Performance Metrics for Selecting Single Nucleotide Polymorphisms in Late-onset Alzheimer's Disease

Previous genome-wide association studies using P-values to select single nucleotide polymorphisms (SNPs) have suffered from high false-positive and false-negative results. This case-control study recruited 713 late-onset Alzheimer's disease (LOAD) cases and controls aged ≥65 from three teaching hospitals in northern Taiwan from 2007 to 2010. Performance metrics were used to select SNPs in stage 1, which were then genotyped to another dataset (stage 2). Four SNPs (CPXM2 rs2362967, APOC1 rs4420638, ZNF521 rs7230380, and rs12965520) were identified for LOAD by both traditional P-values (without correcting for multiple tests) and performance metrics. After correction for multiple tests, no SNPs were identified by traditional P-values. Simultaneous testing of APOE e4 and APOC1 rs4420638 (the SNP with the best performance in the performance metrics) significantly improved the low sensitivity of APOE e4 from 0.50 to 0.78. A point-based genetic model including these 2 SNPs and important covariates was constructed. Compared with elders with low-risks score (0-6), elders belonging to moderate-risk (score = 7-11) and high-risk (score = 12-18) groups showed a significantly increased risk of LOAD (adjusted odds ratio = 7.80 and 46.93, respectively; P_trend < 0.0001). Performance metrics allow for identification of markers with moderate effect and are useful for creating genetic tests with clinical and public health implications.