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Power comparison of different methods to detect genetic effects and gene-environment interactions.

Kazma R, Dizier MH, Guilloud-Bataille M, Bonaïti-Pellié C, Génin E - BMC Proc (2007)

Bottom Line: However, their respective performances have rarely been compared.Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction.The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively).

View Article: PubMed Central - HTML - PubMed

Affiliation: Université Paris-Sud, UMR-S 535, Villejuif, 94817, France. kazma@vjf.inserm.fr

ABSTRACT
Identifying gene-environment (G x E) interactions has become a crucial issue in the past decades. Different methods have been proposed to test for G x E interactions in the framework of linkage or association testing. However, their respective performances have rarely been compared. Using Genetic Analysis Workshop 15 simulated data, we compared the power of four methods: one based on affected sib pairs that tests for linkage and interaction (the mean interaction test) and three methods that test for association and/or interaction: a case-control test, a case-only test, and a log-linear approach based on case-parent trios. Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction. The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively). The case-only design exhibits a 95% power to detect G x E interaction but the type I error rate is increased.

No MeSH data available.


Comparison of the p-values of the interaction tests. -ln(p) are reported for the case-only design (green plot), the case-control design (red plot) and the log-linear-modeling method (blue plot) over the first 25 replicates.
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Figure 2: Comparison of the p-values of the interaction tests. -ln(p) are reported for the case-only design (green plot), the case-control design (red plot) and the log-linear-modeling method (blue plot) over the first 25 replicates.

Mentions: Concerning the detection of the G × E interaction, we found that the case-only design is by far the most powerful test. It reaches 95% power; the case-control design only reaches 69%, the log-linear approach, 53%; and the linkage test (MIT), 12%. When constraining the number of genotyped individuals to be the same for the three association methods, the differences in power are even more pronounced. Figure 2 shows the p-values of the G × E interaction test for the log-linear-modeling approach, the case-control, and the case-only designs for the first 25 replicates. We observe that it is generally in the same replicates that the different methods give the most significant results, with the highest significance achieved for the case-only method.


Power comparison of different methods to detect genetic effects and gene-environment interactions.

Kazma R, Dizier MH, Guilloud-Bataille M, Bonaïti-Pellié C, Génin E - BMC Proc (2007)

Comparison of the p-values of the interaction tests. -ln(p) are reported for the case-only design (green plot), the case-control design (red plot) and the log-linear-modeling method (blue plot) over the first 25 replicates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367459&req=5

Figure 2: Comparison of the p-values of the interaction tests. -ln(p) are reported for the case-only design (green plot), the case-control design (red plot) and the log-linear-modeling method (blue plot) over the first 25 replicates.
Mentions: Concerning the detection of the G × E interaction, we found that the case-only design is by far the most powerful test. It reaches 95% power; the case-control design only reaches 69%, the log-linear approach, 53%; and the linkage test (MIT), 12%. When constraining the number of genotyped individuals to be the same for the three association methods, the differences in power are even more pronounced. Figure 2 shows the p-values of the G × E interaction test for the log-linear-modeling approach, the case-control, and the case-only designs for the first 25 replicates. We observe that it is generally in the same replicates that the different methods give the most significant results, with the highest significance achieved for the case-only method.

Bottom Line: However, their respective performances have rarely been compared.Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction.The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively).

View Article: PubMed Central - HTML - PubMed

Affiliation: Université Paris-Sud, UMR-S 535, Villejuif, 94817, France. kazma@vjf.inserm.fr

ABSTRACT
Identifying gene-environment (G x E) interactions has become a crucial issue in the past decades. Different methods have been proposed to test for G x E interactions in the framework of linkage or association testing. However, their respective performances have rarely been compared. Using Genetic Analysis Workshop 15 simulated data, we compared the power of four methods: one based on affected sib pairs that tests for linkage and interaction (the mean interaction test) and three methods that test for association and/or interaction: a case-control test, a case-only test, and a log-linear approach based on case-parent trios. Results show that for the particular model of interaction between tobacco use and Locus B simulated here, the mean interaction test has poor power to detect either the genetic effect or the interaction. The association studies, i.e., the log-linear-modeling approach and the case-control method, are more powerful to detect the genetic effect (power of 78% and 95%, respectively) and taking into account interaction moderately increases the power (increase of 9% and 3%, respectively). The case-only design exhibits a 95% power to detect G x E interaction but the type I error rate is increased.

No MeSH data available.