Fine Mapping of the Insulin-Induced Gene 2 Identifies a Variant Associated With LDL Cholesterol and Total Apolipoprotein B Levels
Bibliographic record
Abstract
BACKGROUND: In a whole-genome scan, a single nucleotide polymorphism (SNP) (rs7566605) upstream of the insulin-induced gene 2 (INSIG2) was shown to influence body mass index and obesity in the Framingham Heart Study, with replication of these results in an additional 4 of 5 studies. However, other studies could not replicate the association. Because INSIG2 plays an important role in cholesterol biosynthesis, we hypothesized that human INSIG2 variants might play a role in the regulation of plasma lipid and lipoprotein levels. METHODS AND RESULTS: We selected tagging SNPs spanning >100 kb of INSIG2 locus and sequenced 18 434 base pairs to discover novel SNPs. Thirty-two SNPs were genotyped in 645 individuals from the Quebec Family Study. Two SNPs (rs10490626 and rs12464355) were associated with plasma low-density lipoprotein cholesterol (LDL-C) (P<0.0015) and total apolipoprotein B (apoB) levels (P<0.014), whereas no association was found between any SNP and body mass index. We replicated the finding of rs10490626 for both LDL-C and total apoB in additional study samples, including 758 individuals from Saguenay-Lac St. Jean, Quebec (P=0.040 for LDL-C, P=0.044 for apoB), 3247 Europeans (P=0.028 for LDL-C, P=0.030 for apoB), and 1695 South Asians (P=0.0036 for LDL-C, P=0.034 for apoB) from the INTERHEART study (for LDL-C, the combined 2-sided P=6.2×10⁻⁵ and for total apoB, P=0.0011). Furthermore, we identified a variant in the human sorbin and SH(3)-domain-containing-1 gene that was associated with INSIG2 mRNA levels, and this SNP was shown to act in combination with rs10490626 to affect LDL-C (P=0.022) in the Quebec Family Study and in INTERHEART South Asians (P=0.019) and Europeans (P=0.052). CONCLUSION: These results suggest that INSIG2 genetic variants may have a more direct role in lipid and lipoprotein metabolism than in obesity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".