Browsing by Subject "KCNQ1 Potassium Channel"

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    A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese.
    (PLoS Genet, 2010-02-19) Tsai, Fuu-Jen; Yang, Chi-Fan; Chen, Ching-Chu; Chuang, Lee-Ming; Lu, Chieh-Hsiang; Chang, Chwen-Tzuei; Wang, Tzu-Yuan; Chen, Rong-Hsing; Shiu, Chiung-Fang; Liu, Yi-Min; Chang, Chih-Chun; Chen, Pei; Chen, Chien-Hsiun; Fann, Cathy SJ; Chen, Yuan-Tsong; Wu, Jer-Yuarn
    To investigate the underlying mechanisms of T2D pathogenesis, we looked for diabetes susceptibility genes that increase the risk of type 2 diabetes (T2D) in a Han Chinese population. A two-stage genome-wide association (GWA) study was conducted, in which 995 patients and 894 controls were genotyped using the Illumina HumanHap550-Duo BeadChip for the first genome scan stage. This was further replicated in 1,803 patients and 1,473 controls in stage 2. We found two loci not previously associated with diabetes susceptibility in and around the genes protein tyrosine phosphatase receptor type D (PTPRD) (P = 8.54x10(-10); odds ratio [OR] = 1.57; 95% confidence interval [CI] = 1.36-1.82), and serine racemase (SRR) (P = 3.06x10(-9); OR = 1.28; 95% CI = 1.18-1.39). We also confirmed that variants in KCNQ1 were associated with T2D risk, with the strongest signal at rs2237895 (P = 9.65x10(-10); OR = 1.29, 95% CI = 1.19-1.40). By identifying two novel genetic susceptibility loci in a Han Chinese population and confirming the involvement of KCNQ1, which was previously reported to be associated with T2D in Japanese and European descent populations, our results may lead to a better understanding of differences in the molecular pathogenesis of T2D among various populations.
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    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.
    (Journal of visualized experiments : JoVE, 2019-01-16) Jones, Edward G; Landstrom, Andrew P
    Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population "background" variation). This method can identify amino acid residue location "hotspots" of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.