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  • 1
    Language: English
    In: American journal of medical genetics. Part A, 2018-04, Vol.176 (4), p.992-996
    Description: Robinow syndrome is a clinically and genetically heterogeneous disorder characterized by mesomelic limb shortening, distinctive facial features, and variable oral, cardiac, vertebral, and urogenital malformations. We identified the novel de novo splice acceptor mutation c.1715‐2A 〉 C in DVL3 in a 15‐year‐old female patient with typical features of Robinow syndrome. By studying DVL3 transcripts in this patient, we confirmed expression of both wild‐type and mutant alleles. Mutant DVL3 mRNAs were found to harbor a deletion of four nucleotides at the beginning of exon 15 and encode a protein product with a distinct −1 reading‐frame C‐terminus. The data demonstrate that mutant DVL3 proteins associated with Robinow syndrome show truncation of the C‐terminus and share 83 novel amino acid residues before the stop codon confirming highly specific DVL3 alterations to be associated with this syndrome. The phenotype of the Robinow syndrome‐affected female reported here is typical as she shows mesomelia and mild hand anomalies as well as characteristic facial anomalies. She also exhibited a supraumbilical midline abdominal raphe which has not been observed in other patients with Robinow syndrome. In contrast to the clinical data of four previously reported individuals with DVL3‐related Robinow syndrome, short stature was not present in this individual at the age of 15 years. These findings expand the clinical spectrum of Robinow syndrome associated with DVL3 mutations. To date, comparison of clinical data of DVL3 mutation‐positive individuals with those of patients with genetically different forms did not allow delineation of gene‐specific phenotypes.
    Subject(s): Abnormalities, Multiple - diagnosis ; Abnormalities, Multiple - genetics ; Adolescent ; Alleles ; Amino Acid Substitution ; Analysis ; autosomal dominant, DVL1 ; Codon ; Craniofacial Abnormalities - diagnosis ; Craniofacial Abnormalities - genetics ; Dishevelled Proteins - genetics ; DVL3 ; Dwarfism - diagnosis ; Dwarfism - genetics ; Exons ; Facies ; Female ; Gene mutations ; Genetic aspects ; Genetic Association Studies ; Humans ; Limb Deformities, Congenital - diagnosis ; Limb Deformities, Congenital - genetics ; Medical research ; Medicine, Experimental ; Mutation ; Phenotype ; Proteins ; Radiography ; RNA Splice Sites ; RNA Splicing ; Robinow syndrome ; Sequence Deletion ; Urogenital Abnormalities - diagnosis ; Urogenital Abnormalities - genetics ; Wnt signaling
    ISSN: 1552-4825
    E-ISSN: 1552-4833
    Source: Hellenic Academic Libraries Link
    Source: Alma/SFX Local Collection
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  • 2
    Language: English
    In: Scientific reports, 2019-08-19, Vol.9 (1), p.11995-8
    Description: Familial hypercholesterolemia (FH) is characterised by elevated serum levels of low-density lipoprotein cholesterol (LDL-C) and a substantial risk for cardiovascular disease. The autosomal-dominant FH is mostly caused by mutations in LDLR (low density lipoprotein receptor), APOB (apolipoprotein B), and PCSK9 (proprotein convertase subtilisin/kexin). Recently, STAP1 has been suggested as a fourth causative gene. We analyzed STAP1 in 75 hypercholesterolemic patients from Berlin, Germany, who are negative for mutations in canonical FH genes. In 10 patients with negative family history, we additionally screened for disease causing variants in LDLRAP1 (low density lipoprotein receptor adaptor protein 1), associated with autosomal-recessive hypercholesterolemia. We identified one STAP1 variant predicted to be disease causing. To evaluate association of serum lipid levels and STAP1 carrier status, we analyzed 20 individuals from a population based cohort, the Cooperative Health Research in South Tyrol (CHRIS) study, carrying rare STAP1 variants. Out of the same cohort we randomly selected 100 non-carriers as control. In the Berlin FH cohort STAP1 variants were rare. In the CHRIS cohort, we obtained no statistically significant differences between carriers and non-carriers of STAP1 variants with respect to lipid traits. Until such an association has been verified in more individuals with genetic variants in STAP1, we cannot estimate whether STAP1 generally is a causative gene for FH.
