A genome-wide linkage scan for familial partial lipodystrophy susceptibility genes in a German kindreds
Bibliographic record
Abstract
Background: In Dunnigan-type familial partial lipodystrophy (FPLD) patients are born with normal fat distribution, but subcutaneous fat from extremities and gluteal regions are lost during puberty. The abnormal fat distribution leads to the development of metabolic syndrome (MetS), a cluster of phenotypes including hyperglycemia, dyslipidemia, hypertension, and visceral obesity. The study of FPLD as a monogenic model of MetS may uncover genetic risk factors of the common MetS which affects ~30% of adult North Americans. Two molecular forms of FPLD have been identified including FPLD2, resulting from heterozygous mutations in the LMNA gene, and FPLD3, resulting from both heterozygous dominant negative and haploinsufficiency mutations in the PPARG gene. However, many patients with clinically diagnosed FPLD have no mutation in either LMNA or PPARG, suggesting the involvement of additional genes in FPLD etiology. Methods: Here, we report the results of an Affymetrix 10K GeneChip microarray genome-wide linkage analysis study of a German kindred displaying the FPLD phenotype and no known lipodystrophy-causing mutations. Results: The investigation identified three chromosomal loci, namely 1q, 3p, and 9q, with non-parametric logarithm of odds (NPL) scores >2.7. While not meeting the criteria for genome-wide significance, it is interesting to note that the 1q and 3p peaks contain the LMNA and PPARG genes respectively. Conclusions: Three possible conclusions can be drawn from these results: 1) the peaks identified are spurious findings, 2) additional genes physically close to LMNA, PPARG, or within 9q, are involved in FPLD etiology, or 3) alternative disease causing mechanisms not identified by standard exon sequencing approaches, such as promoter mutations, alternative splicing, or epigenetics, are also responsible for FPLD.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".