Six years' experience with LipidSeq: clinical and research learnings from a hybrid, targeted sequencing panel for dyslipidemias

Abstract

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.

Keywords: Dyslipidemia; Familial hypercholesterolemia; Hypertriglyceridemia; Lipid; Lipoprotein; Metabolic disorder; Targeted next-generation sequencing panel.

Fig. 1

Origin of samples sequenced with the LipidSeq panel. Internal samples (45%) come from patients who were referred to the Lipid Genetics Clinic for clinical care and provided consent to have their DNA sequenced. External samples (55%) are referred from all over the world for various reasons. 32% of samples are externally referred from clinical colleagues and are single patient or nuclear family samples sent for diagnosis, typically because they lack access or ability to pay for commercial testing. Each external patient or substitute decision-maker reviews the approved letter of information with the genetics clinic coordinator by telephone or Skype before providing consent. Another 16.2% of samples are sent for external research purposes, typically through academic collaborations; protocols and consent follow in accordance with the collaborating institution. The remaining 6.8% of samples are referred from industry, usually contracted by pharmaceutical companies requesting baseline molecular characterization of participants in clinical trials of investigational lipid-lowering therapies

Fig. 2

Overview of the patient and DNA sample journeys. Upon arrival to clinic (Visit 1), the patient undergoes a clinical assessment (left branch). During their clinic visit, blood is drawn for subsequent lipid tests, as well as genetic assessment (right branch). After DNA has been extracted and has undergone sequencing and bioinformatic processing, genetic factors that are relevant to the patient’s phenotype or present as risk factors for future health concerns are relayed back to the patient at a follow-up appointment. During the follow-up appointment (Visit 2), an additional clinical assessment is performed if required. Advice is given by combined clinical parameter with genetic results, if appropriate

Fig. 3

Breakdown of phenotypes from samples sequenced with the LipidSeq panel. The most prevalent phenotypes include FH and hypertriglyceridemia, accounting for ~ 70% of total samples. The remaining ~ 30% of samples are a mix of dyslipidemia and other metabolic phenotypes. Abbreviations: FH familial hypercholesterolemia, FCHL familial combined hyperlipidemia, HDL high-density lipoprotein, LDL low-density lipoprotein

Fig. 4

Breakdown of unique rare variants across 3262 samples sequenced. a This flowchart demonstrates the number of unique variants that are filtered out at each progressive stage of our rare variant analysis algorithm. A total list of annotated variants is available in Additional file 1: Table S3. b The ontology breakdown of 2205 possible deleterious or damaging variants is presented in this bar graph. Loss-of-function variants are considered to be those with ontologies of either frameshift, splice acceptor, splice donor, stop gain, or stop loss. c These bar graphs demonstrate the distribution of CADD PHRED-scaled scores for 1916 non-loss-of-function variants (left) and 289 loss-of-function variants (right). Abbreviations: indels insertions or deletions, LOF loss-of-function, MAF minor allele frequency