t bother reading them in advance, and I think that’s a huge missed opportunity. Here’s a story to illustrate why. I moved to Nationwide Children’s Hospital about two years ago, and began working with the already-in-progress Rare Disease Genomics Project. This was an effort to uncover the genetic basis of disease in children with rare, undiagnosed disorders who had exhausted all clinical testing.

At the time, the project had already enrolled more than a dozen cases, and had some promising leads. One of them was a particularly memorable (and sad) pediatric case. The proband was a boy who was born with arthrogryposis (contractures of the joints) and a striking atrophy of skeletal muscle. He had a host of medical issues. Whole-genome sequencing had uncovered a de novo mutation in BICD2, a gene associated with spinal muscular atrophy of the lower extremities.

The BICD2 Mutation Story

But there were two problems. First, all of the reported disease-causing mutations were missense changes (in blue, below), but our patient had an inframe deletion (in red, below) that removed a single amino acid.

BICD2 variants classified as Pathogenic/LP (ClinVar, Oct 2017)

Second, most BICD2 mutation carriers described in the literature had milder disease (than our patient), typically restricted to the lower extremities. We couldn’t reach a consensus, even internally, about whether or not we’d found the answer.

Fast forward to about a year ago, when the abstracts for ASHG 2017 came online. ASHG doesn’t just make them browsable, but searchable. So I spent an afternoon searching for phenotypes and genes that came to our attention through the rare diseases project, to learn if anyone else was working on them. There was a poster describing a muscular atrophy patient who carried a BICD2 mutation. In fact, it was the exact same inframe deletion as we’d found in our patient. Thanks to the wonderful ASHG app, I bookmarked the poster presentation so I wouldn’t forget.

At ASHG 2017, I found the poster during its assigned session and introduced myself to the lead author. She was an OB/GYN doing a fellowship in genetics. Basically, I wanted to first find out if we had the same patient, because sometimes individuals with rare disorders end up in multiple genetic studies (see ALS). But no, her patient was female and a few years older than ours. We exchanged contact information, and she promised to put me in touch with the PI.

Patient Commonalities

Our patients, as it turned out, had a number of features in common. It was enough to convince both groups that we had a pathogenic mutation. And over the ensuing months, other publications on BICD2 — including one report of a different inframe mutation — had expanded the set of associated phenotypes. Our collaborative paper, now online at Molecular Case Studies, supports the expansion of clinical features among BICD2 mutation carriers to include cerebral atrophy, seizures, dysmorphic facial features, and profound muscular atrophy.

Sadly, as we were writing up the study, we learned that our patient had passed away. Our finding would not have presented this, but it still felt like a gut-punch. Even so, we had found an answer and helped add to the literature about BICD2 mutation carriers. I like to think that it illustrates the power of both stubbornness and collaboration when working on rare disease cases.

Whole-genome analysis for early infant epilepsy

September 4, 2018 by dkoboldt Leave a Comment

Early infantile epileptic encephalopathy (EIEE) is a devastating syndrome of intractable seizures that strike in the first months of life. According to Orphanet, it affects 1 in 50,000-100,000 births. Infants with EIEE may suffer hundreds of tonic spasms per day, both during sleep and wakefulness. The prognosis is not good. Most patients die within two years; those who survive are severely impaired.

Most cases of EIEE arise sporadically (i.e. without a family history), though autosomal recessive inheritance has also been reported. Clinical diagnosis can be challenging, since infantile epilepsy is also associated with certain brain malformations and metabolic syndromes. Genetic testing for EIEE is made challenging by the fact that upwards of 50 genes have been associated with the disorder (current gene panels test ~50-130 genes). Even so, 40% of patients tested by gene panel or exome sequencing fail to achieve a diagnosis.

Earlier this month, a group at the University of Utah published a study demonstrating the power of whole-genome sequencing as a diagnostic tool for EIEE. Their cohort comprised 14 EIEE patients (recruited in 2015-2016) who had undergone extensive prior testing without receiving a diagnosis. The probands were tested, along with their parents, by deep whole genome sequencing (~65x coverage) and comprehensive variant analysis.

Comprehensive Whole Genome Analysis

One showpiece of this study is the bioinformatics methodology the authors employed to maximize their chances of detecting causal variants. They applied well-established tools to call SNVs/indels detector (GATK) and SVs/CNVs (LUMPY/SVtyper). Because they were particularly interested in de novo mutations — present in the child but not either parent — they also utilized a reference-free k-mer analysis algorithm called RUFUS to reveal sequences present in the proband that were not found in either parent, suggestive of a de novo event.

