Empowering Precision Medicine: The power of WES and mitochondrial DNA analysis

So good evening, hello everyone, and welcome to today's educational webinar titled Empowering Precision Medicine, The Power of Whole Exo Sequencing and Mitochondrial DNA Analysis. The webinar is brought to you by Blueprint Genetics, a genetic knowledge company committed to providing an innovative approach to genetic testing and to ensure accurate and confidence in your clinical practice. My name is Tina Vuopio and I have the privilege of hosting today's webinar. Please submit any questions you may have in the QA Box. You can submit them throughout the webinar and we will answer as many as possible at the end. We are very excited to have two speakers today, Doctor Kirsty Wells and Doctor Raquel Perez Caro. Doctor Kirsty both is a team Manager at Blueprint Genetics, specialized in whole exome sequence interpretation and reporting. Kirsty's background is both research and diagnostics. Before joining Blueprint Genetics in 2018, she completed both PSD and post doctoral research fellowships and undertook in depth training in genetic diagnostics in the UK's National Health Service. Doctor Raquel Perez Caro is a geneticist at Blueprint Genetics, specialized in whole exome sequencing, interpretation and reporting and is actively involved in this related projects. She completed her PhD in retinal dystrophies and with over 13 years of experience in the field of Human Genetics, she has developed a deep understanding of different molecular genetic techniques to study inherited diseases, with a particular focus on NGS approaches. Thank you for being here. Thank you, Tina. Thanks for the nice introduction and good evening everyone. Or, or Good morning. Really nice to see so many online today. Thanks very much for joining us. As Tina said, my name is Kirstie Wells and I'm a senior geneticist in the clinical Interpretation team at Blueprint Genetics. And I'm joined by my colleague from the clinical interpretation team today, also geneticist Raquel Perez Caro. And yeah, we're really happy to be here today to talk to you about our favorite topics, whole XM sequencing and mitochondrial DNA analysis. First of all, let's start with a quick introduction to Blueprint Genetics. So Blueprint Genetics is a Quest Diagnostics company and we provide high quality genetic testing to the global clinical community. We have over 250 employees and we serve over 4000 clinicians in 70 countries with core operations in Helsinki in in Finland. And we offer a range of NGS based test types depending on on the customer's needs. And today we'll focus on whole exome sequencing, today's webinar where we're going to divide into two parts. So first of all, I will talk to you about recent experience in our lab with an optimized whole exome sequencing assay. And then I'll hand over to Raquel who will talk to you about combining whole exome sequencing with mitochondrial DNA analysis. OK, but let's get started first of all and talk about how optimized whole exome sequencing can help unlock answers for patients. Let's start with a bit of an introduction to get everyone on the same page. What is whole exome sequencing? Well, Wes as it's affectionately known, is aiming to sequence the protein coding regions of all known genes and there are over 20,000 of them. Important to note that that Wes is distinct from clinic clinical exome where you sequence disease associated genes only. With a Wes you aim to sequence all genes regardless of whether they have a disease association at that time. Wes covers about 1 to 2% of our DNA because of course the majority of our genome is is non coding. But nevertheless Wes can be a very powerful diagnostic tool because there there is no restriction on what genes are sequenced. And for that reason, it can be especially powerful for patients who have complex phenotypes or an unclear clinical diagnosis. For patients like this, it may have been difficult to know what what gene panel to choose or what targeted tests to go for. So a comprehensive test like where's can be really useful for patients like this. And also where's we find can be really useful for patients who have had previously inconclusive genetic testing. And in our experience that blueprint genetics when when we find a relevant variant on whole exome sequencing when previous genetic testing has been negative. This is often because there have been technical limitations of the previous testing. It can also be because the patient has an an atypical presentation. So again, it may have been difficult to know what more targeted tests to choose for that patient. Or something else that we often see is that the relevant variant ends up being in a gene with a very new gene disease association. And of course, genes like this may not be on panels unless the the gene panel has been updated really recently. So yeah, Blueprint Genetics, our aim is to optimize our whole ectone sequencing assay so that it does the best possible job at identifying relevant variants in patients just like like this. So patients with a complex phenotype, unclear diagnosis or inconclusive previous genetic testing because these are exactly the kinds of patients that that end up coming for whole exome sequencing. There are many elements to an optimized Wes and today I would like to focus on a couple of the the elements of technically optimizing whole exome sequencing. First of all, the importance of