Dataset Information

Whole genome SNP genotyping and exome sequencing reveal novel genetic variants and putative causative genes in congenital hyperinsulinism.

ABSTRACT: Congenital hyperinsulinism of infancy (CHI) is a rare disorder characterized by severe hypoglycemia due to inappropriate insulin secretion. The genetic causes of CHI have been found in genes regulating insulin secretion from pancreatic ?-cells; recessive inactivating mutations in the ABCC8 and KCNJ11 genes represent the most common events. Despite the advances in understanding the molecular pathogenesis of CHI, specific genetic determinants in about 50 % of the CHI patients remain unknown, suggesting additional locus heterogeneity. In order to search for novel loci contributing to the pathogenesis of CHI, we combined a family-based association study, using the transmission disequilibrium test on 17 CHI patients lacking mutations in ABCC8/KCNJ11, with a whole-exome sequencing analysis performed on 10 probands. This strategy allowed the identification of the potential causative mutations in genes implicated in the regulation of insulin secretion such as transmembrane proteins (CACNA1A, KCNH6, KCNJ10, NOTCH2, RYR3, SCN8A, TRPV3, TRPC5), cytosolic (ACACB, CAMK2D, CDKAL1, GNAS, NOS2, PDE4C, PIK3R3) and mitochondrial enzymes (PC, SLC24A6), and in four genes (CSMD1, SLC37A3, SULF1, TLL1) suggested by TDT family-based association study. Moreover, the exome-sequencing approach resulted to be an efficient diagnostic tool for CHI, allowing the identification of mutations in three causative CHI genes (ABCC8, GLUD1, and HNF1A) in four out of 10 patients. Overall, the present study should be considered as a starting point to design further investigations: our results might indeed contribute to meta-analysis studies, aimed at the identification/confirmation of novel causative or modifier genes.

SUBMITTER: Proverbio MC

PROVIDER: S-EPMC3711910 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Whole genome SNP genotyping and exome sequencing reveal novel genetic variants and putative causative genes in congenital hyperinsulinism.

Proverbio Maria Carla MC Mangano Eleonora E Gessi Alessandra A Bordoni Roberta R Spinelli Roberta R Asselta Rosanna R Valin Paola Sogno PS Di Candia Stefania S Zamproni Ilaria I Diceglie Cecilia C Mora Stefano S Caruso-Nicoletti Manuela M Salvatoni Alessandro A De Bellis Gianluca G Battaglia Cristina C

PloS one 20130715 7

Congenital hyperinsulinism of infancy (CHI) is a rare disorder characterized by severe hypoglycemia due to inappropriate insulin secretion. The genetic causes of CHI have been found in genes regulating insulin secretion from pancreatic β-cells; recessive inactivating mutations in the ABCC8 and KCNJ11 genes represent the most common events. Despite the advances in understanding the molecular pathogenesis of CHI, specific genetic determinants in about 50 % of the CHI patients remain unknown, sugge ...[more]

PMID: 23869231

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Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Dataset Information

Whole genome SNP genotyping and exome sequencing reveal novel genetic variants and putative causative genes in congenital hyperinsulinism.

Publications

Whole genome SNP genotyping and exome sequencing reveal novel genetic variants and putative causative genes in congenital hyperinsulinism.

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