The number of Mendelian disorders for which the genetic basis has been identified is increasing rapidly. This is due to technologies that have made

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3 The number of Mendelian disorders for which the genetic basis has been identified is increasing rapidly. This is due to technologies that have made locus identification less laborious and time consuming. Of course, this is a good thing: increased understanding of the molecular basis of Mendelian disorders allows for precise diagnosis, genetic counseling, and prenatal testing. It also opens the door for basic research, which in turn may lead to identification of therapeutic targets. Some geneticists have stated that it should be possible to identify all Mendelian disorders in the next 5 10 years. This may be optimistic, as we are not even certain how many unmapped (and as yet unidentified) Mendelian disorders there are. 3

4 However, the ability of the Genetics team in the clinic, to recognize and diagnose this increasing number of disorders has not kept pace with this rapidly increasing fund of knowledge. It is impossible to remember all the disorders that are out there. Therefore, a few fundamental questions arise: Can a geneticist diagnose a patient with a condition that he or she has never heard of? Maybe not. Also, can a geneticist diagnose a condition that presents in an atypical manner? Or with conditions presenting in a milder, or more severe manner than usual? I think in many cases a geneticist may great difficulties. Clearly, innovative approaches are needed. In the face of too much information to remember or comprehend, information technology should be employed to assist. 4

5 We embarked on a project to improve on our diagnostic abilities in cases where there is consanguinity or inbreeding. We hope that this can serve as a model on how to move our field forward. Consanguinity is an ancient cultural practice, in the past quite common all over the world, but still common among about 20% of the world s population. Geneticists have known for a long time that offspring of consanguineous families are at a somewhat increased risk for autosomal recessive disorders, and are aware that these disorders are often rare and puzzling. They also may remain undiagnosed, even after great diagnostic effort. And this diagnostic odyssee can be stressful for all involved. Geneticists are also aware that once the molecular basis of the disorder is identified, the mutation identified tends to be homozygous. As a matter of fact, this is so well known, that such families, may be enrolled in projects to identify the causative locus using homozygosity mapping. So, the question is therefore, if we can use homozygosity mapping for locus discovery, why don t we use it for diagnostics? We will come back to that shortly. First, a brief introduction to terms used in the setting of consanguinity. 5

6 The reason that the mutations typically are homozygous, is illustrated in this cartoon by Dr. Alkuraya. The proband with a disorder has parents who are first cousins. The cartoon illustrates how a heterozygous mutation (denoted by a star) in a certain locus can be passed on, down the generations. But, more importantly, it illustrates also that this particular mutation is passed on not by itself, but as part of a region, or a haplotype (depicted in blue). One can also appreciate that this region becomes shorter and shorter, due to recombination. In the end, in this cartoon, the affected child has inherited the mutated allele from both parents, as well as the surrounding region (depicted within the red box). Such a region is known as a run (or region) of homozygosity. 6

7 So, in a consanguineous union, the offspring has various runs of homozygosity. The total length of these runs of homozygosity is a function of the degree of relatedness of the parents, and can be calculated as the product of the coefficient of inbreeding F and the size of the human genome. For example, the calculated total runs of homozygosity for the offspring of 1 st cousins is on average 200 megabase. 7

8 If the offspring of a consanguineous union is evaluated for a genetic, likely autosomal recessive disorder, it is reasonable to assume that the gene, with alleles containing homozygous mutations, maps to somewhere within these runs of homozygosity. 8

9 The genomic Single Nucleotide Polymorphism (or SNP) array can identify these runs of homozygosity. So, if a SNP array is useful in the setting of a consanguineous union, why is this relatively inexpensive technology not used more often by medical geneticists for diagnostics? Our experience is that, if there is consanguinity, the dataset generated by the SNP array is large, and the number of genes mapping to these runs of homozygosity many. The interpretation of the SNP array is therefore difficult, time consuming and often intimidating. Against this background we hypothesized that the availability of a user friendly SNP array evaluation tool would make the SNP array more useful to clinicians. We have presented some aspects of this approach last year, and have since improved our evaluation tool. I want to take you on a little trip through the evolution of our evaluation tool. First, I will provide you with a straightforward example illustrating the concept in a case where the diagnosis was obvious, but where the causative locus was unknown due to genetic heterogeneity. 9

