BACKGROUND: Next-generation sequencing (NGS) is nearing routine clinical application, especially for diagnosis of rare monogenic cardiovascular diseases. But NGS uncovers so much variation in an individual genome that filtering steps are required to streamline data management. The first step is to determine whether a potential disease-causing variant has been observed previously in affected patients. METHODS: To facilitate this step for lipid disorders, we developed the Western Database of Lipid Variants (WDLV) of 2776 variants in 24 genes that cause monogenic dyslipoproteinemias, including conditions characterized primarily by either high or low low-density lipoprotein cholesterol, high or low high-density lipoprotein cholesterol, high triglyceride, and some miscellaneous disorders. We incorporated quality-control steps to maximize the likelihood that a listed mutation was disease causing. RESULTS: The details of each mutation found in a dyslipidemia, together with a mutation map of the causative genes, are shown in graphical display items. CONCLUSIONS: WDLV will help clinicians and researchers determine the potential pathogenicity of mutations discovered by DNA sequencing of patients or research participants with lipid disorders.
BACKGROUND: Next-generation sequencing (NGS) is nearing routine clinical application, especially for diagnosis of rare monogenic cardiovascular diseases. But NGS uncovers so much variation in an individual genome that filtering steps are required to streamline data management. The first step is to determine whether a potential disease-causing variant has been observed previously in affected patients. METHODS: To facilitate this step for lipid disorders, we developed the Western Database of Lipid Variants (WDLV) of 2776 variants in 24 genes that cause monogenic dyslipoproteinemias, including conditions characterized primarily by either high or low low-density lipoprotein cholesterol, high or low high-density lipoprotein cholesterol, high triglyceride, and some miscellaneous disorders. We incorporated quality-control steps to maximize the likelihood that a listed mutation was disease causing. RESULTS: The details of each mutation found in a dyslipidemia, together with a mutation map of the causative genes, are shown in graphical display items. CONCLUSIONS: WDLV will help clinicians and researchers determine the potential pathogenicity of mutations discovered by DNA sequencing of patients or research participants with lipid disorders.
Authors: Christopher T Johansen; Joseph B Dubé; Melissa N Loyzer; Austin MacDonald; David E Carter; Adam D McIntyre; Henian Cao; Jian Wang; John F Robinson; Robert A Hegele Journal: J Lipid Res Date: 2014-02-06 Impact factor: 5.922
Authors: Jacqueline S Dron; Jian Wang; Adam D McIntyre; Michael A Iacocca; John F Robinson; Matthew R Ban; Henian Cao; Robert A Hegele Journal: BMC Med Genomics Date: 2020-02-10 Impact factor: 3.063