The Experts below are selected from a list of 71151 Experts worldwide ranked by ideXlab platform
Joris A. Veltman - One of the best experts on this subject based on the ideXlab platform.
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Disease Gene Identification strategies for exome sequencing.
European journal of human genetics : EJHG, 2012Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Next Generation sequencing can be used to search for Mendelian Disease Genes in an unbiased manner by sequencing the entire protein-coding sequence, known as the exome, or even the entire human genome. Identifying the pathogenic mutation amongst thousands to millions of genomic variants is a major challenge, and novel variant prioritization strategies are required. The choice of these strategies depends on the availability of well-phenotyped patients and family members, the mode of inheritance, the severity of the Disease and its population frequency. In this review, we discuss the current strategies for Mendelian Disease Gene Identification by exome resequencing. We conclude that exome strategies are successful and identify new Mendelian Disease Genes in approximately 60% of the projects. Improvements in bioinformatics as well as in sequencing technology will likely increase the success rate even further. Exome sequencing is likely to become the most commonly used tool for Mendelian Disease Gene Identification for the coming years.
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Unlocking Mendelian Disease using exome sequencing
Genome Biology, 2011Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Exome sequencing is revolutionizing Mendelian Disease Gene Identification. This results in improved clinical diagnosis, more accurate genotype-phenotype correlations and new insights into the role of rare genomic variation in Disease.
Han G Brunner - One of the best experts on this subject based on the ideXlab platform.
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Disease Gene Identification strategies for exome sequencing.
European journal of human genetics : EJHG, 2012Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Next Generation sequencing can be used to search for Mendelian Disease Genes in an unbiased manner by sequencing the entire protein-coding sequence, known as the exome, or even the entire human genome. Identifying the pathogenic mutation amongst thousands to millions of genomic variants is a major challenge, and novel variant prioritization strategies are required. The choice of these strategies depends on the availability of well-phenotyped patients and family members, the mode of inheritance, the severity of the Disease and its population frequency. In this review, we discuss the current strategies for Mendelian Disease Gene Identification by exome resequencing. We conclude that exome strategies are successful and identify new Mendelian Disease Genes in approximately 60% of the projects. Improvements in bioinformatics as well as in sequencing technology will likely increase the success rate even further. Exome sequencing is likely to become the most commonly used tool for Mendelian Disease Gene Identification for the coming years.
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Unlocking Mendelian Disease using exome sequencing
Genome Biology, 2011Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Exome sequencing is revolutionizing Mendelian Disease Gene Identification. This results in improved clinical diagnosis, more accurate genotype-phenotype correlations and new insights into the role of rare genomic variation in Disease.
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computational Disease Gene Identification a concert of methods prioritizes type 2 diabetes and obesity candidate Genes
Nucleic Acids Research, 2006Co-Authors: Nicki Tiffin, Han G Brunner, Euan A Adie, Frances Turner, Marc A Van Driel, Martin Oti, Nuria Lopezbigas, Christos A Ouzounis, Carolina Pereziratxeta, Miguel A AndradenavarroAbstract:Genome-wide experimental methods to identify Disease Genes, such as linkage analysis and association studies, Generate increasingly large candidate Gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate Disease Genes from these Gene sets. Here, we review seven independent computational Disease Gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate Genes for type 2 diabetes (T2D) and the related trait obesity. We Generate and analyse a list of nine primary candidate Genes for T2D Genes and five for obesity. Two Genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 Genes) and for obesity (116 Genes) with 58 Genes in common to both Diseases.
Christian Gilissen - One of the best experts on this subject based on the ideXlab platform.
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Disease Gene Identification strategies for exome sequencing.
European journal of human genetics : EJHG, 2012Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Next Generation sequencing can be used to search for Mendelian Disease Genes in an unbiased manner by sequencing the entire protein-coding sequence, known as the exome, or even the entire human genome. Identifying the pathogenic mutation amongst thousands to millions of genomic variants is a major challenge, and novel variant prioritization strategies are required. The choice of these strategies depends on the availability of well-phenotyped patients and family members, the mode of inheritance, the severity of the Disease and its population frequency. In this review, we discuss the current strategies for Mendelian Disease Gene Identification by exome resequencing. We conclude that exome strategies are successful and identify new Mendelian Disease Genes in approximately 60% of the projects. Improvements in bioinformatics as well as in sequencing technology will likely increase the success rate even further. Exome sequencing is likely to become the most commonly used tool for Mendelian Disease Gene Identification for the coming years.
