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Genetic Testing: New Tools Bring Hope

New genetic testing methods are improving diagnoses for those with rare conditions.

Georgia Pitsava, Megan Hawley, Light Auriga, Ivan de Dios, Arthur Ko, Sofia Marmolejos, Miguel Almalvez, Ingrid Chen, Kaylee Scozzaro, Jianhua Zhao, Rebekah Barrick, Nicholas Ah Mew, Vincent A. Fusaro, Jonathan LoTempio, Matthew Taylor, Luisa Mestroni, Sharon Graw, Dianna Milewicz, Dongchuan Guo, David R. Murdock, Kinga M. Bujakowska, Changrui Xiao, Emmanuèle C. Délot, Seth I. Berger, Eric Vilain

― 6 min read


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Table of Contents

Genetic testing has become a common tool in medicine, helping to diagnose rare diseases that sometimes puzzle healthcare professionals. Despite progress in this field, the success rate of these tests often ranges between 30 and 40%. This leaves many individuals still searching for answers about their health conditions. So, what gives?

What is Genetic Testing?

Genetic testing involves analyzing DNA to identify changes or mutations that might cause diseases. Think of it like examining instructions for a recipe to see if any crucial steps are missing or incorrectly written. When it comes to genetic issues, scientists are still discovering many hidden problems where the genes do not function as they should. This means some patients may not receive a clear answer about why they feel unwell.

Why are Some Conditions Hard to Diagnose?

One big reason some conditions remain undiagnosed is that the specific genes responsible for them haven't been found yet. Even when the genes are known, interpreting the tiny changes—called variants—can be quite challenging. Some variants are classified as "Variants Of Unknown Significance" (VUS), which means we can't quite figure out if they are harmful or just harmless quirks of our genetic code.

Additionally, standard tests often miss certain parts of a gene, especially when it comes to regions of DNA that don't directly code for proteins. These regions can still play vital roles in how genes function.

Enter New Technologies

To tackle these challenges, researchers are now using advanced methods like Genome Sequencing (GS) and RNA Sequencing. These advanced techniques aim to give a fuller picture of genetic information and how it leads to diseases.

For instance, while traditional methods might miss crucial variants that exist in "non-coding" regions, GS can pick up on these hidden issues. RNA sequencing, on the other hand, helps researchers see the real-time effects of gene changes. It’s like having a backstage pass to watch a concert where you not only hear the music (DNA) but also see how the instruments (RNA) are being played live.

A Closer Look at the New Research

Researchers at a medical center focused on genetics have been applying these new tools to help families who had previously been without answers. They studied a group of 353 families where genetic conditions were suspected but not diagnosed through traditional testing methods.

Over three years, families shared genetic samples so the researchers could use the latest techniques to potentially find explanations for their conditions. The excitement was palpable as the research team embarked on their quest for answers.

How Many Patients Found Answers?

Through the study, 54 out of the 353 families found a genetic diagnosis, meaning about 15.3% of the cases were solved. This was impressive, especially when considering the number of families who had previously gone years without a definitive answer. Some families had been through multiple tests, yet nothing pointed to a clear issue.

Among the families that found answers, those with more comprehensive testing (like involving both parents) had a better chance of identifying the genetic culprit. It turns out that families where both parents were tested had a "diagnostic yield" of 21%, a figure that was significantly higher compared to the 10% of families tested solo.

What Went Wrong in Previous Tests?

Many of the families who got a diagnosis initially missed out because the tests they underwent didn’t check all the necessary areas of their genes. For example, certain tests didn’t look at specific genes linked to their conditions, while others failed to catch variants hidden in deep parts of the DNA.

In fact, it was found that around 26% of the resolved cases had variants present in the first set of tests but were overlooked due to gaps in the knowledge at the time. Sometimes, it was just a matter of mislabeling a gene as not being associated with the condition.

Cryptic Exons: The Sneaky Culprits

One intriguing finding was that some problems stemmed from what’s called "cryptic exon inclusion." This happens when a genetic mutation silently changes how the gene is expressed, leading to missing or altered protein production. It's like a surprise ingredient making its way into your favorite recipe and changing the flavor completely.

For instance, researchers found that some deep intronic variants (those hidden within non-coding regions) could lead to cryptic exons being included in the final RNA product. This little twist in the genetic mix could result in a significant change in how a protein functions, sometimes leading to disease.

