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Articles by N. J Schork
Total Records ( 3 ) for N. J Schork
  A. L Beitelshees , H Navare , D Wang , Y Gong , J Wessel , J. I Moss , T. Y Langaee , R. M Cooper DeHoff , W Sadee , C. J Pepine , N. J Schork and J. A. Johnson

Background— The gene encoding the target of calcium channel blockers, the 1c-subunit of the L-type calcium channel (CACNA1C), has not been well characterized, and only small pharmacogenetic studies testing this gene have been published to date.

Methods and Results— Resequencing of CACNA1C was performed followed by a nested case-control study of the INternational VErapamil SR/trandolapril STudy (INVEST) GENEtic Substudy (INVEST-GENES). Of 46 polymorphisms identified, 8 were assessed in the INVEST-GENES. Rs1051375 was found to have a significant interaction with treatment strategy (P=0.0001). Rs1051375 A/A genotype was associated with a 46% reduction in the primary outcome among those randomized to verapamil SR treatment, when compared with atenolol treatment (odds ratio 0.54 95% CI 0.32 to 0.92). In heterozygous A/G individuals, there was no difference in the occurrence of the primary outcome when randomized to verapamil SR versus atenolol treatment (odds ratio 1.47 95% CI 0.86 to 2.53), whereas homozygous G/G individuals had a greater than 4-fold increased risk of the primary outcome with verapamil treatment compared with those randomized to atenolol treatment (odds ratio 4.59 95% CI 1.67 to 12.67). We did not identify allelic expression imbalance or differences in mRNA expression in heart tissue by rs1051375 genotype.

Conclusions— Variation in CACNA1C is associated with treatment response among hypertensive patients with stable coronary artery disease. Our data suggest a genetically defined group of patients that benefit most from calcium channel blocker therapy, a group that benefits most from β-blocker therapy, and a third group in which calcium channel blocker and β-blocker therapy are equivalent.

  A Torkamani , B Dean , N. J Schork and E. A. Thomas

We performed integrated gene coexpression network analysis on two large microarray-based brain gene expression data sets generated from the prefrontal cortex obtained post-mortem from 101 subjects, 47 subjects with schizophrenia and 54 normal control subjects, ranging in age from 19 to 81 years. Twenty-eight modules of coexpressed genes with functional interpretations were detected in both normal subjects and those with schizophrenia. Significant overlap of "case" and "control" module composition was observed, indicating that extensive differences in underlying molecular connectivity are not likely driving pathology in schizophrenia. Modules of coexpressed genes were characterized according to disease association, cell type specificity, and the effects of aging. We find that genes with altered expression in schizophrenia clustered into distinct coexpression networks and that these were associated primarily with neurons. We further identified a robust effect of age on gene expression modules that differentiates normal subjects from those with schizophrenia. In particular, we report that normal age-related decreases in genes related to central nervous system developmental processes, including neurite outgrowth, neuronal differentiation, and dopamine-related cellular signaling, do not occur in subjects with schizophrenia during the aging process. Extrapolating these findings to earlier stages of development supports the concept that schizophrenia pathogenesis begins early in life and is associated with a failure of normal decreases in developmental-related gene expression. These findings provide a novel mechanism for the "developmental" hypothesis of schizophrenia on a molecular level.

  V Bansal , O Harismendy , R Tewhey , S. S Murray , N. J Schork , E. J Topol and K. A. Frazer

Next-generation sequencing technologies have made it possible to sequence targeted regions of the human genome in hundreds of individuals. Deep sequencing represents a powerful approach for the discovery of the complete spectrum of DNA sequence variants in functionally important genomic intervals. Current methods for single nucleotide polymorphism (SNP) detection are designed to detect SNPs from single individual sequence data sets. Here, we describe a novel method SNIP-Seq (single nucleotide polymorphism identification from population sequence data) that leverages sequence data from a population of individuals to detect SNPs and assign genotypes to individuals. To evaluate our method, we utilized sequence data from a 200-kilobase (kb) region on chromosome 9p21 of the human genome. This region was sequenced in 48 individuals (five sequenced in duplicate) using the Illumina GA platform. Using this data set, we demonstrate that our method is highly accurate for detecting variants and can filter out false SNPs that are attributable to sequencing errors. The concordance of sequencing-based genotype assignments between duplicate samples was 98.8%. The 200-kb region was independently sequenced to a high depth of coverage using two sequence pools containing the 48 individuals. Many of the novel SNPs identified by SNIP-Seq from the individual sequencing were validated by the pooled sequencing data and were subsequently confirmed by Sanger sequencing. We estimate that SNIP-Seq achieves a low false-positive rate of ~2%, improving upon the higher false-positive rate for existing methods that do not utilize population sequence data. Collectively, these results suggest that analysis of population sequencing data is a powerful approach for the accurate detection of SNPs and the assignment of genotypes to individual samples.

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