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General regression model: A “model‐free” association test for quantitative traits allowing to test for the underlying genetic model
Author(s) -
Gloaguen Emilie,
Dizier MarieHélène,
Boissel Mathilde,
Rocheleau Ghislain,
Canouil Mickaël,
Froguel Philippe,
Tichet Jean,
Roussel Ronan,
Julier Cécile,
Balkau Beverley,
Mathieu Flavie
Publication year - 2020
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/ahg.12372
Subject(s) - genetic model , statistics , genetic association , regression analysis , additive model , association (psychology) , inheritance (genetic algorithm) , additive genetic effects , regression , linear model , linear regression , mathematics , genetics , biology , heritability , single nucleotide polymorphism , psychology , genotype , gene , psychotherapist
Abstract 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.