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# Table reports the results for the categorical coding of diag

Table 3 reports the results for the categorical coding of diagnosis. The categorical coding is less hypothesis driven than the continuous coding because it allows for nonlinear interactions that are driven by only two groups (CN → MCI stable, CN → MCI progressor, and CN → AD). That said, this analysis only revealed significant coefficients for the factor CN → AD. Factors that distinguish MCI groups from CN do not show significant results for the interaction. The main SNP effect is not significant for any of these associations, where the main diagnosis effect is highly significant for all. As for the continuous model, the interactions of diagnosis with rs117253277 and rs6733839 are significant. In addition, SNP × diagnosis interactions are obtained for hippocampal asymmetry for rs1476679 and rs4147929. Both SNPs have been reported in AD GWASs. Interestingly, they have an inverted effect on asymmetry, with a positive interaction coefficient for rs1476679 (estimate = 0.255) and a negative coefficient for rs4147929 (estimate = −0.309). This is consistent with their respective role in AD, as reported in Table 1; rs4147929 is a risk locus, whereas rs1476679 is a preventive locus (25). The minor allele frequency and the tgf beta inhibitor count of rs117253277 are low, which may bias the results. For confirmation, we created random samples of similar sample size that matched the diagnostic distribution. The estimates for the interaction SNP × diagnosis over 50 repetitions are plotted in Supplemental Figure S2. The median of 2.08 and the mean of 2.01 are close to the estimate of the original model (2.36). Figure 1 displays the estimated intra- and interindividual change of the lateral shape asymmetry for the hippocampus, amygdala, and putamen with the associated loci. We show the genotype for control subjects and AD patients. Solid lines depict the global age effect, where the offset in intercept is determined by the genotype. Short line ticks depict the longitudinal intraindividual effect. The common pattern, except for rs117253277, is that genotype has a limited effect on the asymmetry of control subjects but a strong effect for AD patients. For rs117253277, the number of minor alleles also influences the asymmetry of control subjects, which illustrates the strong main effect of SNP (−0.268) in Table 3. For the hippocampus and amygdala, we observe a higher intraindividual increase in asymmetry compared with the interindividual increase (i.e., the age effect), as previously reported (8). Note that cross-sectional and longitudinal effects can vary substantially in Figure 1, which may result from positive selection bias for very old adults in cross-sectional studies. Table 4 provides statistical detail for the model with main effect of SNP only for quantitative coding (see Supplemental Table S2 for categorical coding). The association of rs683250 to putamen asymmetry is consistent with the interaction. A new association with amygdala asymmetry is found for the AD candidate SNP rs10948363. In a post hoc analysis, we added the interaction SNP × years-from-baseline to the models and evaluated whether the interaction was significant for the above identified pairings of asymmetry and SNP. In the model without SNP × diagnosis interaction, we found that the interaction SNP × years-from-baseline was significant for rs683250 and putamen asymmetry (β = .043, p = .00127). Figure 2 illustrates the intra- and interindividual change by genotype, which shows that minor alleles were associated with a steeper increase in asymmetry over time. In all the presented analyses, we included the number of APOE4 risk alleles as a covariate. In an additional analysis, we removed it from covariates and consider it as the SNP of interest. Across all the models, with and without interaction, as well as the different coding of diagnosis, there were no significant associations between asymmetry and APOE4. We used FDR correction to control for multiple testing, but almost all results would also be significant with the conservative Bonferroni correction (p value threshold of .00161). The only exception is the interaction of rs6733839 with diagnosis for putamen asymmetry in the quantitative coding, although the interaction would still be significant for the categorical coding. Note that we do not correct for multiple comparisons across models.