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Complex genetic determinants of hypertriglyceridemia

2019· article· en· W3177198911 on OpenAlexafffundabout
Jacqueline S. Dron, Jian Wang, Henian Cao, Adam D. McIntyre, Rob Hegele

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsRobarts Clinical TrialsWestern University
FundersSchulich School of Medicine and DentistryCanadian Institutes of Health ResearchGenome CanadaHeart and Stroke Foundation of Canada
KeywordsHypertriglyceridemiaSingle-nucleotide polymorphismBiologyGeneticsGenetic associationCopy-number variationBioinformaticsGenotypeGeneEndocrinologyTriglycerideGenomeCholesterol

Abstract

fetched live from OpenAlex

Hypertriglyceridemia (HTG) is a common dyslipidemia defined by elevated circulating blood triglyceride (TG) levels. Individuals with HTG are at risk for several health complications, which can include cardiovascular disease and in severe cases, acute pancreatitis. As the extreme manifestation of a physiological quantitative trait, HTG is influenced by genetic and non‐genetic factors. Genetic determinants include both rare single‐nucleotide variants (SNVs) and copy‐number variants (CNVs) in genes involved in TG metabolism, as well as common single‐nucleotide polymorphisms (SNPs) associated with TG levels. Despite understanding the complex genetic nature of HTG, the individual genetic influences on HTG have so far been examined only in a piecemeal manner. Here, we concurrently assessed rare SNVs and CNVs, and the accumulation of common SNPs in 104 Caucasian patients with mild‐to‐moderate HTG (defined as TG ≥3.3 and <10 mmol/L). As a reference “normolipidemic” group, we studied 503 healthy Caucasians from the open‐source 1000 Genomes Project. Patient DNA was subjected to next‐generation sequencing using our custom‐designed LipidSeq panel, which targets 73 genes and 185 SNPs associated with dyslipidemia and other metabolic disorders. We first screened for rare SNVs and CNVs in TG‐associated genes. For rare variants with likely large phenotypic effects, 1.0% of subjects had homozygous SNVs, and 12.5% had heterozygous SNVs or CNVs. In the normolipidemic controls, there were no homozygous SNVs, and 4.0% of subjects had heterozygous SNVs. We then assessed patients for an accumulation of common SNPs using a polygenic risk score. We identified an extreme score (defined as >90 th percentile of the normolipidemic population) in 27.9% of patients, compared to 9.5% of normolipidemic controls. Taken together, 41.3% of mild‐to‐moderate HTG patients had either a rare variant or high polygenic burden, compared to only 13.5% of controls. Compared to normolipidemic controls, mild‐to‐moderate HTG patients are 3.76‐fold (CI 95% 1.83–7.71; P<0.0001) more likely to carry a rare variant, 3.67‐fold (CI 95% 2.8–6.18; P<0.0001) more likely to have a high polygenic burden, and 4.51‐fold (CI 95% 2.83–7.19; P<0.0001) more likely to carry a TG‐related genetic factor, either a rare variant or polygenic burden of SNPs. We thus report the most in‐depth, systematic evaluation of genetic contributors of mild‐to‐moderate HTG to date. Next steps include: 1) identification of novel genetic determinants in patients negative for genetic determinants studied here; and 2) evaluations of genotype differences in clinical outcomes and intervention response. Support or Funding Information JSD is supported by the Canadian Institutes of Health Research (Doctoral Research Award) and the Schulich School of Medicine and Dentistry (Cobban Student Award in Heart and Stroke Research, and Nellie L. Farthing Memorial Fellowship in the Medical Science). RAH is supported by the Jacob J. Wolfe Distinguished Medical Research Chair, the Edith Schulich Vinet Research Chair in Human Genetics, and the Martha G. Blackburn Chair in Cardiovascular Research. RAH has received operating grants from the Canadian Institutes of Health Research, the Heart and Stroke Foundation, and Genome Canada. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.277
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes3
Has abstractyes

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