including clinically relevant non coding variants on the assay. I'll also talk about optimizing copy number variant detection on Wes. Then I'll talk about the importance of having up to date gene disease associations in the system and of course the importance of skilled variant interpretation that's done in such a way that relevant variants are identified even in patients with maybe atypical or or kind of rarely reported presentations. OK, so that's what I'd like to focus on today. But first of all, I'd like to talk about Wes trios and the importance of running your Wes analysis as a trio if possible for the best possible outcome. So at Blueprint Genetics, we really believe in the power of running whole exome sequence analysis as a trio. So by trio I mean the typical arrangement is an affected patient and both unaffected parents. So by running the analysis in this way, it kind of facilitate the interpretation, it facilitates de Novo variant detection. And we find this is very important because based on our internal data, we see that approximately 50% of molecular diagnosis in West cases are de Novo variants. Having the parents in the analysis enables the determination of variant phase if you have two variants in the same same gene and it can give you recurrence risk information and a more accurate variant classification straight away without having to do necessarily any further family member testing. So, so you can likely complete all the testing that you need to do in one step. So let's first of all take a look at a case to illustrate the, the value of, of running the Wes analysis as a trio. And by the way, all the cases that I, I show today are based on, on real cases from the Blueprint Genetics archive, but with details heavily changed or removed to, to make sure patient confidentiality is, is protected. So in this case, we had a small child with psychomotor delay hypertonia and mild dysmorphic features and there were no affected family members and the analysis was run as a Wes trio including both unaffected parents and and the affected affected pro band. The analysis identified a number of rare heterozygous mis sense variants in genes associated with autosomal dominant neurodevelopmental disorders that could overlap with this patient's reported phenotype. And this is not an unusual situation in a Wes analysis that you would identify this kind of number of rare mis sense variants that could fit with the patient's phenotype. Especially if the clinical history that you have is is maybe somewhat limited or quite non specific. And this illustrates the difficulty often with a pro band only analysis without having the inheritance information and it can make the interpretation quite challenging. And what you may end up getting with a pro band only case with with variants like this is that you you end up reporting a lot of variants of uncertain significance and then there is a lot of family member testing to do later on to try to resolve those variants of uncertain significance. In this case though, we had the parental data, so that meant we could immediately see that the KMT 2A variant here was de Novo. It was not detected in either parent and all of the other MIS sense variants there were inherited from unaffected parents, which meant that we could discard them and inherit report the KMT 2A de Novo variant as likely pathogenic. So this patient received a molecular diagnosis of KMT 2A related disease due to a de Novo variant which comes with a low recurrence risk. So this finding facilitates genetic counselling and management. So running this analysis as a trio, including the parents in the analysis really facilitated the interpretation. It led to a simpler report, gave us an immediate likely pathogenic classification for that KMT 2A variant because the fact we could see it was de Novo allowed us to classify it as as pathogenic, likely pathogenic according to our scheme. And the testing was completed in one step. So there was no subsequent family member testing needed to resolve any of those VU s S or to get the KMT 2A variant upgraded to likely pathogenic. So running the analysis as a trio here really made the difference. And all of the cases that I'll show you today actually are our trio waste cases. We we really believe in in the value of that approach if it's possible. OK, let's move on now to some of the elements of technically optimizing whole exome sequencing starting with the value of including clinically relevant non coding variants on the assay. Let's first of all take a moment to think about where in genes we find disease causing variants. And as we all know, the majority of known disease causing variants are in coding regions of genes, so in the exons and also disease causing variants are very frequently found in the splice regions so close to the exon intron junctions. But also of course we know these days that there are many disease causing variants in non coding regions such as in introns and also other non coding regions like UT, Rs and and promoters. So for that reason we target specific clinically relevant non coding variants on our whole exome sequencing assay. We do that by adding additional capture oligos to the assay which target specific non coding regions where there are potentially clinically relevant variants. And what I mean by that is non coding variants that are that have been reported in the literature in patients or are nearby variants that have been