10 Here is a sample of a SNP array report, for those of you that may not be too familiar with these reports. This is a sample from LabCorp, but reports tend to be very similar. First you see some information on the array. There are 2 million and six hundred ninety five thousand genotyping targets, and you can see to the right, in the red block that of these targets 743,000 are SNP probes, the rest are oligonucleotides. This is a hybrid oligo and SNP array. Back to the right, you see under diagnosis: normal female dosage, and then long contiguous regions of homozygosity in multiple chromosomes. The word multiple is important, because if there were only one long region of homozygosity, it would be suggestive of uniparental isodisomy. There are no other findings, as the array obviously would also have picked up genomic losses and gains. Then the next box is the interpretation: Apparent common descent. The next box explains the level of parental consanguinity, here 2 nd degree relatives, with a total of 382 Mb of regions of homozygosity. 10

11 This a family with 1 st cousin parents. They have 5 live children, of which 3 are affected by Bardet Biedl syndrome, manifesting the diagnostic clinical features of this condition. But, what is the molecular basis of the condition? There are now 16 loci associated with the Bardet Biedl phenotype. Therefore, a SNP array was requested for the youngest of the three patients. 11

12 As expected, the SNP array showed various runs of homozygosity. If the assumption of Bardet Biedl syndrome being due to homozygous mutations is correct, the locus should map to one of the many runs of homozygosity. From database as NCBI and OMIM we can easily extract where in the genome the currently known BBS loci map. 12

13 Here we see the BBS loci depicted in blue arrows. One can appreciate that only one of the BBS genes maps to a region of homozygosity, the one on chr14q. Based on these findings, sequencing of TTC8 was ordered, and a homozygous pathogenic mutation identified. I hope you agree that this is reasonably straightforward and cost effective. 13

14 So, our conclusion is that the SNP array is helpful in identifying the causative locus, if one is dealing with a known condition, but complicated by genetic heterogeneity. But could this type of approach help if the condition is not known? If all we have is a few clinical features? This is where we came up with an idea. 14

15 The concept like this: We have identified the various runs of homozygosity by SNP array, here depicted on chromosomes 1, 5, 7, 11, 18 and 19. For simplicity, we highlight only one region of homozygosity, but the same applies to all. Since we know the coordinates of the region of homozygosity, we can now identify all genes that map to this region here depicted as Gene 1 to 17. From these genes, we can select genes that have some form of annotation in OMIM these are the genes we know something about. We can then further select from within the OMIM annotated genes those that are known as Morbid Map genes these are genes that produce a phenotype, if mutated. We then make the switch from gene to the associated disorders using the fact that these can be linked through OMIM. Within these disorders, we then select those that are inherited in an autosomal recessive manner. Our thinking was, that if this can be accomplished, one should be able to review all these disorders, and pick the one that matches the patient s phenotype. 15

16 So, here is an example: A 7 yr old male was evaluated for concerns for developmental delay. Pedigree revealed that parents were 2nd cousins. Physical examination was essentially normal, except for mildly coarse features. SNP array revealed 3 runs of homozygosity on chromosomes 9, 10, and 17, totaling 38 megabase. 16

17 We entered these runs of homozygosity into our newly developed tool, and selected the units and the human genome version used in the SNP array report here kilobase and human genome assembly version 18. We then picked the query type, here runs of homozygosity, and selected a search for recessive disorders only. Once all these selections were made, we clicked the submit button to start the search. 17

18 As you can see, the search identified 25 genes that have an association with an autosomal recessive disorder.so, now we can review these disorders individually. The first disorder that we review is oculocutaneous albinism, clearly not the disorder we are trying to identify. The second one is Manitoba oculo tricho anal syndrome, and if one had never heard this syndrome (like me), one could briefly review the clinical synopsis for Manitoba oculotrichoanal syndrome, just to make sure that this condition does not need further consideration, by clicking on some of the links shown. A review of these 25 disorders will probably take 5 minutes. But of course, this example was picked because there is only 38 Mb of homozygosity. What if the parents were first cousins, and there was 200 Mb of homozygosity? Then it would take about an minutes. So, the questions arises, can one accomplish this task even faster? 18