    Subject(s): Adaptor Proteins, Signal Transducing - genetics ; Adaptor Proteins, Signal Transducing - metabolism ; Adult ; Aged ; Aged, 80 and over ; Apolipoprotein B ; Biomarkers ; Cardiovascular diseases ; Cholesterol ; Female ; Genetic Association Studies - methods ; Genetic Predisposition to Disease ; Genetic variance ; Humans ; Hypercholesterolemia ; Hyperlipoproteinemia Type II - diagnosis ; Hyperlipoproteinemia Type II - etiology ; Kexin ; Lipid Metabolism ; Lipids - blood ; Low density lipoprotein ; Low density lipoprotein receptors ; Male ; Middle Aged ; Multidisciplinary Sciences ; Mutation ; Phenotype ; Proprotein convertases ; Receptor density ; Science & Technology ; Science & Technology - Other Topics ; Sequence Analysis, DNA ; Serum levels ; Statistical analysis ; Subtilisin
    ISSN: 2045-2322
    E-ISSN: 2045-2322
    Source: Nature Open Access
    Source: Academic Search Ultimate
    Source: Web of Science - Science Citation Index Expanded - 2019〈img src="http://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /〉
    Source: PubMed Central
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  • 3
    Language: English
    In: European journal of human genetics : EJHG, 2019-12-01, Vol.27 (12), p.1827-1835
    Description: Variants in DONSON were recently identified as the cause of microcephaly, short stature, and limb abnormalities syndrome (MISSLA). The clinical spectra of MISSLA and Fanconi anaemia (FA) strongly overlap. For that reason, some MISSLA patients have been clinically diagnosed with FA. Here, we present the clinical data of siblings with MISSLA featuring a novel DONSON variant and summarize the current literature on MISSLA. Additionally, we perform computer-aided image analysis using the DeepGestalt technology to test how distinct the facial features of MISSLA and FA patients are. We show that MISSLA has a specific facial gestalt. Notably, we find that also FA patients feature facial characteristics recognizable by computer-aided image analysis. We conclude that computer-assisted image analysis improves diagnostic precision in both MISSLA and FA.
    Subject(s): Abnormalities, Multiple - diagnosis ; Abnormalities, Multiple - diagnostic imaging ; Abnormalities, Multiple - genetics ; Abnormalities, Multiple - pathology ; Biochemistry & Molecular Biology ; Cell Cycle Proteins - genetics ; Dwarfism - diagnosis ; Dwarfism - diagnostic imaging ; Dwarfism - genetics ; Dwarfism - pathology ; Fanconi Anemia - diagnosis ; Fanconi Anemia - diagnostic imaging ; Fanconi Anemia - genetics ; Fanconi Anemia - pathology ; Female ; Genetics & Heredity ; Genetics research ; Humans ; Infant ; Infant, Newborn ; Life Sciences & Biomedicine ; Male ; Microcephaly - diagnosis ; Microcephaly - diagnostic imaging ; Microcephaly - genetics ; Microcephaly - pathology ; Mutation ; Nuclear Proteins - genetics ; Phenotype ; Science & Technology ; Siblings ; Translational research
    ISSN: 1018-4813
    E-ISSN: 1476-5438
    Source: Web of Science - Science Citation Index Expanded - 2019〈img src="http://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /〉
    Source: PubMed Central
    Source: Alma/SFX Local Collection
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  • 4
    Language: English
    In: BMC medical genomics, 2019-01-10, Vol.12 (1), p.6-6
    Description: BackgroundTwo interstitial microdeletions Xp11.22 including the CLCN5 and SHROOM4 genes were recently reported in a male individual affected with Dent disease, short stature, psychomotor delay and minor facial anomalies. Dent disease, characterized by a specific renal phenotype, is caused by truncating mutations of CLCN5 in the majority of affected cases.Case presentationHere, we present clinical and molecular findings in a male patient with clinical signs of Dent disease, developmental delay, short stature, microcephaly, and facial dysmorphism. Using molecular karyotyping we identified a hemizygous interstitial microdeletion Xp11.23p.11.22 of about 700kb, which was inherited from his asymptomatic mother. Among the six deleted genes is CLCN5, which explains the renal phenotype in our patient. SHROOM4, which is partially deleted in this patient, is involved in neuronal development and was shown to be associated with X-linked intellectual disability. This is a candidate gene, the loss of which is thought to be associated with his further clinical manifestations.To rule out mutations in other genes related to intellectual disability, whole exome sequencing was performed. No other pathogenic variants that could explain the phenotypic features, were found.ConclusionWe compared the clinical findings of the patient presented here with the reported case with an Xp11.22 microdeletion including CLCN5 and SHROOM4 and re-defined the phenotypic spectrum associated with this microdeletion.
