Re: "The Association of Common Variants in PCSK1 With Obesity: A HuGE Review and Meta-Analysis"
Bibliographic record
Abstract
I read with interest the recently published meta-analysis by Stijnen et al. (1) in which the authors assessed the association of common genetic variants in the proprotein convertase subtilisine/kexin type 1 gene (PCSK1) with obesity traits and found a borderline significant association of the single nucleotide polymorphism (SNP) cluster rs6234–rs6235 with body mass index (BMI) and waist circumference but no association between the rs6232 polymorphism and these continuous traits. Additionally, they showed a stronger association of common genetic variants in PCSK1 with childhood obesity than with adult obesity; however, there was no evidence of an association between the SNP cluster rs6234–rs6235 and obesity in Asian populations. I have identified multiple important issues to take into consideration with regard to this work. First, 2 studies that were included in the meta-analysis overlapped. Namely, the French adult obesity case-control study reported by Benzinou et al. (2) (1,045 cases and 1,265 controls) and that reported by Meyre et al. (3) (695 cases and 731 controls) are not independent, and as a result, a subset of case and control individuals have been included twice in the study by Stijnen et al. Additionally, I feel that the study was underpowered for the overall meta-analysis of PCSK1 variants with continuous traits (i.e., BMI and waist circumference), particularly for the low-frequency variant rs6232, which has an allele frequency of approximately 5% in populations of European ancestry. The rs6232 variant was associated with BMI (β = 0.06, 95% confidence interval: −0.00, 0.12) and waist circumference (β = 0.37, 95% confidence interval: −0.00, 0.75), with an association close to the threshold of P = 0.05. Given the strong prior evidence of an association between PCSK1 polymorphisms and obesity traits, a larger sample size might have led to different conclusions. Similarly, the average β reported for the SNP cluster rs6234–rs6235 and waist circumference (β = 0.24, 95% confidence interval: 0.07, 0.41) was larger than that for BMI (β = 0.02, 95% confidence interval: 0.01, 0.03), which likely reflects significantly decreased precision from the limited number of studies used in the meta-analyses of waist circumference (n = 5) compared with the meta-analyses of BMI (n = 25). I therefore encourage readers to cautiously interpret the authors' claim that the association with rs6234–rs6235 was stronger for waist circumference than for BMI. Stinjen et al. may have substantially increased the power of their study by extracting in silico data from available genomewide association study data. In a similar meta-analysis of genetic variants in PCSK1 and obesity traits, Nead et al. (4) increased the sample size by 65% compared with the study by Stinjen et al. (up to 331,175 subjects), and the conclusions were different (e.g., Nead et al. reported a significant association between rs6232 and BMI). Additionally, Stinjen et al. acknowledged that the use of a classic random-effect models was conservative and reduced the power of their analysis. I agree and recommend the use of the global meta-analytic random-effects method recently developed by Lebrec et al. (5), which achieves more power and shows lower rates of false positives compared with classic methods. In their Introduction section, Stijnen et al. claimed that “[w]ith the start of the genomewide association study era, multiple studies were conducted in European, Asian, and African populations” but “PCSK1 SNPs were only marginally associated with BMI” (1, p. 1052). I do not agree with this statement because a strong association between a polymorphism at the PCSK1 locus (rs261967) and BMI (P = 5.1 × 10−9) was found in a recent genomewide association study in East Asian populations (6). Finally, Stinjen et al. reported that their meta-analysis “provides the first evidence that the association between PCSK1 rs6232 polymorphism and obesity is stronger for childhood obesity than for adult obesity” (1, p. 1051). Because evidence for age-dependent effects between the PCSK1 rs6232 variant and obesity have been reported in 2 independent studies in 2009 and 2013 (7, 8), I feel it may have been fair to cite these studies in the discussion. Conflict of interest: none declared.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".