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The identification of novel disease genes sometimes overshadows another crucial form of genomic discovery: expanding the phenotype associated with 原子加速器破解版 disease genes. This is especially important in the era of pervasive clinical genetic testing.

Exome sequencing, which interrogates all ~20,000 protein-coding genes simultaneously, is rapidly becoming a frontline diagnostic test for patients with rare genetic conditions. The obvious advantage of exome sequencing is its comprehensiveness: the coding regions of virtually all genes are sequenced. From a clinician’s point of view, I imagine that has tremendous appeal. Yet the cost of this comprehensive approach is that it uncovers thousands of protein-coding variants in the patient.

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1. Thorough and precise phenotyping of the patient by expert clinicians
2. The expertise of clinical directors and variant scientists
3. Current knowledge of genotype-phenotype relationships for disease genes

Dependency #3 is the one that’s most likely to change over time. This is supported by recently published studies of clinical WES reanalysis, which have consistently found that new knowledge (i.e. a newly identified disease gene) is the most common source of positive findings among previously negative WES cases.

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The rare disease genomics study at our institution has been running for about four years. One of the first cases we enrolled was a child with arthrogryposis multiplex congenita, i.e. joint contractures affecting multiple parts of the body. This condition is believed to be the result of reduced intrauterine movement. In the case of our patient, the reason for that reduced movement was a striking lack of skeletal muscle.

We enrolled the patient and his parents on our research protocol and performed whole-genome sequencing. This uncovered a de novo variant in a gene called BICD cargo adapter 2 (atom官网下载). Back in 2013, three studies of large family kindreds with dominant muscular atrophy affecting predominantly the lower limbs had linked the disorder to missense variants in BICD2. The Online Mendelian Inheritance in Man (OMIM) database, the definitive resource for gene-disease associations, described the condition as spinal muscular atrophy, lower extremity dominant, type 2 (SMALED2).

A dominant form of muscular atrophy could in theory describe our patient, but there were two problems:

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2. Our patient had severe atrophy throughout the body at birth, whereas the patients with SMALED2 generally presented later in life and usually with only lower limbs affected.

As a result of the inconsistencies, we chased other leads in this case, none of which panned out. BICD2 remained our top candidate, but not everyone was convinced that it was the answer. Myself included.

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In October 2017, I went to the annual meeting of the American Society of Human Genetics (ASHG). On the way to the meeting, I perused the program through the mobile app and searched the submitted abstracts for some of my topics of interest. One of those was the BICD2, and it came back with a hit: a poster from a researcher at Mount Sinai that described a patient with a de novo inframe indel in BICD2.

And it was the same indel we’d found in our patient, a deletion of a single amino acid: p.(Asn546del).

I met the poster’s author, who was an Ob/Gyn doing a fellowship in genetics. My main concern was that we might have the same patient. We quickly determined that this was not the case — her patient was several years older and female. Then we discussed the clinical features, and determined that the overlap was significant. Same gene, same variant, similar clinical presentation in unrelated patients. That’s the holy grail of rare disease research. It was enough to publish, and (importantly) enough to convince me and my collaborators that BICD2 was the answer after all.

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BICD2 itself is a fascinating gene. It’s one of two human homologs of the fly gene Bicaudal D (bicD), so named because mutating it in Drosophila produced flies with two trunk segments, rather than a head and a trunk. As a component of the dynein molecular motor complex, human BICD2 plays a pivotal role in intracellular transport. The protein’s three coiled-coil domains have specific binding partners:

BICD2’s N-terminal stabilizes the dynein-dynactin complex. Direct visualization in live cells indicates that the complex alone is unable to interact with microtubules, but tethering of BICD2’s C-terminus to different membrane cargoes induces their movement toward microtubule minus ends. As a cell prepares to divide, BICD2 switches its binding preference from RAB6A to nucleoporin RANBP2, recruiting it to the nuclear pore context and regulating dynein and kinesin to keep the centrosomes closely tethered to the nucleus prior entering mitosis.