This is important because most of the apparent de novo mutations identified by routine variant analysis tools (e.g. GATK) are not real: they’re either alignment artifacts in the child, or variants that were missed in one of the parents. By performing a k-mer analysis, you avoid the former. It’s a cool idea, really. Wish I had thought of it myself.

The authors prioritized variants using what one might call the “lowest hanging fruit” strategy. They only looked at de novo events, and their search tiers were as follows:

Nonsynonymous coding mutations in known or candidate EIEE-associated genes
SVs predicted to disrupt EIEE-associated genes
Nonsynonymous coding mutations in genes not associated with EIEE
SVs affecting genes not associated with EIEE

De Novo Mutations in Infant Epilepsy Genes

Notably, the first tier search returned a diagnosis — a pathogenic/likely pathogenic mutation in an EIEE-associated gene — for 10/14 subjects (71%). That’s close to the expected diagnostic rate, and thus not terribly surprising. What is a bit surprising is that three of these subjects reportedly had negative epilepsy gene panels prior to enrollment, which raises the question of how such obvious pathogenic mutations were missed.

One case was solved in search tier 2, when the proband was found to harbor a de novo 63-kb duplication within CDKL5. The duplication was predicted to cause a frameshift and early stop, and it was on the X-chromosome (so hemizygous) in a male patient.

Mutations in Strong Candidate Genes

Two cases were solved in search tier 3. One harbored a mutation in DEAF1 (associated with dominant intellectual disability and recessive epilepsy). The variant (p.G212S) lies in the SAND domain, in which virtually all pathogenic / likely pathogenic missense variants in ClinVar reside:

ClinVar Pathogenic Variants in DEAF1 — ClinVar P/LP missense (blue), nonsense (red), and inframe (light blue) variants in DEAF1.

As you may have noticed from the above, the p.G212S variant is already in ClinVar, as it was reported in a different patient with dominant intellectual disability and seizures.

Another harbored a missense mutation in CAMK2G, which the authors propose as a novel EIEE gene. CAMK2G encodes the gamma subunit of the calcium/calmodulin-dependent protein kinase II complex, which plays an essential role in synaptic function. The missense variant is predicted to be damaging by about half of in silico algorithms (our lab would not apply PP3 here, but this doesn’t change the interpretation). There is one reported heterozygote for this variant in gnomAD, but read data are not available and in my opinion that’s within the margin of error. Missense variants in CAMK2G appear to be under constraint. Overall, a pretty good candidate.

A de novo balanced translocation

So now there’s only one case to solve, and for that one the authors plunge into search tier 4 (SVs not affecting EIEE genes), also called the bucket of crap. I have spent countless hours running down candidates in tier 4 for our rare disease cases with little to show for it. Unlike me, however, the authors of this study actually found something that may be relevant: a de novo balanced translocation between chromosome 2 (2p16.1) and the X-chromosome (Xq28). A 92-gene segment from chromosome X thus ends up on chromosome 2, where the authors speculate that X-inactivation may be disrupted, altering their transcription.

One of the translocated genes is MECP2, which also harbored a de novo noncoding mutation in this patient. Mutations in MECP2 can cause Rett syndrome, an X-linked dominant disorder that manifests with a rather specific phenotype. From OMIM:

Rett syndrome is a neurodevelopmental disorder that occurs almost exclusively in females. It is characterized by arrested development between 6 and 18 months of age, regression of acquired skills, loss of speech, stereotypic movements (classically of the hands), microcephaly, seizures, and mental retardation.

Rett, a Viennese pediatrician, first described Rett syndrome after observing 2 girls who exhibited the same unusual behavior who happened to be seated next to each other in the waiting room.

The patient in this study had microcephaly, seizures, and global delay, which are all prominent features of the Rett phenotype. I’d be very curious to learn if she also develops the characteristic hand movements. While not definitive, the fact that these alterations are de novo, and affect multiple highly relevant genes, suggests that these are somehow responsible for the EIEE phenotype.

Molecular Diagnosis versus Candidate Gene

In the abstract, Ostrander et al write that “the detection of a pathogenic or likely pathogenic mutation in all 14 subjects demonstrates the utility of WGA.” If I wanted to nitpick, I’d argue that they have P/LP variants in 12 patients, since CAMK2G is not yet established as an EIEE gene and the causal nature of the chr2-chrX translocation in patient 2 is not established. More accurately, the authors have uncovered a P/LP variant or strong candidate variant in all 14 patients.

