General regression model: A "model-free" association test for quantitative traits allowing to test for the underlying genetic model.
HbA1c
genetic model
genome-wide association studies (GWAS)
linear regression
quantitative traits
Journal
Annals of human genetics
ISSN: 1469-1809
Titre abrégé: Ann Hum Genet
Pays: England
ID NLM: 0416661
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
20
07
2018
revised:
19
11
2019
accepted:
20
11
2019
pubmed:
14
12
2019
medline:
24
3
2021
entrez:
14
12
2019
Statut:
ppublish
Résumé
Most genome-wide association studies used genetic-model-based tests assuming an additive mode of inheritance, leading to underpowered association tests in case of departure from additivity. The general regression model (GRM) association test proposed by Fisher and Wilson in 1980 makes no assumption on the genetic model. Interestingly, it also allows formal testing of the underlying genetic model. We conducted a simulation study of quantitative traits to compare the power of the GRM test to the classical linear regression tests, the maximum of the three statistics (MAX), and the allele-based (allelic) tests. Simulations were performed on two samples sizes, using a large panel of genetic models, varying genetic models, minor allele frequencies, and the percentage of explained variance. In case of departure from additivity, the GRM was more powerful than the additive regression tests (power gain reaching 80%) and had similar power when the true model is additive. GRM was also as or more powerful than the MAX or allelic tests. The true simulated model was mostly retained by the GRM test. Application of GRM to HbA1c illustrates its gain in power. To conclude, GRM increases power to detect association for quantitative traits, allows determining the genetic model and is easily applicable.
Substances chimiques
Glycated Hemoglobin A
0
hemoglobin A1c protein, human
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
280-290Informations de copyright
© 2019 John Wiley & Sons Ltd/University College London.
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