19 Our thinking was that in principle it could. Since we have already shown that we can identify which ones of the MorbidMap disorders are recessive (by using the OMIM Clinical Synopsis), we should be able, in principle, to search for clinical features of our patient with the clinical features in the OMIM clinical synopsis of these identified disorders. So, we decided to try to add this option to our tool. 19

20 Having implemented this, in the example that you are already familiar with, we decided to search for keywords coarse AND (delay OR retar*) using this newly added ability, searching within the runs of homozygosity for genes, that are associated with recessive disorders, for disorders that have coarse features and delayed development as part of their phenotype. Having entered these few data, we clicked the submit button. 20

21 Now, using the genomic SNP array evaluation tool, only 1 established, OMIM annotated recessive disorder fitting the clinical search was found to map to these 3 runs. Sanfilippo type B syndrome was the only condition matching the patient s signs and symptoms. Subsequently, the diagnosis was confirmed by enzyme studies and by identifying homozygous pathogenic missense mutations in NAGLU. 21

22 So, we can conclude for now that we have accomplished a tool that allows us to filter from the genotype end by using the identified runs of homozygosity, and from the phenotype end by using the OMIM clinical synopsis for relevant clinical features. Having reached here, we felt that the genotype search is as good as it can be, but that the clinical feature search required closer evaluation. So, let us now turn to the annotation of clinical features in the OMIM Clinical Synopsis 22

23 The OMIM Clinical Synopsis is a dataset of clinical features of a certain condition organized under certain categories and subcategories, as seen here in Glucocorticoid deficiency 2, known as OMIM# Under the category Inheritance it states autosomal recessive. This is how we can search for AR disorders associated with genes mapping to the ROH. Then there are several descriptors. In this case, there is a Category of Laboratory Abnormalities, under which several entries can be found, for example hypoglycemia, elevated plasma ACTH, and low plasma cortisol (in BLUE). Then there are other entries, as normal renin and normal aldosterone, in RED, that do NOT describe the phenotype of glucocorticoid deficiency, as they are NORMAL, though still listed under the Category Laboratory Abnormalities. This leads us to the next topic, and I run the risk of sounding overly critical of OMIM. So, before we move on, I would like to state that OMIM is an immensely important collection of data, and that without it, the discipline and practice of Medical Genetics would be next to impossible. 23

24 OMIM searches can be really rewarding, but also frustrating. For example, if one searches for disorders associated with the phenotype blindness in the clinical synopsis, 237 hits are returned. On the other hand, if one searches the clinical synopses for the phenotype blind only 96 hits are returned. Then, if one searches for blind OR blindness, 243 hits are returned, suggestive of significant overlap between the entries, but also illustrating that they are not identical. If one scrutinizes the hits, there are unusual, and certainly unintended ones. For example, Treacher Collins syndrome is considered a hit, as there is an entry under the Subcategory Ears of blind fistulas. Maybe even more frustrating, the condition retinitis pigmentosa inversa with deafness is also considered a hit, as there is an entry under the Category Eyes (the correct category) of No night blindness. Now, to be fair, most of these issues can be addressed from within OMIM. But, I think it is also fair to say that the way OMIM is currently set up lacks sensitivity and specificity. And of course, when doing an OMIM search, or if using our evaluation tool, the last thing one needs is to miss out on relevant hits due to the very complicated search interface and ambiguous terminology employed by OMIM. We feel that this is a very important shortcoming in OMIM. So, now that we know this, what can be done about it? 24

25 In a way, the solution is already available. A group at Charite Hospital in Berlin has been working for quite a few years on a hierarchically structured database, that uses OMIM descriptors, and has converted these into what they call HPO terms, named after the project Human Phenotype Ontology. Currently, the database has almost 10,000 HPO terms, all linked in a tree like manner as displayed in the cartoon on the right. So, an atrial septal defect is_an abnormality of the atrial septum, etc. becoming less specific going up the tree. For example, a ventricular septal defect would also be a subset of a cardiac malformation. The OMIM terms blind and blindness have now the same HPO term. So, we set out to incorporate HPO into our tool, and have been reasonably successful in doing this. HPO kindly allowed us to use their database. 25