reported in patients or variants that have some kind of functional evidence supporting them. And the key here is that these variants that were non coding variants that we're targeting, we are potentially able to interpret them. So there is some evidence out there allowing us to interpret them and giving us a fighting chance of being able to classify them as perhaps slightly pathogenic or higher. So doing this enables us to detect almost 2000 non coding variants on our whole exome sequencing assay. And we believe this is powerful because based on our internal data approximately 1 in 90 of positive WAS cases involve a non coding variant. So this kind of variation is clinically very relevant. Let's take a look at a case to illustrate the value of of of having these non coding variants on where's this case involved a child with muscular dystrophy. And again the analysis was run as a trio including both healthy parents in the analysis. So the the Wes identified non coding variant in the Col. 6A1 gene. So this was the de Novo variant and it's a known pathogenic variant that it's that's been reported in in multiple patients with autisomal dominant collagen 6 deficient muscular dystrophy. So this is a good match with the patient's reported phenotype. And this variant is located deep within intron at 11 of of Col. 6A1. So if you think about a a kind of out-of-the-box whole exome sequencing assay if if such a thing exists. But but all, all a Wes needs to do to be kind of to, to be defined as a Wes is to sequence the coding regions and often the, the splice regions may be going 20 base pairs into the intron is covered. So kind of a standard Wes like this would not detect this deep intronic pathogenic variant in Col. 6A1. But in our Wes are saying we specifically target this region of intron 11 in Col. 6A1 because we know there is this variant located there based on the literature and we want to be able to detect it. So that's how we were able to detect this variant on Wes in this patient. So this patient received a molecular diagnosis of of Col. 6A1 related disease due to a de Novo variant facilitating genetic counselling and management. And and this is thanks to the inclusion of clinically relevant non coding variants on Wes and not all, not all whole XM sequencing assays would detect this variant. Let's take a look now at the value of optimizing CNV detection on whole XM sequencing and other important technical optimization. So we detect the MVS from NGS data using a method called read depth mapping. And this is when you take the, the number of sequencing reads that you observe in a particular region and then that's compared with the number of reads you would expect in that region. And if you see a significant difference between the A call then that between the two, then that generates ACMVE call. That's a very, very simplified way of explaining that. But that's the method that we use. And we have 3 complementary bioinformatic tools that we use to to call CMVS from the NGS data. And the point is we these all work together and deep and uniform coverage across the whole whole exome is, is really key for these tools to work, work optimally. So we ensure we have good coverage on our ways as well. And this has a knock on effect that we get really good CMVCMV calling and CMVS are really important clinically. This is very well known. Based on our our internal data, we see that one in seven positive wears cases involves ACMV and about 2010% of those involve a very small CMV of less than 1000 base pairs. And and these very small CMVS can be quite challenging to detect, but are clinically very relevant. So it's important to have good, good CMV detection on wears. Let's take a look again at a case to illustrate the value of this. This case involved a deceased fetus with multiple anomalies, including shortening of long bones and micronathia, and a previous skeletal dysplasia panel had been negative. And again, the case was run as a trio. The analysis identified compound heterozygous variants in the fetus in a gene called COG 5. One of the variants was a known pathogenic sequence variant in the gene and the other one was a single exon deletion in the gene. And these variants were inherited from different parents. So they were in compound heterozygous state in the in the fetus. So taking a closer look at this gene COG 5, the gene has been associated with with the autosomal recessive form of congenital disorder of glycosylation has a really variable broad phenotype and the phenotypic spectrum is has really been evolving over time. So originally bialelic variants in this gene were reported in a small number of adult patients with a history of developmental delay and ataxia. But then a larger group of patients were reported with additional features, including intellectual disability with a range of severity, short stature, microcephaly and dysmorphic features. And then even more recently, retinal degeneration and skeletal dysplasia have been added to the clinical spectrum. So, so, so the the kind of phenotype associated with with this gene has really been evolving over time. And actually the most recent publication regarding this gene describes a a fetus homozygous for a known pathogenic variant in this gene. And that fetus showed a severe fetal phenotype, which is actually very similar to the one described in our current current case. So that was that was very interesting taking a closer look at the