The cohort in this study does not entirely represent clinical reality, particularly because only subjects for which both parents were willing and available to participate were included. That’s an ideal setup if one wants to pursue only de novo mutations. A patient with only one available parent (or no available parents) is a common situation in genetics clinic and would present considerable challenges to this type of analysis.

Whole Genome Sequencing Cost and Speed

Most geneticists recognize the power of WGS to comprehensively detect genomic variants, and to return those results relatively quickly. The cost of WGS, however, remains a significant barrier. Clinical WGS currently costs around $15,000 per trio, roughly twice the cost of clinical exome sequencing. Yet, as Ostrander et al point out, the patients in this cohort underwent a minimum of 24 diagnostic tests, costing (on average) $30,866 per patient.

Perhaps just as importantly, at least three of the patients in this study had undergone gene panel and/or exome sequencing which failed to detect a rather obvious pathogenic mutation. The authors did not explore this further, at least in the paper. Yet it emphasizes that WGS may provide faster and more comprehensive results as a diagnostic tool.

Sequencing or Array Testing for Genetic Diseases?

July 19, 2018 by dkoboldt Leave a Comment

When a child is born with a suspected genetic condition, an increasing number of tools are available to the clinician: newborn screening panels, metabolic testing, cytogenetic testing, gene panels for certain conditions… and more recently, comprehensive sequencing of the exome (i.e. all protein-coding genes) or the genome. Yet chromosomal microarray (CMA) is often the frontline diagnostic tool.

In fact, many clinical laboratories offering clinical exome sequencing require or strongly recommend that an array be performed. There are good reasons for this. CMA testing:

Has a faster turnaround time than WES
Costs less than WES/WGS
Diagnoses (or excludes) many prevalent genetic conditions such as Down syndrome, 22q (deletion) syndrome, Smith-Magenis syndrome, etc.

Many clinical testing labs actually require that microarray testing to have been performed (when appropriate) prior to exome sequencing. There’s sound reasoning behind this: large-scale chromosomal aberrations should generally be ruled out before one goes looking for small variants that might cause syndromic changes.

Will Sequencing Supplant Arrays for Testing?

As the cost and turnaround time of sequencing-based tests improve, there’s a growing interest in ordering these tests either in parallel or instead of chromosomal microarray. Some of that interest comes from Illumina, whose representatives have been touting whole-genome sequencing as the “one test to rule them all” for the diagnosis of rare disorders.

Of course, Illumina has a monopoly on the next-gen sequencing market, so they have financial incentives for encouraging the switch to sequencing-based tests.

One requirement, if we move in that direction, is a solid understanding of the diagnostic and clinical utility of each test. In other words, it’s useful to know how often array, exome, or genome sequencing tests provide a molecular diagnosis (diagnostic utility) and whether or not that changes patient care (clinical utility).

Diagnostic Success of Array, Exome, and WGS

A study published this month in NPJ Genomic Medicine examines the diagnostic success and clinical utility of whole genome sequencing (WGS), whole exome sequencing (WES), and chromosomal microarray (CMA) over the past five years (see the nice summary on GenomeWeb). Michelle Clark et al looked at 37 studies (published from 2013 to 2017) describing a total of 20,068 patients who underwent genetic testing by WGS, WES, and/or CMA.

diagnostic utility by size — Clark et al, NPJ 2018

The studies ranged in size from 22 to 1,745 probands, and the number was modestly correlated with diagnostic success: on average, an increase of 1,000 probands decreased diagnostic utility by 28% (see right). So large studies tend to have a lower diagnostic rate, but I should point out that there are a number of factors at play here. One obvious consideration is that it’s easier to perform deeper analysis/interpretation on a small cohort than a large one. Another is that only a subset of laboratories have the capacity to test thousands of samples. More on that in a minute.

