9 research outputs found

    Mutations in FGF17, IL17RD, DUSP6, SPRY4, and FLRT3 Are Identified in Individuals with Congenital Hypogonadotropic Hypogonadism

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    Congenital hypogonadotropic hypogonadism (CHH) and its anosmia-associated form (Kallmann syndrome [KS]) are genetically heterogeneous. Among the >15 genes implicated in these conditions, mutations in FGF8 and FGFR1 account for ∼12% of cases; notably, KAL1 and HS6ST1 are also involved in FGFR1 signaling and can be mutated in CHH. We therefore hypothesized that mutations in genes encoding a broader range of modulators of the FGFR1 pathway might contribute to the genetics of CHH as causal or modifier mutations. Thus, we aimed to (1) investigate whether CHH individuals harbor mutations in members of the so-called "FGF8 synexpression" group and (2) validate the ability of a bioinformatics algorithm on the basis of protein-protein interactome data (interactome-based affiliation scoring [IBAS]) to identify high-quality candidate genes. On the basis of sequence homology, expression, and structural and functional data, seven genes were selected and sequenced in 386 unrelated CHH individuals and 155 controls. Except for FGF18 and SPRY2, all other genes were found to be mutated in CHH individuals: FGF17 (n = 3 individuals), IL17RD (n = 8), DUSP6 (n = 5), SPRY4 (n = 14), and FLRT3 (n = 3). Independently, IBAS predicted FGF17 and IL17RD as the two top candidates in the entire proteome on the basis of a statistical test of their protein-protein interaction patterns to proteins known to be altered in CHH. Most of the FGF17 and IL17RD mutations altered protein function in vitro. IL17RD mutations were found only in KS individuals and were strongly linked to hearing loss (6/8 individuals). Mutations in genes encoding components of the FGF pathway are associated with complex modes of CHH inheritance and act primarily as contributors to an oligogenic genetic architecture underlying CHH

    Novel variation and <i>de novo </i>mutation rates in population-wide <i>de novo</i> assembled Danish trios

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    Building a population-specific catalogue of single nucleotide variants (SNVs), indels and structural variants (SVs) with frequencies, termed a national pan-genome, is critical for further advancing clinical and public health genetics in large cohorts. Here we report a Danish pan-genome obtained from sequencing 10 trios to high depth (50 × ). We report 536k novel SNVs and 283k novel short indels from mapping approaches and develop a population-wide de novo assembly approach to identify 132k novel indels larger than 10 nucleotides with low false discovery rates. We identify a higher proportion of indels and SVs than previous efforts showing the merits of high coverage and de novo assembly approaches. In addition, we use trio information to identify de novo mutations and use a probabilistic method to provide direct estimates of 1.27e−8 and 1.5e−9 per nucleotide per generation for SNVs and indels, respectively

    Protein Interaction-Based Genome-Wide Analysis of Incident Coronary Heart Disease

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    BACKGROUND: Network-based approaches may leverage genome-wide association (GWA) analysis by testing for the aggregate association across several pathway members. We aimed to examine if networks of genes that represent experimentally determined protein-protein interactions are enriched in genes associated with risk of coronary heart disease (CHD). METHODS AND RESULTS: GWA analyses of ~700,000 SNPs in 899 incident CHD cases and 1,823 age- and sex-matched controls within the Nurses’ Health and the Health Professionals Follow-Up Studies were used to assign gene-wise p-values. A large database of protein-protein interactions (PPI) was used to assemble 8,300 unbiased protein complexes and corresponding gene-sets. Superimposed gene-wise p-values were used to rank gene-sets based on their enrichment in genes associated with CHD. After correcting for the number of complexes tested, one gene-set was overrepresented in CHD-associated genes (p-value=0.002). Centered on the beta-1-adrenergic receptor gene (ADRB1), this complex included 18 protein interaction partners that, so far, have not been identified as candidate loci for CHD. Five of the 19 genes in the top-complex are reported to be involved in abnormal cardiovascular system physiology based on knock-out mice (4-fold enrichment; p-value, Fisher’s exact test= 0.006). Ingenuity pathway analysis revealed that especially canonical pathways related to blood pressure regulation were significantly enriched in the genes from the top complex. CONCLUSIONS: The integration of a GWA study with PPI data successfully identifies a set of candidate susceptibility genes for incident CHD that would have been missed in single-marker GWA analysis

    Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

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    Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples
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