actual variants identified here. So I said one of them was a known pathogenic sequence variant in the gene and the other one was a single exon deletion. This deletion actually involved an in frame exon. So it encoded 37 complete amino acids, so in frame and quite small encoding only 4.5% of the protein. So that means this deletion is is very small and and is in frame. So that means we can't classify it as a predicted loss of function variant. What we did see though is that there are a couple of pathogenic missense variants in this Exxon reported in the literature. So this is indicating that this Exxon encodes a functionally important region. The question is then how to classify this difficult small deletion. And when considering the the classification, we consider things like the phenotype match. So we believe that we had quite a good match between the the the fetal phenotype reported in our current case and also the one reported in the literature, also the frequency. So this deletion is absent in reference populations like nomad. It's assumed to affect a functionally relevant exon based on those pathogenic misense variants in in the region deleted. And also really importantly, because we've done a trio here, we can see that this deletion is in trans with a known pathogenic sequence variant in this in in this case. So based on all that information, we decided to classify the deletion is likely pathogenic after quite a lot of discussion within the team. Therefore, this family received a molecular diagnosis of COG 5 related disease for the fetus, which importantly comes with a 25% recurrence risk. And what really made the difference here? So a few things. First of all, having optimized CMV analysis on, on wares that allowed us to detect this, this, this very small deletion, these, these small deletions can be particularly challenging to detect. And, and the different bioinformatic tools and the good coverage made it possible to detect this deletion. And also careful and skilled variant interpretation to weigh up the evidence, classify this deletion, which was quite tricky to classify and also to go through the literature and identify a potential phenotype match here that maybe wasn't quite so obvious because this this fetal presentation is seems to be quite under reported. And then finally, of course, it was important here that trio Wes was done because it allows it allowed us to see that these two variants were in trans, which helped us to classify the the small deletion. OK, let's move on finally now to the importance of having up to date gene disease associations in in the system when you're analysing Wes. So a gene disease association refers to the relationship, if there is one, between a gene and a particular disease. And it's important to identify if there is an association between gene and disease and what the the strength of that evidence is supporting that gene disease relationship when you're classifying variants in that gene. So at Blueprint Genetics, we have a system for classifying gene disease associations, which is based on the Clingen framework. And in our W analysis, we aim to mostly focus on variance in genes with a moderate or above gene disease association because anything below that is not not not clinically relevant at least at that time. And we want to focus on on potentially clinically relevant findings in our whole exome sequencing. What we find is that up to date gene disease associations are really crucial for identifying relevant variants in Wes. So to illustrate that I have a case here of a young adult with dilated cardiomyopathy. And again, this case was run as a trio with both healthy parents, and the analysis identified compound heterozygous variants in a gene called NRAP. One was a frameshift variant expected to induce nonsense mediator decay, and the other one was a rare predicted deleterious missense variant. So interestingly, at least at the time of putting this presentation together, the NRAP gene does not have a disease association on OMIM or on other places like Orphanet or Gentiven or or Gen. CC. So it wouldn't appear to have a gene disease association except for the fact that a couple of years ago, bialelic loss of function variants in in this gene NRAP were reported in the literature as a cause of recessive dilated cardiomyopathy. SO11 unrelated probab were reported with with DCM and there was found to be significant enrichment of of of bialylic NRAP variants in patients with DCM and also there was a knockout zebra fish with a cardiac phenotype. So taking all of that evidence together, we would classify the the association between NRAP variants and DCM to be a strong gene disease association to have strong evidence behind it. So based on that and and also the the the parameters of the variants that we actually identified, so their consequences, their frequencies and the reported patients, we are also able to classify the two NRAP variants identified in in this patient as pathogenic. So the patient was compound heterozygous for pathogenic variants in NRAP, allowing us to report this molecular diagnosis of NRAP related dilated cardiomyopathy in in this individual, which importantly comes with a 25% risk to siblings. And the thing that really made the difference here is having an up to date gene disease associations in the system for this for this NRAP gene. So the system that we use to filter variants and organize the variants and and prioritize