The Setting Effect: Hospitals versus Reference Labs

The studies in this meta-analysis took place in one of three settings:

Research studies to identify new disease genes or test novel methodology (8 studies totaling 1,885 probands; the majority coming from the DDD study)
Hospital-based sequencing and interpretation (19 studies totaling 1,597 probands)
Clinical testing by reference laboratories (8 studies totaling 7,065 probands)

The authors found that the diagnostic utility of hospital-based testing (0.42) was significantly higher than that of reference laboratories (0.29). They speculate that this, in view of the number of probands reported, may influence the inverse correlation between study size and diagnostic success. Consider some of the well-known studies in the “Reference Laboratory” category:

Baylor (2014): 1,745 probands, 26% diagnostic success
Ambry (2015): 417 probands, 31% diagnostic success
GeneDx (2016): 3,040 probands, 29% diagnostic success

Despite the lower-than-hospital diagnostic utility, reference laboratory publications are important for a number of reasons. First, they were large studies that helped set expectations in the medical and research communities about the expected “yield” of clinical WES. In essence, they’re the basis of the widely-accepted figure that exome sequencing provides an answer in about 1/3 of cases.

Second, their underlying metrics provided guidance for groups who carried out their own sequencing and interpretation. For example, they highlighted the major contribution of de novo mutations, which account for ~45-50% of successful diagnoses. Third, these studies served as a form of accreditation for major commercial providers of clinical WES.

The Value of Trios

It’s well known among sequencing laboratories that there’s a higher chance of success when testing a trio (proband and both parents) than a proband alone (singleton). Trios provide valuable information, such as the origin of a variant, that factor into interpretation. The meta-analysis by Clark et al has put some numbers behind it: Across five studies totaling 3,613 children, the diagnostic rate for trios was double the diagnostic rate of singletons.

This supports the notion that, when possible, a full trio should be used for genetic testing. Some laboratories accomplish this by sequencing the proband and then testing parents for a limited number of promising variants. Other labs, like ours, prefer to sequence all three samples right off the bat.

It’s good that we can quantify the benefit of testing a full family trio. That being said, we have to recognize that this will not always be possible in our modern world. Some children won’t have both biological parents around, available, and willing to participate in genetic testing. That’s just a fact, and we have to do our best to serve them even in non-ideal testing conditions.

Sequencing Versus Array

Now we come to the meat of it: comparing diagnostic and clinical utility between WES, WGS, and microarray. Only two studies compared the diagnostic rate between WES and WGS, and it was not significantly different. Thus, WES and WGS together were compared to microarray.

On average across 28 studies totaling 10,392 probands, the diagnostic rate by WES/WGS was 31%.

On average across 13 studies totaling 11,429 probands, the diagnostic rate by array was 11%.

That’s the main answer, but we can dig deeper using studies of children that received both WES/WGS and array testing. There were four such studies that provided sufficient information to calculate the odds ratios, which revealed that the odds of a diagnosis by WGS/WES was 8.3 times greater than array.

The authors also noticed a interesting trend over the five years of their meta-analysis: the odds of diagnosis increased 16% each year for WES/WGS but decreased 14% each year for array. They point out that the former is probably due to the maturation of sequencing-based tests over time. The latter, however, is initially surprising since array testing was already fairly mature in 2013.

It turns out that the decrease in array diagnostic utility is probably due to reimbursement. Once it started being routinely reimbursed by insurance, array testing got ordered much more often and the diagnostic utility decreased as a result. I expect we might see a similar effect as WES becomes the frequent go-to for clinicians.

It isn’t necessarily a bad thing to have WES accepted and reimbursed as a frontline diagnostic tool, but we should be prepared to explain such possible outcomes.

Clinical Utility of WES, WGS, and Array

Last we come to the vital question of clinical utility, i.e. whether a patient’s management is changed based on the results of molecular testing. Here there were differences between sequencing strategies: clinical utility was significantly higher for WGS (0.27) than for WES (0.17). Yet both of them had substantially better clinical utility than array (0.06).

Recommendations for Genetic Testing Practice

Under current guidelines, chromosomal microarray testing is the first-line genomic test for children with intellectual disability, developmental delay, autism spectrum disorder, and congenital anomalies. There are no guidelines yet established for WES/WGS testing.

It may be time for that to change.

Rare Disease Research Collaboration: RLIM in X-linked Intellectual Disability

May 7, 2018 by dkoboldt Leave a Comment

Access to samples is one of the main challenges of studying rare inherited conditions, particularly those whose molecular basis is unknown. Every individual harbors 3-5 million genetic variants compared to the human reference sequence. Even if we winnow these down to only rare variants likely to disrupt a protein-coding gene, hundreds still remain.

