BACKGROUND: Marfan Syndrome (MFS) is a genetic disorder of the connective tissue which may be mortal for affected individuals. It is fundamental to detect the disease as soon as possible, but the diagnosis may be complicated because of the presence of other connective tissue disorders, phenotypically similar to MFS and often named "Marfan-like" syndromes. Currently, the FBN1 gene is the principal gene analyzed for MFS, but researchers agree that the genotype-phenotype association is widely misundertood. In this study we proposed a method of analysis which is suitable for studying the genotype-phenotype association of MFS and that takes into account FBN1 and other 10 genes implied in the TGF-β signaling pathway, such as ACTA2, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, MYH11, NOTCH1, TGFBR1 and TGFBR2 genes. MATERIALS AND METHODS: A tool was developed in order to format the genetic data resulting from Next Generation Sequencing methods and create a genetic dataset which may be read and analyzed by statistical software. The tool was applied to genotype data of 181 patients suspected of having a connective tissue disorder. Phenotype data of the patients were also collected and the variables representing the presence/absence of a given clinical sign were considered as outcomes. Logistic regression models were used to study the effect of the common variants on the clinical signs. Polymorphisms nominally associated with the clinical signs were summarized by Polygenic Risk Scores (PRSs). Optimized Sequence Kernel Association Test (SKAT-O) was applied on the rare variants subdivided for genes in order to study the effect of rare variants on the clinical signs. Results of common and rare variant analyses were included in logistic regression models and compared with models which considered only the rare variants in the FBN1 gene. Comparisons were made through the Akaike Information Criteria (AIC) and the deviance statistics. RESULTS: 5 polymorphisms in COL1A1 gene resulted in borderline associations with scoliosis showing a protective effect. Rare variants in FBN1, ACTA2, COL3A1, COL1A2, MYH11, NOTCH1 and TGFBR2 genes resulted in associations with part of the clinical signs. Models containing information about common variants and rare variants in different genes resulted more informative than the models containing only information about rare variants in the FBN1 gene. CONCLUSIONS: Results seem to suggest that clinical signs may be influenced by common and rare variants in different genes, hence it may be useful analyze more genes and not only FBN1.

BACKGROUND: Marfan Syndrome (MFS) is a genetic disorder of the connective tissue which may be mortal for affected individuals. It is fundamental to detect the disease as soon as possible, but the diagnosis may be complicated because of the presence of other connective tissue disorders, phenotypically similar to MFS and often named "Marfan-like" syndromes. Currently, the FBN1 gene is the principal gene analyzed for MFS, but researchers agree that the genotype-phenotype association is widely misundertood. In this study we proposed a method of analysis which is suitable for studying the genotype-phenotype association of MFS and that takes into account FBN1 and other 10 genes implied in the TGF-β signaling pathway, such as ACTA2, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, MYH11, NOTCH1, TGFBR1 and TGFBR2 genes. MATERIALS AND METHODS: A tool was developed in order to format the genetic data resulting from Next Generation Sequencing methods and create a genetic dataset which may be read and analyzed by statistical software. The tool was applied to genotype data of 181 patients suspected of having a connective tissue disorder. Phenotype data of the patients were also collected and the variables representing the presence/absence of a given clinical sign were considered as outcomes. Logistic regression models were used to study the effect of the common variants on the clinical signs. Polymorphisms nominally associated with the clinical signs were summarized by Polygenic Risk Scores (PRSs). Optimized Sequence Kernel Association Test (SKAT-O) was applied on the rare variants subdivided for genes in order to study the effect of rare variants on the clinical signs. Results of common and rare variant analyses were included in logistic regression models and compared with models which considered only the rare variants in the FBN1 gene. Comparisons were made through the Akaike Information Criteria (AIC) and the deviance statistics. RESULTS: 5 polymorphisms in COL1A1 gene resulted in borderline associations with scoliosis showing a protective effect. Rare variants in FBN1, ACTA2, COL3A1, COL1A2, MYH11, NOTCH1 and TGFBR2 genes resulted in associations with part of the clinical signs. Models containing information about common variants and rare variants in different genes resulted more informative than the models containing only information about rare variants in the FBN1 gene. CONCLUSIONS: Results seem to suggest that clinical signs may be influenced by common and rare variants in different genes, hence it may be useful analyze more genes and not only FBN1.