26 Having incorporated HPO into our tool, we can now better define our phenotype. Here is a snapshot of part of HPO (it is a little small but I will walk you through it). We use as example the case of a 16 yr old female patient with abnormal palpebral fissures and multiple contractures. To start, we search the HPO database for palpebral, and identify the HPO term abnormality of the palpebral fissures. Since we feel that this is the best descriptor of the pateint s feature, we drag and drop this HPO term into our search box. We can add other search terms, and connect these with Boolean operator AND, OR, NOT. Here we combine the search terms Contractures and Abnormality of the palpebral fissures. To complete the search strategy the user then clicks Finish, and moves back to the SNP array search page. 26

27 So, now we need to complete the search page. This patient with the findings of multiple contractures and abnormal palpebral fissures, has 5 runs of homozygosity, on chromosomes 1, 4, 11, 15 and 20, as seen in the upper text box, totaling 141 Mb. The coordinates are set in Mb, and human genome version 18 was used in the SNP array report, so these are ticked. We set the search for runs of homozygosity and look for recessive disorders, that have as part of their phenotype the mentioned combination of HPO search terms: contractures and abnormal palpebral fissures. We now submit the search to the tool. 27

28 Seconds later, we get the output page, shown here. Let s go over this page, with the relevant areas highlighted here. If doing an OMIM search using the mentioned combination of 2 HPO search terms (in red), we get 36 hits in OMIM. And we can review these 36 entries. However, confining the search to the identified 5 ROH, we are left with only 3 of these 36 entries. Now, having reduced the number of disorders to be considered to 3, the user can now review these 3 hits. In this case, the conditions to consider are 1. the contractural type of Ehlers Danlos syndrome, 2. arterial tortuosity syndrome, and 3. congenital disorder of glycosylation 1E. I will leave this case without its conclusion, as more details can be founds in abstract #211, presented as poster on Thursday by Dr. Ammous. Also, a more in depth description of the HPO addition to our tool can be found in abstract #206, presented as a poster on Friday by Mr. Winters, one of the 2010 ACMG Foundation Summer Scholars. 28

29 I haven t spent much time on copy number variants. Especially useful in non recurrent microdeletions, identified by oligoarray or by SNP array, one can use the same strategy looking for relevant genes within the deleted region. Of course, one would be most interested at first in dominant conditions. The tool is also very useful for counting the genes that are deleted and for quickly educating oneself about the relevant genes to consider within a deleted region. To summarize, here is a cartoon of the evaluation process as previously discussed. The process starts with the evaluation of a patient with a disorder that cannot easily be diagnosed. A pedigree will help to determine if there is consanguinity or inbreeding. If so, the user can identify the runs of homozygosity by SNP array. Once these are known, one can use our tool to come to a short list of disorders to be seriously considered based on clinical features using OMIM or HPO. The user can then review the short listed disorders, rank them. The user can then strategize further studies, by doing more targeted testing, which may include sequencing of relevant genes, performing biochemical tests, and obtaining X rays or even biopsies. The process is completed if a diagnosis is reached. If no diagnosis is reached, the user needs to consider where the process went wrong, by reviewing the assumptions made. Of course, it is possible that the disease is not a genetic disorder, not a recessive disorder, or recessive, but due to heterozygous mutations. One needs to consider whether any runs of homozygosity went unreported. The laboratories offering clinical SNP arrays typically only report runs of homozygosity larger than 8 or 10 Mb, but will provide shorter ROH on request. Actually, we always request these short runs of homozygosity, and search through these as well, as our tool can evaluate them all in one exercise. Then, of course, the clinical features may have been picked poorly, and some permutations of search terms should be considered. And, lastly, a real possibility is also that the disorder IS recessive, that the locus MAPS to the runs of homozygosity, but has not been described or identified yet. In this scenario, NOT having found the disorder within the runs of homozygosity, makes it in our opinion more likely that the disease is unknown or that the responsible gene has not yet been mapped. Such cases would make good candidates for enrolment into a whole exome sequencing project. We have already embarked on a few whole exome sequencing projects based on exactly this scenario. 29

30 I d like to thank my collaborators Dr. Jiang and Dr. Tsinoremas from the University of Miami Center for Computational Science. Dr. Jiang is an expert in bioinformatics, and he singlehandedly wrote the code, constructed the database and designed the website. Dr Jiang and I are always interested in feedback and suggestions. Our website is also easily found using a Google search. For now, the HPO addition is an evaluation version, and not as yet available online to users. Thank you for your attention. 30