Many of the consequences of a pathogenic BICD2 mutations in muscle cells are striking enough to be seen under a microscope. The most common mutation in SMALED2 (p.S107L) occurs in the first coiled-coil domain and appears to increase BICD2’s binding affinity for dynein. This causes the normally compact Golgi apparatus to disperse throughout the cell, a phenomenon of impaired dynein function called Golgi fragmentation.

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Some new information emerged as I worked on the case report. First, the study coordinator discovered that our patient had recently passed away at the age of six. This was saddening, even though our finding would not have changed it.

Second, I learned that there had been new reports on BICD2 in the intervening years, some of which described a more severe phenotype with onset in utero associated with de novo mutations in BICD2. At around the time I’d gone to ASHG in 2017, a group at the University of Cologne (Storbeck et al) published a study emphasizing the atom安卓下载 mutation carriers. Four of their five reported patients had also passed away at a young age. The last line of their abstract read:

Our data define an additional severe disease type caused by BICD2 and emphasize a possibly variable etiology of BICD2-opathies with regard to primary muscle and neuronal involvement.

Around the time we submitted our manuscript, a group in France published a 黑猫tomAPP破解版 of an inframe indel in BICD2 segregating in a family with non-progressive SMA. Now there was precedent for the variant type as well.

Our report in Molecular Case Studies added to the growing number of reports of patients with BICD2 variants who manifested a disease that was far more severe than the later-onset, lower-extremity-predominant SMA on record for BICD2. This was brought to the attention of the curators of the OMIM database, who revised their entry for BICD2 to recognize two associated disorders:

• SMALED 2A (MIM #615290), the classic presentation of later-onset affecting mainly lower limbs
• SMALED 2B (MIM #618291), the severe systemic disease with prenatal onset

Both conditions are caused by dominant missense or inframe variants in BICD2. The large family kindreds published in 2013 fall under type 2A. Our patient and others with atomVNP mutations generally fall under type 2B.

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New information continues to emerge as more patients are described in the literature, or their variants are submitted to the ClinVar database by clinical laboratories. As best I could tell, there were close to a hundred patients with pathogenic BICD2 variants described across a dozen publications and/or the ClinVar database.

I spent much of 2024 collating all of these reports and organizing them into the most comprehensive review to date of the genetic basis and phenotypic spectrum of BICD2 disease in humans. It was just published in Annals of Neurology and I hope you’ll give it a read:

Koboldt DC, Waldrop MA, Wilson RK, and Flanigan KM. The Genotypic and Phenotypic Spectrum of BICD2 Variants in Spinal Muscular Atrophy. Ann Neurol. 2024 Apr;87(4):487-496. doi: 10.1002/ana.25704. PubMed: 32057122

Despite its proven utility for providing molecular diagnoses in patients with genetic disorders, exome sequencing is rarely the first-line diagnostic test ordered by most physicians. There are numerous valid medical and practical reasons for this. In some ways, exome sequencing is a victim of its own success: by enabling rapid discovery of many disease-causing genes, it has facilitated the development of numerous commercial gene panels that can be ordered (usually with lower cost and quick turnaround) for patients with suspected diagnoses.

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I came across a recent study of clinical exome sequencing in a highly consanguineous population led by researchers in Saudi Arabia that was published in the 增值税电子发票阅读器绿色版_增值税电子发票阅读器下载 v3 ...:2021-6-4 · 增值税电子发票阅读器是一款由国家税务总局推出的软件工具，它的主要功能是帮助用户在电脑上打开ofd格式的电子发票文件，从而方便用户查看和打印发票。增值税电子发票阅读器使用起来很方便，适用于各种类型的增值税电子发票，有需要的用户敬请下载。. Although a number of exome cohort studies have been published over the past eight years or so, this one is unique for a couple of reasons. First and most obvious is the Saudi population, which has a high fertility rate and a shockingly high consanguinity rate of >50%. Second, the country’s Ministry of Health began covering clinical whole exome sequencing (WES) as a first-line diagnostic test. This financial commitment is unusual, and enabled WES for thousands of patients by a single clinical laboratory.

