General regression model: A "model-free" association test for quantitative traits allowing to test for the underlying genetic model

被引：1
|
作者：
Gloaguen, Emilie [1 ,2 ]
Dizier, Marie-Helene [2 ,3 ]
Boissel, Mathilde [4 ,5 ,6 ]
Rocheleau, Ghislain [4 ,5 ,6 ]
Canouil, Mickael [4 ,5 ,6 ]
Froguel, Philippe [4 ,5 ,6 ,7 ,18 ]
Tichet, Jean [8 ,21 ]
Roussel, Ronan [2 ,9 ,10 ,15 ]
Julier, Cecile [1 ,2 ]
Balkau, Beverley [11 ,14 ]
Mathieu, Flavie [12 ,13 ]
Ducimetiere, Pierre [14 ]
Eschwege, Eveline [14 ]
Alhenc-Gelas, Francois [15 ]
Fumeron, Frederic [15 ]
Marre, Michel [15 ]
Gallois, Yves [16 ]
Girault, Andree [16 ]
Bonnet, Fabrice [17 ]
Bonnefond, Amelie [18 ]
Cauchi, Stephane [18 ]
Alencon, Angers [19 ]
Blois, Caen [19 ]
Chartres, Chateauroux [19 ]
Cholet, Le Mans [19 ]
Orleans, Tours [19 ]
Cogneau, Joel [20 ]
Born, Catherine [21 ]
Caces, Emile [21 ]
Cailleau, Martine [21 ]
Copin, Nane [21 ]
Lantieri, Olivier [21 ]
Moreau, Jean-Guy [21 ]
Rakotozafy, Fabienne [21 ]
Vol, Sylviane [21 ]
机构：
[1] Inserm UMRS 958, Paris, France
[2] Univ Paris Diderot, Sorbonne Paris Cite, Paris, France
[3] Inserm UMR 946, Paris, France
[4] Univ Lille, UMR 8199 EGID, Lille, France
[5] CNRS, Paris, France
[6] Inst Pasteur, Lille, France
[7] Imperial Coll London, Dept Genom Common Dis, London, England
[8] IRSA, La Riche, France
[9] Ctr Rech Cordeliers, Inserm 01138, Paris, France
[10] Hop Bichat Claude Bernard, AP HP, Diabetol Endocrinol & Nutr Dept, DHU FIRE, Paris, France
[11] UVSQ UP, Team 5, CESP, Inserm U1018, Villejuif, France
[12] DISC, Inserm Siege, Mission Assoc Rech & Soc, 101 Rue Tolbiac, F-75013 Paris, France
[13] Sorbonne Paris Cite, Paris Diderot, Paris, France
[14] Inserm U1018, Villejuif, France
[15] Inserm U1138, Paris, France
[16] CHU Angers, Angers, France
[17] CHU Rennes, Rennes, France
[18] CNRS, UMR8090, Paris, France
[19] Centres Examens Sante Assurance Malad, Paris, France
[20] Inst Rech Med Gen, Paris, France
[21] Inst Interreg Sante IRSA, Paris, France
来源：
ANNALS OF HUMAN GENETICS | 2020年 / 84卷 / 03期
关键词：
genetic model; genome-wide association studies (GWAS); HbA1c; linear regression; quantitative traits; GENOME-WIDE ASSOCIATION; SAMPLE-SIZE; TREND TESTS; LOCI; POWER; COMPLEX; MARKERS; MTNR1B; FRAMEWORK; VARIANTS;
D O I：
10.1111/ahg.12372
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
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.
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页码：280 / 290
页数：11
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