the variants knows that there is an association between NRAP and disease. So that means variants in that gene are not filtered out and and and are not de prioritized. So if you're using kind of gene disease associations to filter out variants or to prioritize them and the information on the gene disease associations is not correct, it's not up to date, then of course there's a danger that relevant variants will will be missed. So it's really important to have up to date gene disease associations in in the system and any system you have for analysing where's or or even whole genome is only as good as the gene disease association association information that that you have in in that system. OK. So to summarize, there are many elements of an optimized where's including performing trios where possible, technical optimization, making sure you have good up to date gene disease associations and all underpinned by skilled interpretation. And one element that I haven't spoken about yet is the inclusion of the mitochondrial genome on Wes and that's because it deserves a presentation all of its own. So I will hand over now to my colleague Raquel, who is going to talk to you about combining whole exome sequencing with mitochondrial DNA analysis.Thanks, Kirsty. Hi, everyone, and thanks for joining us today. And now I'm going to talk about mitochondrial diseases and the importance of integrating mitochondrial DNA analysis into a whole exome sequencing approach. Mitochondrial diseases are very complex both clinically and genetically, and that's why including high quality mitochondrial DNA testing to the workflow can make a real difference in identifying their underlying cause. So first a brief introduction to mitochondrial diseases. They are a group of genetic disorders caused by mitochondrial dysfunction and therefore defects in oxidative phosphorylation which leads to impaired energy production. One of the key features of this disease is that they can affect different systems or organs, especially the high energy demanding ones such as the brain, muscles or heart. And another hallmark is the significant phenotypic variability even among individuals within the same family and the progressive course of the disease complicating their diagnosis. These conditions can present with a wide spectrum of symptoms and some of the common ones include ataxia, optic neuropathy, here loss, cardiomyopathy, muscle weakness or diabetes. Mitochondrial diseases can arise from variants in mitochondrial DNA and these are maternally inherited as mitochondria are passed down exclusively from the mother, but also can arise from variants in nuclear DNA, in nuclear genes affecting mitochondrial elements or empty DNA maintenance, and these conditions can follow anatosomal recessive, autosomal dominant or X linked inheritance patterns. The mitochondrial genome has distinctive characteristics that entirely impact how we interpret mitochondrial variants. It's a circular double stranded DNA molecule. There are 37 genes including SUV units of respiratory chains, complexes but also RNAs and trnas which are essential for mitochondrial proteins in disease. The mitochondrial genome lacks intronic regions and about 93% and 9093% of the sequence it's coding, except for these significant non coding region. Here the mitochondrial genetic code differs slightly from the nuclear code, and this is very important or relevant when annotating and interpreting mitochondrial variants. Another important aspect is that is a polyploid, meaning that each cell contains multiple mitochondria and each mitochondria contains multiple copies of mitochondrial DNA. And this leads to the concepts of homoplasmy, where all or the vast majority of the copies are identical or heteroplasmy, where there is a mixture of normal unmutated mitochondrial DNA are found in a cell. And the heteroplasma level can vary between individuals, but also between different tissues in the same individual. And this is known as a tissue specificity. And this can significantly influence the disease expression. And let's talk now about the technical insights. Currently the next generation sequencing is the new gold standard technique for mitochondrial DNA sequencing. But which are the main challenges we face in mitochondrial DNA testing? So on the one hand, we have the technical challenges and this include ensuring an uniform coverage and high sensitivity. Also, the alignment and mapping processes can be complex and may lead to false positives or misinterpretation of the variance if not handled properly due to the nuclear mitochondrial DNA elements or norms which are mitochondrial fragments that have been integrated into the nuclear genome. And also it's important are reliable detection of very low level heteroplasmy, which can be clinically relevant and be different in different tissues. And on the other hand, we also face some challenges during the interpretation process. There is a limited population data available for mitochondrial variants. The evidence in in the literature can be inaccurate. Many publications lack critical information such as the heteroplasma levels, the tissues tested, or detailed clinical descriptions. The variability in phenotypes or heteroplasma levels even among individuals covering the same variant makes the interpretation challenging and also we need specific classification guidelines for MiTo variants. At Blueprint, our whole exome includes a