Because of this support, any clinician could order WES, and it was also their decision whether additional family members would be sequenced as well. Nearly half of the ordering physicians, interestingly, were not clinical geneticists. About 75% elected to send the proband only for WES. Normally, that’s a disadvantage, as full trios (child and both parents) tend to have a higher diagnostic yield. However, in this population it might not be as detrimental since the expectation is a homozygous causal allele.

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The well-documented diagnostic yield (success rate) of clinical exome/genome sequencing for unselected sequential patients with rare disorders is around 30%, with a community-accepted range of perhaps 25-35%. This figure has remained remarkably consistent across several years and numerous different clinical diagnostic laboratories, including our own. Although some groups have achieved and reported higher rates, these typically reflect subsets of phenotypes or some type of case selection. Generally speaking, 30% is the expected diagnostic yield.

In the Saudi study, the authors reached a conclusive diagnosis for 961 out of 2,219 families, for an overall diagnostic yield of 43.3%. That’s a very good diagnostic rate, and supports the notion that employing WES as a frontline test in an at-risk population tends to yield more success.

Among the 961 definitively solved cases:

• 77.2% were autosomal recessive (98.4% were homozygous-recessive)
• 20% were autosomal dominant (25% proven de novo; the rest did not have parental samples available)
• 3.9% were X-linked

The proportion of recessive diagnoses is higher than even I’d have expected given the population. For a point of comparison, the DDD study reported thousands of molecular diagnoses for neurodevelopmental disorders, finding that just 3.6% were due to autosomal recessive inheritance.

WES Result versus Clinical Diagnosis

Another key finding of this study was that an overwhelming majority of individuals with solved diagnoses (78%) did not carry the correct clinical diagnosis when the WES was ordered. That’s a compelling figure. It suggests that, particularly in this study population, an early comprehensive laboratory test provided new information. Perhaps even information that changed management or care, though that’s not investigated here.

Molecular Autopsy by Proxy

Another powerful application of clinical WES was showcased by this study: the ability to perform a molecular autopsy when material from a deceased child is not available. There were 80 couples who had lost at least one child and were referred for prenatal counseling. In 45 of these (56.3%), WES of the parental samples provided a likely molecular cause. All of these were recessive disease genes where both parents were heterozygous for a pathogenic variant. The majority of those (28, or 62%) were loss-of-function variants, which undoubtedly was a factor in the previous losses.

This naturally highlights the potential positive impact of prenatal testing in populations (like this one) where there’s an increased risk of recessive disorders. The authors plunge into an exploration of population-specific and private alleles that inform this topic, but I won’t cover them here.

Confirmed and Candidate Disease Genes

One very appealing aspect of this study is that a single laboratory conducted all of the WES, enabling them to catalogue genomic findings across thousands of families. After all, the 56.7% of cases that did not achieve a definite molecular diagnosis are fertile grounds for gene discovery. The authors thus included in their study a list of 236 proposed candidate genes for genetic disorders, some of which were observed in multiple families in their cohort or had supporting evidence in animal model systems.

Their efforts also “confirmed” the disease association for 60 genes which had been reported to cause disease on the basis of a single family and/or were not otherwise recorded in OMIM. This work thus represents a valuable resource to the research community, since rare disease genes tend to be, you know, rare. The authors also helped delineate and expand the phenotypic spectrum of many disorders, which is always a valuable addition to the public record.

One intriguing class of findings are the 10 cases (8 genes) that appear to have recessive forms of genes previously believed to be autosomal dominant. Some of these presented with a phenotype that matched the AD disorder, while others showed earlier onset or a more severe presentation. For at least one disease gene, the recessive disorder apparent in the family was quite distinct from the known AD disorder.

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A few other known but uncommon genetic testing phenomena were observed in this cohort.

• Parental gonadal mosaicism was noted in at least 3 families, highlighting the importance of looking for this in apparent AR pedigrees.
• A 原子加速器破解版 mutation “second hit” in a recessive disease gene was noted in 1 family. This is something a number of analysis pipelines might not detect, so it’s reassuring that it’s somewhat rare.
• 3.1% of cases had more than one genetic diagnosis (called multi-locus diagnoses in this study), either multiple AR disorders (1.57%), multiple AD disorders (0.4%), or a combination (1.35%).