    Subject(s): Case Report ; Child, Preschool ; Chloride Channels - genetics ; Chromosome Deletion ; CLCN5 ; Cytoskeletal Proteins - genetics ; Dent disease ; Dent Disease - complications ; Dent Disease - genetics ; Dent's disease ; Dwarfism - complications ; Female ; Genetic aspects ; Genetics & Heredity ; Humans ; Intellectual Disability - complications ; Karyotyping ; Life Sciences & Biomedicine ; Male ; Mental retardation ; Microcephaly ; Microcephaly - complications ; Pedigree ; Science & Technology ; SHROOM4 ; Stature, Short ; Usage
    ISSN: 1755-8794
    E-ISSN: 1755-8794
    Source: BioMedCentral Open Access
    Source: Academic Search Ultimate
    Source: Web of Science - Science Citation Index Expanded - 2019〈img src="http://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /〉
    Source: PubMed Central
    Source: DOAJ Directory of Open Access Journals - Not for CDI Discovery
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  • 5
    Language: English
    In: Journal of medical Internet research, 2020-10-22, Vol.22 (10), p.e19263-e19263
    Description: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt's quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning-based framework for the automated differentiation of DeepGestalt's output on such images. Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt's high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt's syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt's top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P〈.001). A linear SVM running on DeepGestalt's result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P〈.001). DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt's results and may help enhance it and similar computer-aided facial phenotyping tools.
    Subject(s): Computers - standards ; Craniofacial Abnormalities - diagnostic imaging ; DeepGestalt ; diagnostic accuracy ; Face - diagnostic imaging ; Face2Gene ; facial phenotyping ; facial recognition ; Female ; genetic syndrome ; Humans ; machine learning ; Male ; medical genetics ; Original Paper ; Phenotype
    ISSN: 1439-4456
    E-ISSN: 1438-8871
    Source: PubMed Central
    Source: Alma/SFX Local Collection
    Source: DOAJ Directory of Open Access Journals - Not for CDI Discovery
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  • 6
    Language: German
    Description: Hintergrund: Die familiäre Hypercholesterinämie (FH) ist eine angeborene Lipidstoffwechselstörung, welche klinisch durch einen erhöhten LDL-Cholesterinspiegel, sowie ein erhebliches Risiko für Herz-Kreislauf-Erkrankungen gekennzeichnet ist. Diese FH unterliegt einem autosomal-dominanten Vererbungsmechanismus und wird hauptsächlich durch Mutationen im LDLR (Low Density Lipoprotein Rezeptor), APOB (Apolipoprotein B) und PCSK9 (Proprotein Conversionase Subtilisin/kexin) verursacht. Kürzlich wurde STAP1 als viertes verursachendes Gen vorgeschlagen. Methoden: Es wurde eine Sequenzierung aller 9 Exons des STAP1 Gens bei insgesamt 75 Berliner Patienten mit Hypercholesterinämie, die keine pathogene Mutation in einem der drei Hauptgene LDLR, APOB und PCSK9 aufwiesen, durchgeführt. Bei 10 dieser Patienten mit negativer Familienanamnese wurde zudem das Gen LDLRAP1 (Low Density Lipoprotein Rezeptor Adapter Protein 1) untersucht. Zur Evaluierung potentieller Auswirkungen von STAP1-Varianten auf den Serumlipidspiegel wurden zusätzlich Daten aus der populationsbasierten Kohortenstudie Cooperative Health Research in South Tyrol (CHRIS) hinzugezogen. Aus derselben Kohorte erfolgte die randomisierte Bildung einer Kontrollgruppe von 100 Nicht-Trägern für die statistische Analyse. Ergebnisse: Es konnte eine seltene STAP1-Variante c.526C〉T,p.(Pro176Ser), jedoch keine potentiell pathogene Variante im LDLRAP1 Gen der Berliner FH-Kohorte nachgewiesen werden. Über die CHRIS-Kohorte wurden neben der oben Genannten n=1 drei weitere STAP1-Varianten identifiziert (c.619G〉A,p.(Asp207Asn) n=3; c.35G〉A,p.(Arg12His) n= 14; c.414G〉C,p.(Leu138Leu) n=2). Es zeigte sich keine statistisch relevante Erhöhung der Serumlipidwerte in Abhängigkeit vom STAP1-Trägerstatus. Fazit/Diskussion: Es konnte keine Assoziation zwischen Varianten im STAP1-Gen und dem Auftreten einer Hypercholesterinämie gezeigt werden. Die vorliegenden Ergebnisse sprechen gegen STAP1 als ein kausales FH-Gen. Aufgrund der weltweit hohen Anzahl klinischer FH-Diagnosen ohne molekulargenetische Bestätigung ist eine Suche nach weiteren Kandidatgenen mittels Exom- bzw. Genomsequenzierung erstrebenswert. Background: Autosomal-dominant familial hypercholesterolemia (FH) leads to increased serum levels of low-density lipoprotein cholesterol (LDL-C). FH patients have therefore a substantial risk of developing cardiovascular disease (CVD). Disease causing mutation are found in three major genes: LDLR (low density lipoprotein receptor), APOB (apolipoprotein B), and PCSK9 (proprotein convertase subtilisin/kexin). However, the absence of the mutation in one of these genes, does not exclude the diagnosis of FH. Recently, STAP1 (signal transducing adaptor family member 1) has been suggested as a fourth causative gene. Methods: STAP1 was analyzed in 75 hypercholesterolemic patients from Berlin, Germany, in whom the clinical diagnosis of FH could not be confirmed by molecular genetic testing of the three canonical genes. Additionally, ten patients with negative family history were screened for pathogenic variants in LDLRAP1 (low density lipoprotein receptor adaptor protein 1), associated with autosomal recessive hypercholesterolemia. The association of serum lipid levels according to STAP1 carrier status was evaluated. For this purpose, data from a population-based cohort, the Cooperative Health Research in South Tyrol (CHRIS), were included. Results: In the Berlin FH cohort one rare STAP1 variant, c.526C〉T,p.