The Power of Multiple Patients

One of the most efficient ways to isolate a disease-causing variant is to search for genes that are disrupted in multiple patients. Better yet, genes disrupted in multiple unrelated patients. This is nicely illustrated by Miller syndrome, one of the first disorders whose gene was identified by exome sequencing in 2010. Here’s a table I’ve adapted from that paper showing the number of candidate genes at each stage of the analysis:

rare syndrome recurrence power — Adapted from Ng et al, Nature 2010 (doi:10.1048/ng499)

The first family kindred had two affected siblings, each harboring several thousand nonsynonymous (NS), splice site (SS), or indel (I) variants in coding regions. Filtering against population allele frequency databases and looking at both siblings simultaneously winnowed this down to 237 candidate genes, which is a lot. However, incorporating data from unrelated kindreds 2 and 3 rapidly narrowed it down to less than ten genes, of which one was DHODH, the ultimate cause of disease.

Clearly, searching for recurrently altered genes in multiple patients/kindreds with a rare disorder provides enormous discriminatory power. The challenge is finding two or more willing participants with a rare condition.

The Challenge of Rare Disorders

A rare disorder that occurs in 1 out of every 100,000 individuals will affect about 40 patients per year in the United States. Most of those will go through a battery of diagnostic testing once they come to medical attention. Only a fraction will be referred to a research study, and an even smaller fraction will be enrolled. That’s effectively a handful of new cases per year. These will likely be spread out across dozens of research institutions.

Given the wide spectrum of rare conditions, it’s extremely unlikely that two patients with the same disorder would be identified (and recognized as such) at a single institution. Programs like the NIH Undiagnosed Disease Network aim to centralize the recruitment, sequencing, and analysis of rare disease cases. However, the vast majority of undiagnosed disorders are being studies by research institutions outside of such programs.

Efforts such as MyGene2 and the MatchMaker Exchange aim to facilitate data sharing among families with rare disorders and the investigators who study them. However, a number of rare disorders are still solved by good old-fashioned academic collaboration.

The RLIM/RNF12 Gene in X-linked Intellectual Disability

Case in point: in late 2016, our rare disease research program enrolled a patient with small size, hypotonia, and developmental delay — three of the most common phenotypes of patients referred to genetics. This patient, however, also had hypospadias and ambiguous genitalia. There was also a family history of congenital diaphragmatic hernia (a birth defect involving the diaphragm), which initially did not seem very important.

We performed whole-genome sequencing on the patient and his affected parents, which yielded about 6.5 million genetic variants. After running these through our comprehensive annotation/analysis pipeline, I narrowed the list to 12 recessive genes and three X-linked genes. The most interesting candidate was a hemizygous missense variant in X-linked gene RLIM, also known as RNF12. In a recent paper in the journal Molecular Psychiatry, a European-led team had reported a similar variant (p.P587R) in the same domain in two families with non-specific mild to profound ID, behavior problems, ID, microcephaly, micrognathia and cryptorchidism.

Our clinicians thought it was an interesting fit, but noted that our patient’s phenotype seemed more severe than the cases described in the paper. We discussed testing additional family members and possibly doing X-chromosome inactivation studies (XCI) on the patient’s mother. At the same time, one of our genetics clinicians reached out to the group in Germany who’d published the RLIM paper. We were hoping, at the very least, to get their impression of whether our patient resembled the RLIM cases in their cohort.

The response was unexpected but exciting: the German team was working on a follow-up paper with clinical details and functional studies on 3 large families with affected males in several generations.
They’d already performed functional studies in zebrafish and examined XCI in heterozygous females. The phenotypes were quite similar to those of our family, including (in some cases) congenital diaphragmatic hernia. The authors graciously invited us to be part of the manuscript.

The resulting paper, Pathogenic variants in E3 ubiquitin ligase RLIM/RNF12 lead to a syndromic X-linked intellectual disability and behavior disorder, is now online at Molecular Psychiatry. Its publication not only provides definitive diagnosis for our patient, but expands the phenotypic spectrum of RLIM mutation carriers and establishes the gene’s role in normal development, cognition, and behavior.

On a broader scale, this demonstrates the power of open communication and data sharing in the rare disease research community.

The Power of Sequencing Data Re-analysis

April 6, 2018 by dkoboldt Leave a Comment

The clinical genetics group at our hospital holds a weekly conference to discuss patients recently seen by the clinicians and/or genetic counselors. I began attending this “post-clinic conference” when I moved here a year and a half ago. It’s been very educational for me, both in learning about some of the rare conditions affecting our pediatric patients and in how clinicians diagnose them.

