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Despite the popularity of case\u2010control and case\u2010only designs, longitudinal cohort studies that can capture time\u2010varying outcome and exposure information have long been recommended for gene\u2013environment (G\u00a0\u00d7\u00a0E) interactions. To date, literature on sampling designs for longitudinal studies of G\u00a0\u00d7\u00a0E interaction is quite limited. We therefore consider designs that can prioritize a subsample of the existing cohort for retrospective genotyping on the basis of currently available outcome, exposure, and covariate data. In this work, we propose stratified sampling based on summaries of individual exposures and outcome trajectories and develop a full conditional likelihood approach for estimation that adjusts for the biased sample. We compare the performance of our proposed design and analysis with combinations of different sampling designs and estimation approaches via simulation. We observe that the full conditional likelihood provides improved estimates for the G\u00a0\u00d7\u00a0E interaction and joint exposure effects over uncorrected complete\u2010case analysis, and the exposure enriched outcome trajectory dependent design outperforms other designs in terms of estimation efficiency and power for detection of the G\u00a0\u00d7\u00a0E interaction. We also illustrate our design and analysis using data from the Normative Aging Study, an ongoing longitudinal cohort study initiated by the Veterans Administration in 1963. 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