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Six years' experience with LipidSeq: clinical and research learnings from a hybrid, targeted sequencing panel for dyslipidemias.
Dron, Jacqueline S; Wang, Jian; McIntyre, Adam D; Iacocca, Michael A; Robinson, John F; Ban, Matthew R; Cao, Henian; Hegele, Robert A.
Affiliation
  • Dron JS; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON, N6A 5B7, Canada.
  • Wang J; Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond Street, London, ON, N6A 5B7, Canada.
  • McIntyre AD; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON, N6A 5B7, Canada.
  • Iacocca MA; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON, N6A 5B7, Canada.
  • Robinson JF; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON, N6A 5B7, Canada.
  • Ban MR; Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond Street, London, ON, N6A 5B7, Canada.
  • Cao H; Department of Biomedical Data Science, Stanford School of Medicine, Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA.
  • Hegele RA; Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, 1151 Richmond St, London, ON, N6A 5B7, Canada.
BMC Med Genomics ; 13(1): 23, 2020 02 10.
Article in En | MEDLINE | ID: mdl-32041611
BACKGROUND: In 2013, our laboratory designed a targeted sequencing panel, "LipidSeq", to study the genetic determinants of dyslipidemia and metabolic disorders. Over the last 6 years, we have analyzed 3262 patient samples obtained from our own Lipid Genetics Clinic and international colleagues. Here, we highlight our findings and discuss research benefits and clinical implications of our panel. METHODS: LipidSeq targets 69 genes and 185 single-nucleotide polymorphisms (SNPs) either causally related or associated with dyslipidemia and metabolic disorders. This design allows us to simultaneously evaluate monogenic-caused by rare single-nucleotide variants (SNVs) or copy-number variants (CNVs)-and polygenic forms of dyslipidemia. Polygenic determinants were assessed using three polygenic scores, one each for low-density lipoprotein cholesterol, triglyceride, and high-density lipoprotein cholesterol. RESULTS: Among 3262 patient samples evaluated, the majority had hypertriglyceridemia (40.1%) and familial hypercholesterolemia (28.3%). Across all samples, we identified 24,931 unique SNVs, including 2205 rare variants predicted disruptive to protein function, and 77 unique CNVs. Considering our own 1466 clinic patients, LipidSeq results have helped in diagnosis and improving treatment options. CONCLUSIONS: Our LipidSeq design based on ontology of lipid disorders has enabled robust detection of variants underlying monogenic and polygenic dyslipidemias. In more than 50 publications related to LipidSeq, we have described novel variants, the polygenic nature of many dyslipidemias-some previously thought to be primarily monogenic-and have uncovered novel mechanisms of disease. We further demonstrate several tangible clinical benefits of its use.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Multifactorial Inheritance / Polymorphism, Single Nucleotide / Dyslipidemias / DNA Copy Number Variations Type of study: Prognostic_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: BMC Med Genomics Journal subject: GENETICA MEDICA Year: 2020 Document type: Article Affiliation country: Canada Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Multifactorial Inheritance / Polymorphism, Single Nucleotide / Dyslipidemias / DNA Copy Number Variations Type of study: Prognostic_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: BMC Med Genomics Journal subject: GENETICA MEDICA Year: 2020 Document type: Article Affiliation country: Canada Country of publication: United kingdom