A method of analysis for the genotype-phenotype association based on Next Generation Sequencing data: development of a tool for the formatting of genetic data for statistical purposes and application to the Marfan syndrome

OLIVERI, ANTONINO
2020-02-14

Abstract

BACKGROUND: Marfan Syndrome (MFS) is a genetic disorder of the connective tissue which may be mortal for affected individuals. It is fundamental to detect the disease as soon as possible, but the diagnosis may be complicated because of the presence of other connective tissue disorders, phenotypically similar to MFS and often named "Marfan-like" syndromes. Currently, the FBN1 gene is the principal gene analyzed for MFS, but researchers agree that the genotype-phenotype association is widely misundertood. In this study we proposed a method of analysis which is suitable for studying the genotype-phenotype association of MFS and that takes into account FBN1 and other 10 genes implied in the TGF-β signaling pathway, such as ACTA2, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, MYH11, NOTCH1, TGFBR1 and TGFBR2 genes. MATERIALS AND METHODS: A tool was developed in order to format the genetic data resulting from Next Generation Sequencing methods and create a genetic dataset which may be read and analyzed by statistical software. The tool was applied to genotype data of 181 patients suspected of having a connective tissue disorder. Phenotype data of the patients were also collected and the variables representing the presence/absence of a given clinical sign were considered as outcomes. Logistic regression models were used to study the effect of the common variants on the clinical signs. Polymorphisms nominally associated with the clinical signs were summarized by Polygenic Risk Scores (PRSs). Optimized Sequence Kernel Association Test (SKAT-O) was applied on the rare variants subdivided for genes in order to study the effect of rare variants on the clinical signs. Results of common and rare variant analyses were included in logistic regression models and compared with models which considered only the rare variants in the FBN1 gene. Comparisons were made through the Akaike Information Criteria (AIC) and the deviance statistics. RESULTS: 5 polymorphisms in COL1A1 gene resulted in borderline associations with scoliosis showing a protective effect. Rare variants in FBN1, ACTA2, COL3A1, COL1A2, MYH11, NOTCH1 and TGFBR2 genes resulted in associations with part of the clinical signs. Models containing information about common variants and rare variants in different genes resulted more informative than the models containing only information about rare variants in the FBN1 gene. CONCLUSIONS: Results seem to suggest that clinical signs may be influenced by common and rare variants in different genes, hence it may be useful analyze more genes and not only FBN1.
BACKGROUND: Marfan Syndrome (MFS) is a genetic disorder of the connective tissue which may be mortal for affected individuals. It is fundamental to detect the disease as soon as possible, but the diagnosis may be complicated because of the presence of other connective tissue disorders, phenotypically similar to MFS and often named "Marfan-like" syndromes. Currently, the FBN1 gene is the principal gene analyzed for MFS, but researchers agree that the genotype-phenotype association is widely misundertood. In this study we proposed a method of analysis which is suitable for studying the genotype-phenotype association of MFS and that takes into account FBN1 and other 10 genes implied in the TGF-β signaling pathway, such as ACTA2, COL1A1, COL1A2, COL3A1, COL5A1, COL5A2, MYH11, NOTCH1, TGFBR1 and TGFBR2 genes. MATERIALS AND METHODS: A tool was developed in order to format the genetic data resulting from Next Generation Sequencing methods and create a genetic dataset which may be read and analyzed by statistical software. The tool was applied to genotype data of 181 patients suspected of having a connective tissue disorder. Phenotype data of the patients were also collected and the variables representing the presence/absence of a given clinical sign were considered as outcomes. Logistic regression models were used to study the effect of the common variants on the clinical signs. Polymorphisms nominally associated with the clinical signs were summarized by Polygenic Risk Scores (PRSs). Optimized Sequence Kernel Association Test (SKAT-O) was applied on the rare variants subdivided for genes in order to study the effect of rare variants on the clinical signs. Results of common and rare variant analyses were included in logistic regression models and compared with models which considered only the rare variants in the FBN1 gene. Comparisons were made through the Akaike Information Criteria (AIC) and the deviance statistics. RESULTS: 5 polymorphisms in COL1A1 gene resulted in borderline associations with scoliosis showing a protective effect. Rare variants in FBN1, ACTA2, COL3A1, COL1A2, MYH11, NOTCH1 and TGFBR2 genes resulted in associations with part of the clinical signs. Models containing information about common variants and rare variants in different genes resulted more informative than the models containing only information about rare variants in the FBN1 gene. CONCLUSIONS: Results seem to suggest that clinical signs may be influenced by common and rare variants in different genes, hence it may be useful analyze more genes and not only FBN1.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/1329170
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