high quality Mt DNA analysis. The mitochondrial genome is sequenced in parallel with nuclear genome. We cover the whole mitochondrial genome with an uniform coverage. Now we are able to detect all types of variants, SMVS in this but also CMVS and for that we have a specific bioinformatic pipeline to determine the breakpoints. And also our assay has a high sensitivity and good repair to reliably and detect low level etraplasmes. And for those we use a specific variant color that is typically used for semantic variation and these are our performance statistics. And as you can see our mean read that is more than 9000 reads. And regarding heteroplasmic detection capabilities, we are able to detect SMBs in this or CMBS with a high sensitivity at a very low heteroplasmic level at Blueprint. Also we have a large in house population data which supports more accurate variant evaluation. We also carry out extensive little to review when a variant it's been evaluated and we carefully interpret not only variants, but also gene disease associations, heteroplasmic levels or disease thresholds, phenotypes. And we also have a systematic classification scheme for mitochondrial variants and why it's important to include mitochondrial DNA analysis in exomes. Well, as Kirstie said, many of the cases referred for was present with complex phenotypes or have unclear clinical diagnosis or a typical presentations that don't really point to a specific syndrome or or disease. And these are precisely the kind of cases where mitochondrial diseases might be hidden or misdiagnosed. Because as we have mentioned at the beginning, mitochondrial diseases are very challenging as there is a high penalty people ability. They follow our progressive course and they can mimic other conditions and making the diagnosis particularly complex. And now I'd like to show you some internal data on our mitochondrial financing. W The mitochondrial findings contributed to 1.3 to 2% of diagnostic cases. On the right hand side, you can see the most prevalent genes. In 70% of the cases, pathogenic or Lili pathogenic MiTo variant was the only diagnostic finding. In 20% of the cases, the MiTo variant was found together with a nuclear variant and both were likely contributing to the phenotype. And in 10% of the cases, the MiTo variant's role was uncertain. And we are still gathering some W data to accurately evaluate the diagnostic impact. But the addition of the mitochondrial genome to our panels resulted in a 1.1 increase in diagnostic yield and here are the four recovering. This is causing variants in our cohort as well as the heteroplasma levels found and the phenotypes reported to our patients. And one interesting observation is the wide range of heteroplasma level found associated with the variant M324382G and the variability in in phenotypes in our affected patients. And this variability clearly highlights the complexity of the interpretation process and what does always mean for patients. Now I'd like to show you 3 interesting cases in which we are going to see the potential impact of including mitochondrial DNA testing and also reflect different aspects of interpretation and reporting processes. This is the first case. It's a baby who presented with epilepsy, apnea and suspected bradycardia and the patient had a previous epilepsy panel performed in another laboratory, which was inconclusive and the patient was referred for wastrio together with an affected parents. So we didn't find any diagnostic variant in nuclear genes, but when we analyzed the mitochondrial genome, we find we found this variant in MTATP 6. The heteroplasma was around 55% and as you can see in the picture, the variant was absent in mother's sample, which is consistent with likely the novel origin. And this variant was absent in population databases and it creates premature subcodon removing 23% of the protein. And interestingly, this variant was previously identified in a patient with Lane syndrome and the variant was found with a 21% heteroplasmine blood sample. And also this variant was detected by other laboratories in and submitted in Clinbar. And what about the phenotype? So variants in MTA TP6 have been associated with a highly variable phenotypes including LACE syndrome, NARP neuropathy, ataxia and breading spimentosa, upper motor neuron disease as well as other isolated manifestations. And the most common frequent. The most frequent symptoms include developmental delay, movement disorders, epilepsy, seizures, respiratory abnormalities including apnea and less difficult symptoms include mitochondria, cardiomyopathy and conduction defects and lactic acidosis. And as you can see highlighted in red, our patients features could be explained by this variant. And what about the classification? So let's summarize the evidence that we have. The variant is absent in databases. It creates premature stub:.It's been reported in the literature in a patient with mitochondrial disease. There is an established gene disease association and it's a good. There is a good clinical correlation with our patient manifestations. The HR plasma level found was consistent with what's been reported in the literature and the variant. It's likely they're noble. So we could classify the variant as pathogenic. So this patient received the molecular diagnosis of MTA TPC related mitochondrial disease. And as we analyzed the the parents, we could see that the variant is absent in mothers