Genome-wide association study (GWAS) for late-onset Alzheimer’s disease (LOAD) was lacking in Chinese. In addition, prior GWASs using P-values to select single nucleotide polymorphisms (SNPs) have suffered from high false-positive and false-negative results. This two-stage GWAS used 10 performance metrics to select SNPs for LOAD in a Chinese elderly population, which was published on Nov. 2, 2016 by Scientific Reports Article Number 6:36155 (http://www.nature.com/articles/srep36155).
This is a collaborative research between Dr. Yen-Ching Karen Chen from National Taiwan University (Institute of Epidemiology and Preventive Medicine) with Drs. Chien-Jen Chen and Hwai-I Yang at Academia Sinica (Genomic Research Center). This case-control study recruited 713 LOAD cases and controls aged ≥65 from three teaching hospitals in northern Taiwan from 2007 to 2010.
For stage 1 (training set), 500,941 SNPs were used for analysis. 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. At stage 2 (validation set), 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; Ptrend < 0.0001).
This study, for the first time, compared SNPs selected by performance metrics with those selected by traditional P-values. While predicting LOAD risk, simultaneous testing using APOC1 rs4420638 and APOE e4 significantly improved the low sensitivity of APOE e4. The point-based genetic model including these two SNPs and important covariates successfully differentiated elders with different risk of LOAD. Our findings showed that performance metrics can be an ideal alternative for identifying SNPs for disease prediction and creating genetic tests with public health and clinical implications.