In one of the first post-clinic conferences I attended (early September 2016), one of the clinicians presented a case of two sister with a severe disorder of unknown cause. They’ve apparently been followed by our team for a number of years. Both have severe intellectual disability, hypotonia, seizures, and a distinctive craniofacial appearance. Various clinical tests had been ordered, but none had provided a diagnosis.

It came up in the course of discussion that research exome sequencing had been performed on both girls and their (healthy) parents some time ago. There had been no finding at the time, but it was a few years back. Since I’d been recruited to work on rare disease genomics, I offered to revisit the analysis. We ran the case through our current pipeline, and found an interesting result:

Both sisters appeared to share a de novo (i.e. newly arisen) nonsense mutation in a gene called Additional Sex Combs Like 3 (ASXL3).

ASXL Mutations and Disease

A few years ago, Matt Bainbridge and his colleagues at Baylor had sequenced four unrelated patients with a disorder resembling Bohring-Opitz syndrome. Bohring-Opitz is a severe malformation disorder characterized by intrauterine growth retardation, severe intellectual disability, flexion deformities of the upper limbs, forehead hirsutism, and some craniofacial features. In 2011, a group in Europe had found that Bohring-Opitz was caused by de novo nonsense mutations in Additional Sex Combs Like 1 (ASXL1), which is required for maintenance of both activation and silencing of Hox genes.

The four unrelated patients in the Bainbridge study had de novo nonsense and frameshift mutations in ASXL3 (not ASXL1). Moreover, they were clinically distinct from Bohring-Opitz syndrome, with features including severe feeding difficulties, failure to thrive, and neurologic abnormalities with significant developmental delay. OMIM entered it as a new syndrome in their database, and named it Bainbridge-Ropers syndrome (BRPS).

The two sisters from our case also had a de novo ASXL3 mutation. I was excited, and presented the finding at post-clinic in mid-September 2016. However, it got a lukewarm reception. Our mutation was in the middle of the ASXL3 gene (residue 1036), and predicted to truncate it to 46% of its wild-type length. The Bainbridge et al mutations were closer to the front of the gene (residues 404-466), which would remove a lot more. The phenotype was not a perfect match, either. Bottom line, they were unconvinced.

New Reports of Bainbridge-Ropers Syndrome

Over the next year, however, other groups identified de novo truncating ASXL3 mutations in other individuals with severe intellectual disability. A 2017 study by Balasubramanian et al described 12 previously unreported patients who were part of the DDD Consortium study (a large-scale intellectual disability cohort based in the UK). They delineated the six emerging common features of BRPS:

Severe intellectual disability (11/12 patients)
Poor or absent speech (12/12)
Autistic traits (9/12)
Distinct face, including arched eyebrows, prominent forehead, high-arched palate, hypertelorism and downslanting palpebral fissures (9/12)
Hypotonia (11/12)
Significant feeding difficulties when young (9/12)

Our patients, you might recall, had all of these features. Also, the new studies reported de novo truncating mutations in the 1,000-1,100 region of the ASXL3 protein, similar to ours. With this new information, everyone was convinced and we started writing the paper. In the interim, one of our clinicians saw another (unrelated) patient who got a clinical diagnosis of BRPS.

A Chat with Bainbridge

In October 2017, I went to the ASHG annual meeting, where I bumped into Matt Bainbridge. We’ve known each other for years (since the 1,000 Genomes Project). Naturally, I gave him some trouble about having a syndrome named after him. Then I asked him about our patients’ mutation in ASXL3. He basically said, that’s right in the other cluster of disease-causing mutations.

I asked Matt if he thought it strange that our institution had three patients with a super-rare syndrome (<30 cases in the literature). He said that it’s probably more common than people realized. In fact, there might be as many as 200 cases diagnosed in the US.

“How do you know?” I asked.

“Because there’s a Facebook support group for the families,” he said. “In fact, they’d probably like to talk to your family, if they were interested, because most of their kids are younger.”

A Shared de novo Mutation

We reported the ASXL3 mutation to ClinVar in October 2017. By then, we’d performed independent Sanger sequencing of all four family members to confirm that it was de novo. The paper was accepted in December, and is available as an early access publication at Molecular Case Studies.

One obvious question is how two siblings born over a year apart could share the same de novo mutation if it arose after conception. Although this could theoretically happen by chance, the odds are extremely low. More likely, one of the parents is a mosaic for the mutation. The family declined further testing to determine the origin. They were not planning to have more children, anyway.

Unfortunately, there’s not much else we can do to help the family. However, we did refer them to the Facebook support group, and that may offer some support.

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