sample, which is consistent with likely the novel occurrence And this result enabled to provide genetic and appropriate genetic counseling to the patient and family. And what made the difference in in this case? First, the inclusion of mitochondrial DNA in West, which is particularly relevant in junk patients like this in which mitochondrial disease would not necessarily have been expected. And also having a high sensitivity and read the assay allow us to determine that this variant it's a likely de Novo and also having a classification scheme and doing a careful interpretation. The second case is a young patient who presented with hearing ambition laws, diabetes, brain MRI abnormalities, mild cognition concerns, and there were no family history reported. This patient had multiple previous genetic tests including mitochondrial tests performed in another laboratory. And all these previous tests were uninformative. So we didn't find any potential diagnostic bar, any nuclear genes. But what we found when we analyzed the mitochondrial genome. So as you can see in the picture here, we were able to identify 2 heteroplasma large mitochondrial deletions, one at 10% heteroplasma and the other one at 30% heteroplasma. We were able to determine by breakpoints in both of them. One includes 24 genes and the other 113 genes and none of them were previously reported. But several similar overlapping deletions were reported in the Tartar. So we were able to classify both as pathogenic. Large mitochondrial deletions are associated with mitochondrial DNA deletion syndromes, which include overlapping clinical phenotypes characterized by retinopathy, ophthalmoplasia, conduction defects, cognitive clients, human loss, muscle weakness, endocrinopathies or brain MRI abnormalities. And our patients features or manifestations are highlighted in red here as well. And as you can see it, there is a really good clinical correlation. So finally, this patient got a molecular confirmation of mitochondrial DNA deletion syndrome after a lifelong diagnostic odyssey, large mitochondrial deletions typically for the Novo and these results enabled to provide an appropriate genetic counselling to this patient. And what made the difference in this case? First, detection of mitochondrial DNA CMVS in exons and this is thanks to our customized clinical bioinformatic analysis and also having a high sensitivity and rate that say allow us to also identify the large deletion with lower heteroplasma level. And the final case is a baby who presented with microcephaly, global developmental delay, hypertonia, some brain MRI, some brain abnormalities and small optic nerves. This patient also had some previous genetic tests which were uninformative and a family history include another sibling with a small head side but normal brain examination. So analyzing nuclear genes, we found these two heterozygous missions variants in ASNS gene, one was classified as pathogenic and the other one as lightly pathogenic. And the face of the variants were unknown at the time. Variants in this gene are associated with asparagine synthetized deficiency and this is characterized by a congenital microcephaly, severe developmental delay, hypertonia, ******* quadriplasia and some brain abnormalities. And as you can see, most of our patients manifestations could be explained by this disease or this deficiency except for the small optic nerves. So we decided to continue the analysis in case there is an additional variant that could explain that feature. And what we found analyzing the mitochondrial genome was this homoplasmic pathogenic variant in Mt and the 6th. This is a really well established variant associated with levers hereditary optic nerve body or lawn. This condition is characterized by visual failure, reduced visual acuity sensors, scotoma, retinal vascular issues, optic atrophy and optic nerve dysfunction as well as other extra color pictures. So this variant could explain our patient's optic nerve issues. But we also wondered why our patient doesn't present the typical lung symptoms. So we investigated further the variant in the literature and what we found is that this variant is associated with best long term visual outcome. It causes less severe phenotype with visual recovery seen in some patients. The age of answers range from adolescents to adulthood and there is low preneutrans reported in the literature. And also some specific HAPS groups may influence the disease suppression. So a week on molecular findings in both nuclear and mitochondrial genome. So we could have that possible double diagnosis here for this patient, the mitochondrial finding was relevant and support an early ophthalmologic examination or follow up to this patient, which is clinically important as and this is a baby and some of the lung symptoms may appear later in life. And yeah, these results enable to provide an appropriate genetic counseling to the patient and family. And what made a difference in, in this case, basically a careful interpretation by doing a comprehensive variant assessment, taking the literature for checking heteroplasmic levels, phenotypes reported or even the penetrans, and also by elucidating genotype, phenotype correlations. And that's all for my part. But none of these would be possible without our wonderful colleagues at Blueprint. So we want to thank them all for their effort, dedication and passion. And thank you all for listening.