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In summary, this is a very useful study that emphasizes the power of WES as a first-line diagnostic tool and a second-line discovery vehicle, particularly in this unique population. I hope to see more studies like these in years to come.

This week I’m attending the American Society of Human Genetics meeting in Houston, Texas.

If you’re at this meeting, please track me down. I’d love to connect with past colleagues and long-distance collaborators as well as anyone with an interest in rare diseases and epilepsy. I’m also a new incoming member of the ASHG 2024 Program Committee, so your feedback on meeting programming is welcome. Here’s where to find me.

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This invited session (#22, 10:30-12:30 in Grand Ballroom A) brings together experts who have developed model systems for the functional validation of rare sequence variants:

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Wednesday Evening: Scarlet and Gray Reception

The Ohio State University hosts a “Scarlet and Gray” reception on Wednesday, October 16th at 7:30-9:15 pm in Kingwood A (in the Marriott hotel). Everyone is welcome, even Michigan fans.

Thursday: Somatic Mosaicism in Affected and Unaffected Individuals

I’m giving a talk on our somatic epilepsy project s part of this session (#41, 9-10:30 in Grand Ballroom A) which highlights the role of somatic mosaicism in:

• Tissues of healthy individuals
• Parents of probands with pathogenic CNVs
• Brain tissue of pediatric epilepsy patients (my talk)
• Autism spectrum disorder probands
• Human pre-implantation embryos
• Parents of individuals with Mendelian disorders

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I’m the PC liaison for this session (#69, 9-10 in Grand Ballroom A), which brings together some interesting talks on gene expression and regulation in ovarian cancer, lung cancer, and pancreatic cancer, as well as immune regulation in common tumor types.

Saturday: Darin’s Tumor: Mutation and Selection in Cancer Genomes

I’m the PC liaison to this session (#102, 9:45-11:15) of abstract-driven platform talks on:

• Evolution in common tumors
• Unexpectedly high recurrent somatic mutation rates
• Complex focal amplifications
• Positive selection modeling for driver genes
• Gene networks under purifying selection
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Lastly, I’ll be tweeting on the #ASHG19 hashtag as @MassGenomics.

Epilepsy is a broad term used to describe brain disorders that cause seizures. About 1.2% of the population has active epilepsy; in the United States this includes more than 3 million adults and 470,000 children. According to the CDC, about 1 in 10 people will have a seizure in their lifetime.

(Side note: this means it’s somewhat likely that you’ll need to help someone during or after a seizure. Please take 5 minutes to peruse the CDC’s page on atom免费版安卓apk so that you know what to do / what not to do).

The causes of epilepsy are manifold, including strokes, tumors, infections, and injuries affecting the brain. There are also a number of genetic causes of epilepsy, hence my interest. Most currently known epilepsy genes are associated with monogenic (single gene) epilepsy disorders and were identified in family studies. These tend to be genes for developmental and epileptic encephalopathies, which tend to begin early in life and have profound effects on development.

The etiology of other forms of epilepsy — generalized epilepsy (affecting both brain hemispheres) and focal epilepsy (involving a localized region) — are more complex. Genetic studies have struggled to pinpoint the genes involved. For all three types of epilepsy, however, it’s clear that many more genes remain to be discovered.

A manuscript in press at the American Journal of Human Genetics describes the largest exome sequencing study of epilepsy conducted to date. It reports the exomes of 9,170 people with epilepsy (1,476 with developmental/epileptic encephalopathy, 4,453 with generalized epilepsy, and 5,331 with focal epilepsy) compared to 8,436 ancestry-matched controls. There’s a lot of good stuff here, especially if you’re into analysis methods (as I am). For the moment, I thought I’d highlight the results.

Analysis of Ultra-rare Variants in Epilepsy

The authors performed three types of analyses. The main analysis as indicated in the title of the paper, focused on “ultra-rare” variants observed in 3 or fewer individuals in the entire cohort and are absent from the 50,726 individuals in the DiscovEHR database of healthy [European ancestry] individuals.