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Unlocking Mendelian Disease using exome sequencing
Genome Biology, 2011Co-Authors: Christian Gilissen, Han G Brunner, Alexander Hoischen, Joris A. VeltmanAbstract:Exome sequencing is revolutionizing Mendelian Disease Gene Identification. This results in improved clinical diagnosis, more accurate genotype-phenotype correlations and new insights into the role of rare genomic variation in Disease.
Loic Quevarec - One of the best experts on this subject based on the ideXlab platform.
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phenotypic spectrum and genomics of undiagnosed arthrogryposis multiplex congenita
Journal of Medical Genetics, 2021Co-Authors: Annie Laquerriere, Dana Jaber, Emanuela Abiusi, Jerome Maluenda, Dan Mejlachowicz, Alexandre J Vivanti, Klaus Dieterich, Radka Stoeva, Loic QuevarecAbstract:Background Arthrogryposis multiplex congenita (AMC) is characterised by congenital joint contractures in two or more body areas. AMC exhibits wide phenotypic and Genetic heteroGeneity. Our goals were to improve the Genetic diagnosis rates of AMC, to evaluate the added value of whole exome sequencing (WES) compared with targeted exome sequencing (TES) and to identify new Genes in 315 unrelated undiagnosed AMC families. Methods Several genomic approaches were used including Genetic mapping of Disease loci in multiplex or consanguineous families, TES then WES. Sanger sequencing was performed to identify or validate variants. Results We achieved Disease Gene Identification in 52.7% of AMC index patients including nine recently identified Genes (CNTNAP1, MAGEL2, ADGRG6, ADCY6, GLDN, LGI4, LMOD3, UNC50 and SCN1A). Moreover, we identified pathogenic variants in ASXL3 and STAC3 expanding the phenotypes associated with these Genes. The most frequent cause of AMC was a primary involvement of skeletal muscle (40%) followed by brain (22%). The most frequent mode of inheritance is autosomal recessive (66.3% of patients). In sporadic patients born to non-consanguineous parents (n=60), de novo dominant autosomal or X linked variants were observed in 30 of them (50%). Conclusion New Genes recently identified in AMC represent 21% of causing Genes in our cohort. A high proportion of de novo variants were observed indicating that this mechanism plays a prominent part in this developmental Disease. Our data showed the added value of WES when compared with TES due to the larger clinical spectrum of some Disease Genes than initially described and the Identification of novel Genes.
Emanuela Abiusi - One of the best experts on this subject based on the ideXlab platform.
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phenotypic spectrum and genomics of undiagnosed arthrogryposis multiplex congenita
Journal of Medical Genetics, 2021Co-Authors: Annie Laquerriere, Dana Jaber, Emanuela Abiusi, Jerome Maluenda, Dan Mejlachowicz, Alexandre J Vivanti, Klaus Dieterich, Radka Stoeva, Loic QuevarecAbstract:Background Arthrogryposis multiplex congenita (AMC) is characterised by congenital joint contractures in two or more body areas. AMC exhibits wide phenotypic and Genetic heteroGeneity. Our goals were to improve the Genetic diagnosis rates of AMC, to evaluate the added value of whole exome sequencing (WES) compared with targeted exome sequencing (TES) and to identify new Genes in 315 unrelated undiagnosed AMC families. Methods Several genomic approaches were used including Genetic mapping of Disease loci in multiplex or consanguineous families, TES then WES. Sanger sequencing was performed to identify or validate variants. Results We achieved Disease Gene Identification in 52.7% of AMC index patients including nine recently identified Genes (CNTNAP1, MAGEL2, ADGRG6, ADCY6, GLDN, LGI4, LMOD3, UNC50 and SCN1A). Moreover, we identified pathogenic variants in ASXL3 and STAC3 expanding the phenotypes associated with these Genes. The most frequent cause of AMC was a primary involvement of skeletal muscle (40%) followed by brain (22%). The most frequent mode of inheritance is autosomal recessive (66.3% of patients). In sporadic patients born to non-consanguineous parents (n=60), de novo dominant autosomal or X linked variants were observed in 30 of them (50%). Conclusion New Genes recently identified in AMC represent 21% of causing Genes in our cohort. A high proportion of de novo variants were observed indicating that this mechanism plays a prominent part in this developmental Disease. Our data showed the added value of WES when compared with TES due to the larger clinical spectrum of some Disease Genes than initially described and the Identification of novel Genes.