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Record W2912037782 · doi:10.1161/circ.133.suppl_1.mp84

Abstract MP84: Trans-ethnic Meta-analysis of Exome Chip Data Reveals Novel Low-frequency Variants Contributing to Central Adiposity

2016· article· en· W2912037782 on OpenAlexaff
Kristin L. Young, Anne E. Justice, Tugce Karaderi, Heather M. Highland, Mariaelisa Graff, Valérie Turcot, Paul L. Auer, Nancy L. Heard‐Costa, Claudia Schurmann, Yingchang Lu, Dorota Pasko, L. Adrienne Cupples, Caroline S. Fox, Thomas W. Winkler, R. Douglas Scott, Mark I. McCarthy, Karen L. Mohlke, Ruth J. F. Loos, Kari E. North, Ingrid B. Borecki, Cecilia M. Lindgren

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMinor allele frequencyMeta-analysisExomeMedicineGeneticsAllele frequencyExome sequencingAlleleOncologyGeneInternal medicineDemographyBiologyMutation

Abstract

fetched live from OpenAlex

Central adiposity is a leading risk factor for cardiovascular disease, and genetic factors contribute both to fat distribution, measured as waist-to-hip ratio adjusted for BMI (WHRa), and to differences in central adiposity prevalence. To date, 49 loci have been associated with WHRa, based on studies of common [minor allele frequency (MAF) ≥5%] single nucleotide variants (SNVs), primarily in European descent populations. Our aim was to identify low frequency (LFV: MAF <5%) and rare (RV: MAF <1%) coding variants associated with WHRa using Exome-Chip data from 344,369 individuals of European (84%), South Asian (8%), African (5%), East Asian(2%), and Hispanic/Latino (1%) ancestry. We performed fixed effects meta-analyses of study-specific WHRa associations stratified by sex and ancestry and then combined across strata for both SNV and gene-based results. We used a strict definition of variants annotated as damaging by 5 algorithms to perform gene-based analyses using the sequence kernel association test (SKAT). Analyses included up to 284,499 SNVs (218,195 with MAF<5%), and 15,063 genes with at least one SNV that met our inclusion criteria. Five LFVs reached chip-wide significance (CWS: P<2.5E-7) in our all ancestry sex-combined analyses, including one novel non-synonymous LFV in RAPGEF3 [MAF=0.01, β (SE) = -0.09 (0.012), P=1.28E-13]. In addition, one novel RV reached CWS in men for UGGT2 [MAF<0.01, β (SE) = -0.142 (0.025), P=9.71E-9], and one RV reached CWS in women for ACVR1C [MAF<0.01, β (SE) = -0.09 (0.018), P=1.09E-7]. Gene-based analyses identified RAPGEF3 (P=1.18E-11) as significantly associated with WHRa in the all ancestry sex combined analyses after correction for multiple tests (P<2.5E-6), though conditional analysis revealed that this result is driven by the top SV identified in this region. RAPGEF3 also shows a significant association (p=4.68E-12) in all ancestry, sex combined gene-based analysis of BMI. RAPGEF3 is expressed in subcutaneous and visceral adipose tissue, and has been implicated in insulin regulation. RAPGEF3 plays a role in the GLP1 pathway, which controls insulin secretion in response to blood glucose concentration. Our results highlight the importance of large-scale genomic studies for identifying LFV and RV influencing central fat distribution. Understanding these genetic effects may provide insights into the progression of central adiposity and highlight potential population-specific variants that increase susceptibility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.010
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.021
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.087
GPT teacher head0.314
Teacher spread0.226 · 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 designMeta-analysis
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
Published2016
Admission routes1
Has abstractyes

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