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Abstract BackgroundImaging, cognitive and fluid data have been widely studied to identify quantitative biomarkers that can help predict the status and progression of Alzheimer’s disease (AD). However, it is still an underexplored topic whether there exist subpopulations with different genetic profiles across which the biomarker‐based prediction models may vary. We propose to use the Chow test (Chow 1960 Econometrica 28(3)) to perform genetically stratified analyses for identifying SNP‐based subpopulations coupled with precision AD biomarkers with varying effects on future diagnosis in these subpopulations. The investigation of such SNPs and precision biomarkers may eventually pave the way for increased customization of AD care. MethodParticipants included 1,324 subjects from the ADNI cohort with both AD biomarker and genotyping data available (http://www.pi4cs.org/qt‐pad‐challenge). 30 significant (P < 1.5E‐278) AD SNPs were sourced from (Jansen 2019 NatGen). Chow tests were performed to determine whether each of baseline visit measures of 16 AD biomarkers predicted AD diagnosis at the three‐year visit with varying slopes when stratifying upon the allelic dosage of each of 30 chosen SNPs. Bonferroni correction (P < 1.04E‐4) was employed to correct for multiple comparisons. ResultMultiple SNP‐biomarker pairs showed significant genetically driven deviations in the regression coefficients when predicting diagnosis in three years using baseline biomarkers (Figure 1). Top SNP hits involved rs769449 (Chr 19,APOE) and rs7561528 (Chr 2,LOC105373605), and almost all 16 studied biomarkers demonstrated differential slopes in different genotype groups to predict diagnosis in three years. To examine the details of these top findings, the regression coefficients calculated for each of the five most significant biomarkers of both SNPs were bootstrapped and plotted in Figure 2. ConclusionGenetic analysis of AD candidate SNPs in conjunction with AD biomarker data via the Chow test identified several SNPs coupled with precision AD biomarkers with varying prognosis effects in the corresponding genotype groups. These findings provide valuable information to reveal disease heterogeneity and help facilitate precision medicine.