Genotype and phenotype correlation of patients diagnosed of osteogenesis imperfecta

Osteogenezis imperfekta (Oİ), artmış kemik frajilitesi, düşük kemik kitlesi, tekrarlayan kırık ve deformitelerle karakterize, kemik dokusunun sık görülen kalıtsal bağ dokusu hastalığıdır. Bu çalışmada, 2016-2020 yılları arasında Bursa Uludağ Üniversitesi Tıp Fakültesi Tıbbi Genetik Anabilim Dalı polikliniğine başvuran, Oİ ön tanısı alan 54 olgu kullanıldı. Tanı amaçlı yapılan yeni nesil dizileme (YND) sonucunda genlerde saptanan varyantlar veritabanları kullanılarak olguların klinik özellikleri ile birlikte retrospektif olarak değerlendirildi. Olguların 17’si erişkin, 34’ü çocuk ve 3’ü fetüstü. 54 olguya klinik sınıflandırma yapıldığında 24 olgu Tip I, 3 olgu Tip II, 16 olgu Tip III, 9 olgu Tip IV olarak değerlendirildi. Olguların 30’unda mavi sklera, 16’sında skolyoz, 32’sinde çoklu kırığa bağlı ekstremite deformiteleri, 27’sinde osteoporoz, 19’unda osteopeni, 38’inde boy kısalığı, 7’sinde Dİ, 1’inde işitme kaybı görüldü. Olguların 19’unda COL1A1 geninde 17, 10’unda COL1A2 geninde 10, 4’ünde LEPRE1/P3H1 geninde 5, 3’ünde FKBP10 geninde 3, 2’sinde SERPINH1 geninde 3, 1’inde IFITM5 geninde 1, 1’inde PLS3 geninde 1, 1’inde NBAS geninde 2 varyant tespit edildi. Veri analizi yapılan 54 olgunun 41’inde, 18 yeni varyant olmak üzere toplam 39 varyant saptandı. Varyant saptanmayan 13 olguya tüm ekzom dizi analizi yapılması planlandı. Çalışmamızda Oİ’nin moleküler tanısında panel testinin YND tekniği ile çalışılmasının etkinliği, genotip-fenotip korelasyonu, genetik danışma ve preimplantasyon/prenatal tanının önemi vurgulandı.Osteogenesis imperfecta (OI) is the most common inherited connective tissue disease of the bone, characterized by increased bone fragility, low bone mass, recurrent fractures and deformities. In this study, 54 patients who were admitted to the outpatient clinic of Bursa Uludağ University, Department of Medical Genetics between 2016-2020 and prediagnosed as OI were used. The variants detected in the genes as a result of rouitine next generation sequencing (NSD) diagnostic tests were evaluated retrospectively with the clinical data of the cases. 17 of the cases were adult, 34 were children and 3 were fetuses. 24 cases were evaluated as Type I, 3 cases as Type II, 16 cases as Type III, and 9 cases as Type IV. Blue sclera in 30, scoliosis in 16, extremity deformities due to multiple fractures in 32, osteoporosis in 27, osteopenia in 19, short stature in 38, DI in 7, and hearing loss in 1 case were seen. 17 variants in COL1A1 In nineteen caseses 10 variants in COL1A2 ; in ten cas, 5 variants in LEPRE1 / P3H1 in four cases, 3 variants in FKBP10 in three cases, 3 variants in SERPINH1 in two cases, 1 variant in IFITM5 , 1 variant in PLS3 and 2 variants in the NBAS gene in three cases were detected. A total of 39 variants were detected on 41 cases. 22 of these variants were novel. In our study, the effectiveness of the NGS panel test in the molecular diagnosis of OI, genotype-phenotype correlation, genetic counseling and preimplantation/prenatal diagnosis were emphasized

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients' data were used to train the system using Mamdani's Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network's overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations' risk assessment in breast cancers as well as a unique tool for personalized medicine software

A Multicenter Study of Genotype Variation/Demographic Patterns in 2475 Individuals Including 1444 Cases With Breast Cancer in Turkey

Objective: Breast cancer (BC) is the most common cancer type in women and may be inherited, mostly in an autosomal dominant pattern. The clinical diagnosis of BC relies on the published diagnostic criteria, and analysis of two genes, BRCA1 and BRCA2, which are strongly associated with BC, are included in these criteria. The aim of this study was to compare BC index cases with non-BC individuals in terms of genotype and diagnostic features to investigate the genotype/demographic information association. Materials and Methods: Mutational analyses for the BRCA1/BRCA2 genes was performed in 2475 individuals between 2013-2022 from collaborative centers across Turkey, of whom 1444 with BC were designated as index cases. Results: Overall, mutations were identified in 17% (421/2475), while the percentage of mutation carriers in cases of BC was similar, 16.6% (239/1444). BRCA1/BRCA2 gene mutations were detected in 17.8% (131/737) of familial cases and 12% (78/549) of sporadic cases. Mutations in BRCA1 were found in 4.9%, whereas 12% were in BRCA2 (p<0.05). Meta-analyses were performed to compare these results with other studies of Mediterranean-region populations. Conclusion: Patients with BRCA2 mutations were significantly more common than those with BRCA1 mutations. In sporadic cases, there was a lower proportion with BRCA1/BRCA2 variants, as expected, and these results were consistent with the data of Mediterranean-region populations. However, the present study, because of the large sample size, revealed more robust findings than previous studies. These findings may be helpful in facilitating the clinical management of BC for both familial and non-familial cases

Clinical and molecular evaluation of MEFV gene variants in the Turkish population: a study by the National Genetics Consortium

Familial Mediterranean fever (FMF) is a monogenic autoinflammatory disorder with recurrent fever, abdominal pain, serositis, articular manifestations, erysipelas-like erythema, and renal complications as its main features. Caused by the mutations in the MEditerranean FeVer (MEFV) gene, it mainly affects people of Mediterranean descent with a higher incidence in the Turkish, Jewish, Arabic, and Armenian populations. As our understanding of FMF improves, it becomes clearer that we are facing with a more complex picture of FMF with respect to its pathogenesis, penetrance, variant type (gain-of-function vs. loss-of-function), and inheritance. In this study, MEFV gene analysis results and clinical findings of 27,504 patients from 35 universities and institutions in Turkey and Northern Cyprus are combined in an effort to provide a better insight into the genotype-phenotype correlation and how a specific variant contributes to certain clinical findings in FMF patients. Our results may help better understand this complex disease and how the genotype may sometimes contribute to phenotype. Unlike many studies in the literature, our study investigated a broader symptomatic spectrum and the relationship between the genotype and phenotype data. In this sense, we aimed to guide all clinicians and academicians who work in this field to better establish a comprehensive data set for the patients. One of the biggest messages of our study is that lack of uniformity in some clinical and demographic data of participants may become an obstacle in approaching FMF patients and understanding this complex disease