Application of longitudinal follow-up data increases power in the identification of genetic loci for type 2 diabetes

Background Genome-wide association studies (GWASs) have identified several genetically susceptible loci associated with type 2 diabetes mellitus (T2DM). However, a large sample size is required to detect such loci, posing challenges for the application of GWASs in translational research. Result Herein, a meta-analysis of repeat GWASs (MERG) was developed to increase the power for susceptible loci discovery. Repeat GWASs refer to GWASs that are performed with follow-up phenotypes of the study population. As the repeat GWAS results have a dependency structure because of overlapping samples between follow-ups, they were integrated into the meta-analysis using an empirical estimation of the structure using a resampling process. The simulation analysis results indicated that the MERG had acceptable type 1 error and statistical power. In the exome data analysis for T2DM, the MERG detected 14 susceptible loci with high reproducibility. Of the 14 significant loci, 12 were identified in previous GWASs. However, conventional GWASs using the same data identified only two significant loci. After clumping, six loci were selected, four of which (rs2206734, rs2233580, rs2237895, and rs2237892) showed reproducibility. Moreover, the mapped genes ( MRGPRX3 and RPL24P7 ) at the remaining two loci (rs12291017 and rs4334660) were associated with T2DM. Conclusion MERG is a powerful method for identifying the genetic loci associated with T2DM in terms of power and reproducibility. This provides additional opportunities to identify novel loci for other traits.