(Pro176Ser), predicted to be disease causing utilizing bioinformatic tools, was identified. In the CHRIS cohort further three variants were detected: (c.619G〉A,p.(Asp207Asn) n=3; c.35G〉A,p.(Arg12His) n=14; c.414G〉C, p.(Leu138Leu) n=2). One further individual carried also the previously determined variant c.526C〉T,p.(Pro176Ser). One hundred non-carriers from the CHRIS cohort were randomly selected as controls. There was no statistically significant difference between carriers and non-carriers of STAP1 variants with respect to lipid traits. Conclusion/discussion: According to the data, rare STAP1 variants seem not to be associated with familial hypercholesterolemia. Comprehensive genetic analysis, such as Whole Exome or Whole Genome Sequencing in order to identify further genes for FH is recommended.
    Subject(s): Autosomal-dominant familial hypercholesterolemia ; ddc:610 ; LDL-C ; STAP1
    Source: Dokumentenserver der FU Berlin
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  • 7
    Language: English
    In: Genetics in medicine, 2019-12-01, Vol.21 (12), p.2807-2814
    Description: Purpose: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. Conclusion: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.
    Subject(s): Algorithms ; Bioinformatics ; Computational Biology - methods ; Databases, Genetic ; Deep Learning ; Exome - genetics ; Female ; Genetics & Heredity ; Genomics ; Humans ; Image Processing, Computer-Assisted - methods ; Life Sciences & Biomedicine ; Male ; Phenotype ; Science & Technology ; Sequence Analysis, DNA - methods ; Software
    ISSN: 1098-3600
    E-ISSN: 1530-0366
    Source: NORA - Norwegian Open Research Archives
    Source: Web of Science - Science Citation Index Expanded - 2019〈img src="http://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /〉
    Source: Alma/SFX Local Collection
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  • 8
    Language: English
    In: NAR Genomics and Bioinformatics, 2021-06-23, Vol.3 (3), p.lqab078-lqab078
    Description: Abstract Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype–genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.
    ISSN: 2631-9268
    E-ISSN: 2631-9268
    Source: Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
    Source: PubMed Central
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  • 9
    Language: English
    Description: Background: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P〈.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P〈.001). Conclusions: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
    Subject(s): ddc:610 ; DeepGestalt ; diagnostic accuracy ; Face2Gene ; facial phenotyping ; facial recognition ; genetic syndrome ; machine learning ; medical genetics
    Source: Dokumentenserver der FU Berlin
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  • 10
    Language: English
    Description: Familial hypercholesterolemia (FH) is characterised by elevated serum levels of low-density lipoprotein cholesterol (LDL-C) and a substantial risk for cardiovascular disease. The autosomal-dominant FH is mostly caused by mutations in LDLR (low density lipoprotein receptor), APOB (apolipoprotein B), and PCSK9 (proprotein convertase subtilisin/kexin). Recently, STAP1 has been suggested as a fourth causative gene. We analyzed STAP1 in 75 hypercholesterolemic patients from Berlin, Germany, who are negative for mutations in canonical FH genes. In 10 patients with negative family history, we additionally screened for disease causing variants in LDLRAP1 (low density lipoprotein receptor adaptor protein 1), associated with autosomal-recessive hypercholesterolemia. We identified one STAP1 variant predicted to be disease causing. To evaluate association of serum lipid levels and STAP1 carrier status, we analyzed 20 individuals from a population based cohort, the Cooperative Health Research in South Tyrol (CHRIS) study, carrying rare STAP1 variants. Out of the same cohort we randomly selected 100 non-carriers as control. In the Berlin FH cohort STAP1 variants were rare. In the CHRIS cohort, we obtained no statistically significant differences between carriers and non-carriers of STAP1 variants with respect to lipid traits. Until such an association has been verified in more individuals with genetic variants in STAP1, we cannot estimate whether STAP1 generally is a causative gene for FH.
    Subject(s): ddc:610 ; familial hypercholesterolemia ; low density lipoprotein receptor (LDLR) ; low-density lipoprotein cholesterol (LDL-C) ; STAP1
    Source: Dokumentenserver der FU Berlin
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