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• Protein-truncating variants (PTVs), meaning nonsense, frameshift, and splice site variants
• Missense variants predicted to be damaging by in silico tools and a regional constraint score.
• Inframe insertions and deletions, which appear to be treated the same as damaging missense
• Benign missense variants that did not qualify as damaging
• Synonymous variants not predicted to change an amino acid

Excess of Ultra-rare Variants in Genes

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Protein-truncating variants (PTVs) are clearly enriched in cases relative to controls at these extremely rare frequency levels. At less stringent MAF thresholds, this enrichment is apparent but not as significant.

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Most of the burden of urPTVs comes from genes showing strong evolutionary constraint for LOF variants (pLI>0.995). This suggests that the excess PTVs affect genes where such variants are not tolerated in the general population. It’s another way of showing that these variants are probably deleterious.

The authors estimated the excess of ultra-rare variants in numerous subsets of biologically-relevant genes, finding enrichment for damaging/PTV variants in:

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• Brain-enriched genes, defined as those expressed 2x higher in brain tissues according to GTEx data.
• GABAergic pathway genes and voltage-gated ion channel genes

Of note, most of these patterns were in the developmental/epileptic encephalopathy and generalized epilepsy patient groups. The focal epilepsy group — incidentally, also the largest group — yielded few findings, and the authors’ frustration with this outcome is not hard to suss out (or understand).

Per-Gene Burden Testing for Association

Ultra-rare variants were collapsed by gene to examine the burden of such variants in cases relative to controls. This is a fairly standard way to test for association when the variants are (by definition) too rare to reach statistical significance on their own. Basically, you consider anyone with at least one ultra-rare coding variant in a gene to be “mutated” and everyone else to be “non-mutated” for the sake of analysis.

Of note, it’s not only possible, but somewhat likely, that many such variants are de novo mutations. We don’t know for certain, since only the affected individuals and not their parents were sequenced.

There are some major statistical challenges to conducting per-gene tests on the entire exome in a large sample size. The adjustments required to minimize false-positive associations when testing so many genes simultaneously mean that the bar for statistical significance is high. In this study, SCN1A in individuals with developmental and epileptic encephalopathies (p=5.8e-08) was the only gene that met so-called exome-wide significance for association (p-value < 6.8 × 10⁻⁷ after Bonferroni correction).

A jaded view of this outcome might be that the largest exome sequencing study of epilepsy managed to find one gene, that was atom 加速器 apk the best-known and most clinically relevant epilepsy gene. But that’s hardly satisfying. Furthermore, some argue that focusing only on p-values causes one to ignore patterns that are obvious to the human eye, such as the association signal for NEXMIF.

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The authors also performed two other types of analyses. One searched for genes consistent with recessive inheritance that were enriched in cases relative controls, and the other examined the contribution of low-frequency coding variants to epilepsy risk. Both of these used a relaxed threshold for variant inclusion, taking any with MAF<0.01. The recessive analysis found nothing, though the authors note that it was somewhat under-powered.

The SKAT burden analysis did not yield much in the way of compelling findings (surprising no one). Its top associations included known epilepsy genes like STXBP1, KCNA2, NEXMIF, and SCN1A but these failed to reach exome-wide significance. The take-home message here is that ultra-rare, possibly de novo damaging/truncating variants in constrained genes are enriched in the developmental/epileptic encephalopathy subset, and this underlies most of the gene burden in a fairly large epilepsy cohort. It supports the notion that DEE is often caused by damaging, highly penetrant mutations in single dominant genes. The essentially negative findings in generalized and focal epilepsy suggest that these types may have a more complex genetic etiology.

February 28th is atom 加速器 apk, and it reminded me that I’ve been rather sporadic in updating this blog. That’s mostly because I’ve been working on rare disease analyses, publications, and grants. However, it’s motivated me to share a bit about my strategy for studying rare diseases with genomics and how my thinking has evolved in the past year.

