6 research outputs found

    The Human Transcriptome Map Reveals Extremes in Gene Density, Intron Length, GC Content, and Repeat Pattern for Domains of Highly and Weakly Expressed Genes

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    The chromosomal gene expression profiles established by the Human Transcriptome Map (HTM) revealed a clustering of highly expressed genes in about 30 domains, called ridges. To physically characterize ridges, we constructed a new HTM based on the draft human genome sequence (HTMseq). Expression of 25,003 genes can be analyzed online in a multitude of tissues (http://bioinfo.amc.uva.nl/HTMseq). Ridges are found to be very gene-dense domains with a high GC content, a high SINE repeat density, and a low LINE repeat density. Genes in ridges have significantly shorter introns than genes outside of ridges. The HTMseq also identifies a significant clustering of weakly expressed genes in domains with fully opposite characteristics (antiridges). Both types of domains are open to tissue-specific expression regulation, but the maximal expression levels in ridges are considerably higher than in antiridges. Ridges are therefore an integral part of a higher order structure in the genome related to transcriptional regulation

    Integrated clinical and omics approach to rare diseases : Novel genes and oligogenic inheritance in holoprosencephaly

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    Holoprosencephaly is a pathology of forebrain development characterized by high phenotypic heterogeneity. The disease presents with various clinical manifestations at the cerebral or facial levels. Several genes have been implicated in holoprosencephaly but its genetic basis remains unclear: different transmission patterns have been described including autosomal dominant, recessive and digenic inheritance. Conventional molecular testing approaches result in a very low diagnostic yield and most cases remain unsolved. In our study, we address the possibility that genetically unsolved cases of holoprosencephaly present an oligogenic origin and result from combined inherited mutations in several genes. Twenty-six unrelated families, for whom no genetic cause of holoprosencephaly could be identified in clinical settings [whole exome sequencing and comparative genomic hybridization (CGH)-array analyses], were reanalysed under the hypothesis of oligogenic inheritance. Standard variant analysis was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis. Statistical validation was performed on 248 ancestrally similar control trios provided by the Genome of the Netherlands project and on 574 ancestrally matched controls provided by the French Exome Project. Variants of clinical interest were identified in 180 genes significantly associated with key pathways of forebrain development including sonic hedgehog (SHH) and primary cilia. Oligogenic events were observed in 10 families and involved both known and novel holoprosencephaly genes including recurrently mutated FAT1, NDST1, COL2A1 and SCUBE2. The incidence of oligogenic combinations was significantly higher in holoprosencephaly patients compared to two control populations (P < 10 -9). We also show that depending on the affected genes, patients present with particular clinical features. This study reports novel disease genes and supports oligogenicity as clinically relevant model in holoprosencephaly. It also highlights key roles of SHH signalling and primary cilia in forebrain development. We hypothesize that distinction between different clinical manifestations of holoprosencephaly lies in the degree of overall functional impact on SHH signalling. Finally, we underline that integrating clinical phenotyping in genetic studies is a powerful tool to specify the clinical relevance of certain mutations

    A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity

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    Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

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