The Test Drive of New Methods

The researchers used a combination of genome sequencing and RNA sequencing to identify these tricky genetic variants. When they examined the RNA product, they could see changes in the gene expression that standard tests might have missed.

In some cases, they confirmed these findings with additional tests, like a mini-gene assay. This involved creating a little experimental gene segment to test how the original gene affected the RNA produced. The results were fascinating and often revealing.

Structural Variants: Another Missing Piece

Besides cryptic exons, the research highlighted structural variants—larger changes to the architecture of the genome. Some previous tests had missed these significant rearrangements. For example, a child with developmental issues had a deletion in a gene that could explain their symptoms, but it hadn’t been caught in earlier screenings.

Differences in Diagnostic Rates

As the analysis continued, researchers discovered that syndromic cases (where multiple symptoms are present) had the highest success rate in finding genetic explanations. When looking at non-syndromic cases (where symptoms are isolated), the rate of solved cases was much lower.

Interestingly, when breaking down non-syndromic cases by the affected organ systems, those with cardiovascular issues showcased a notably higher diagnostic yield. This hints at the importance of understanding the entire context of a patient's symptoms.

A Path Forward

While the results were promising, researchers know there's still work to be done. The tools available are continually improving, and they're optimistic that future advancements in genetic testing will help close the gap even further.

They also suggest that in some cases, it might be more cost-effective to analyze data from previous tests rather than jumping straight to new tests. Knowing when to re-evaluate past tests could help families find answers more quickly.

Conclusion

The world of genetic testing is rapidly evolving. As scientists and researchers work tirelessly to uncover the secrets of our DNA, the hope is that more individuals suffering from rare genetic diseases will finally get the answers they’ve been searching for.

So, whether it’s finding those sneaky cryptic exons or recognizing the value of new testing methods, the future looks brighter for those caught in the maze of unknown genetic disorders. After all, in the search for answers, sometimes the most surprising findings come from the places we least expect!

Original Source

Title: Genome Sequencing reveals the impact of non-canonical exon inclusions in rare genetic disease

Abstract: IntroductionAdvancements in sequencing technologies have significantly improved clinical genetic testing, yet the diagnostic yield remains around 30-40%. Emerging sequencing technologies are now being deployed in the clinical setting to address the remaining diagnostic gap. MethodsWe tested whether short-read genome sequencing could increase diagnostic yield in individuals enrolled into the UCI-GREGoR research study, who had suspected Mendelian conditions and prior inconclusive clinical genetic testing. Two other collaborative research cohorts, focused on aortopathy and dilated cardiomyopathy, consisted of individuals who were undiagnosed but had not undergone harmonized prior testing. ResultsWe sequenced 353 families (754 participants) and found a molecular diagnosis in 54 (15.3%) of them. Of these diagnoses, 55.5% were previously missed because the causative variants were in regions not interrogated by the original testing. In 9 cases, they were deep intronic variants, 5 of which led to abnormal splicing and cryptic exon inclusion, as directly shown by RNA sequencing. All 5 of these variants had inconclusive spliceAI scores. In 26% of newly diagnosed cases, the causal variant could have been detected by exome sequencing reanalysis. ConclusionGenome sequencing overcomes multiple limitations of clinical genetic testing, such as inability to call intronic variants and technical limitations. Our findings highlight cryptic exon inclusion as a common mechanism via which deep intronic variants cause Mendelian disease. However, they also reinforce that reanalysis of exome datasets can be a fruitful approach.

Authors: Georgia Pitsava, Megan Hawley, Light Auriga, Ivan de Dios, Arthur Ko, Sofia Marmolejos, Miguel Almalvez, Ingrid Chen, Kaylee Scozzaro, Jianhua Zhao, Rebekah Barrick, Nicholas Ah Mew, Vincent A. Fusaro, Jonathan LoTempio, Matthew Taylor, Luisa Mestroni, Sharon Graw, Dianna Milewicz, Dongchuan Guo, David R. Murdock, Kinga M. Bujakowska, Changrui Xiao, Emmanuèle C. Délot, Seth I. Berger, Eric Vilain

Last Update: 2024-12-26 00:00:00

Language: English

Source URL: https://www.medrxiv.org/content/10.1101/2024.12.21.24318325

Source PDF: https://www.medrxiv.org/content/10.1101/2024.12.21.24318325.full.pdf

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to medrxiv for use of its open access interoperability.

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