Rare Disease Research at NCH

Our research study on the genomic basis of rare diseases has been running for about three years. To date, we’ve enrolled more than 200 individuals from about 65 families. A wide range of phenotypes are represented, including congenital malformations, developmental disorders, neuromuscular diseases, and other conditions.

We’ve found a likely cause of disease or are pursuing a candidate variant in roughly 1/3 of the cases analyzed so far. Three of those were published last year, and several more are submitted or in preparation. This is consistent with the widely-established diagnostic yield (25-30%) for studies like ours. Our clinical laboratory has a similar diagnostic rate for WES cases.

It’s daunting sometimes. We want to provide an answer for every family, but statistically speaking, we’re more likely to have negative results. However, there is value in both success, i.e. providing an answer, and in failure. Here are some key lessons learned.

Rare diseases are more common than you think

This sounds counter-intuitive at first, but it’s something that is quite apparent when working at a major children’s hospital. Any particular rare disease affects only a small proportion of the population by definition. For example, consider 原子加速器破解版, a rare congenital malformation syndrome with an estimated prevalence of 1 in 100,000. That’s pretty rare. Even so, at least three patients have been diagnosed with RSTS in the past couple of years at our institution alone.

That’s just one example of a rare disease. At least 7,000 rare diseases have been described. Consider this infographic from NORD, the National Organization for Rare Disorders:

While individually rare, they collectively are a significant cause of medical conditions. Especially in children. Granted, there’s undoubtedly a selection bias: Kids with rare disorders are more likely to be hospitalized and be seen by a geneticist. Also, many genetic disorders cause chronic medical issues, which means we see them a lot more often than the average healthy kid. This probably makes them appear more prevalent than they really are. Even so, there are 30 million people affected by rare disorders in the United States alone. That’s about the same number as people with diabetes, one of the most common diseases.

Precise and detailed phenotyping is crucial for diagnostics and research

Many rare diseases, especially the well-studied genetic syndromes, have a “classic” clinical presentation. Clinical geneticists are taught to recognize patterns of human malformation that manifest in certain conditions. However, there’s a growing appreciation for the phenotypic spectrum of disease. In other words, not everyone with the same rare condition has the same set of medical problems (or characteristic features). Further, many of the genetic variants underlying rare disease have incomplete penetrance, i.e., not everyone with them gets the disease.

This is why precise and thorough phenotyping is critical in our field. I don’t just mean the patient, but also parents, siblings, and distant relatives. Genetic diseases tend to run in families, and the family history often guides our approach to analysis. When it comes to pedigrees, the word “normal” is a dangerous thing. Sometimes it means perfectly healthy, but other times it means they haven’t come to medical attention as far as we know. Although de novo mutations account for a significant proportion of genetic diagnoses, many causal variants turn out to have been inherited from “normal” parents.

Data sharing and collaboration are keys to success

This is especially true for rare disorders because the patients are usually few and far between.

Back in September, I heard a wonderful talk by Dr. Kym Boycott of the Children’s Hospital of Eastern Ontario who is one of the leaders of Canada’s Care4Rare Consortium. That consortium has provided a genetic diagnosis for 1,500 patients and families, and discovered 135 new disease genes along the way. One of their keys to success? Data sharing. They share genetic and phenotypic data with other investigators via the Matchmaker Exchange (a free service that unifies GeneMatcher, MyGene2, DECIPHER, and other data sharing portals). Further, they make their sequence data available to any qualified researchers who want to have it. They share everything, which I suppose is not surprising since they’re Canadian.

Dr. Boycott’s talk motivated me to start sharing our rare disease research data more systematically. We submit candidate variants/genes of interest to Matchmaker Exchange through GeneMatcher, and we submit variant interpretations to the ClinVar database. These efforts have already paid off; at least three of our rare disease cases now have a diagnosis because of data sharing efforts.

Summary

With new genomics tools & approaches, we’re better equipped than ever to study rare diseases. Even so, sharing and collaboration are essential for the discovery and characterization of new disease genes. There are millions of people around the world counting on us. Let’s get to work.