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Unlike tests for marginal genetic association, set-based tests for gene-environment interaction face the challenges of a potentially misspecified and high-dimensional main effect model under the null hypothesis.
A fractional factorial design in this class has a desirable property that parameters of the main effect model are simultaneously identifiable.
Results of the main effect model appear in Table 5.
The COO impact on derived utility is reduced in this income effect model compared to the main effect model (0.7311 to 0.5261 for China and 1.077 to 0.5132 for California).
Results from the main effect model are showed in Table 4 (Model I).
The main effect model forces the protective effect to be constant across all ages in adolescence and young adulthood.
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Therefore the main-effect model seems to be adequate.
The main-effect model detected one SNP, rs12934561a, significantly associated with shock.
The main-effect model detected no SNP that was significantly associated with death at day 60.
The main-effect model detected two SNPs, rs11861531a and rs11641468a, that were significantly associated with DOVs.
The main-effect model detected rs9927163 with a marginally significant dominance effect, which was also present in the interacting model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com