Partial LPL deletions: rare copy-number variants contributing towards severe hypertriglyceridemia.
Jacqueline S DronJian WangAdam D McIntyreHenian CaoJohn F RobinsonP Barton DuellPriya ManjooJames FengIrina MovsesyanMary J MalloyClive R PullingerJohn P KaneRobert A HegelePublished in: Journal of lipid research (2019)
Severe hypertriglyceridemia (HTG) is a relatively common form of dyslipidemia with a complex pathophysiology and serious health complications. HTG can develop in the presence of rare genetic factors disrupting genes involved in the triglyceride (TG) metabolic pathway, including large-scale copy-number variants (CNVs). Improvements in next-generation sequencing technologies and bioinformatic analyses have better allowed assessment of CNVs as possible causes of or contributors to severe HTG. We screened targeted sequencing data of 632 patients with severe HTG and identified partial deletions of the LPL gene, encoding the central enzyme involved in the metabolism of TG-rich lipoproteins, in four individuals (0.63%). We confirmed the genomic breakpoints in each patient with Sanger sequencing. Three patients carried an identical heterozygous deletion spanning the 5' untranslated region (UTR) to LPL exon 2, and one patient carried a heterozygous deletion spanning the 5'UTR to LPL exon 1. All four heterozygous CNV carriers were determined to have multifactorial severe HTG. The predicted null nature of our identified LPL deletions may contribute to relatively higher TG levels and a more severe clinical phenotype than other forms of genetic variation associated with the disease, particularly in the polygenic state. The identification of novel CNVs in patients with severe HTG suggests that methods for CNV detection should be included in the diagnostic workup and molecular genetic evaluation of patients with high TG levels.
Keyphrases
- copy number
- mitochondrial dna
- early onset
- genome wide
- healthcare
- public health
- end stage renal disease
- single cell
- mental health
- case report
- electronic health record
- ejection fraction
- drug delivery
- risk assessment
- newly diagnosed
- social media
- chronic kidney disease
- machine learning
- deep learning
- peritoneal dialysis
- artificial intelligence
- circulating tumor cells