10.3389/fgene.2019.00319.s010 Yi Wang Yi Wang Yi Li Yi Li Meng Hao Meng Hao Xiaoyu Liu Xiaoyu Liu Menghan Zhang Menghan Zhang Jiucun Wang Jiucun Wang Momiao Xiong Momiao Xiong Yin Yao Shugart Yin Yao Shugart Li Jin Li Jin Table_6_Robust Reference Powered Association Test of Genome-Wide Association Studies.xls Frontiers 2019 GWAS reference public datasets test statistic T online tool 2019-04-09 07:38:46 Dataset https://frontiersin.figshare.com/articles/dataset/Table_6_Robust_Reference_Powered_Association_Test_of_Genome-Wide_Association_Studies_xls/7969532 <p>Genome-wide association studies (GWASs) have identified abundant genetic susceptibility loci, GWAS of small sample size are far less from meeting the previous expectations due to low statistical power and false positive results. Effective statistical methods are required to further improve the analyses of massive GWAS data. Here we presented a new statistic (Robust Reference Powered Association Test<sup>1</sup>) to use large public database (gnomad) as reference to reduce concern of potential population stratification. To evaluate the performance of this statistic for various situations, we simulated multiple sets of sample size and frequencies to compute statistical power. Furthermore, we applied our method to several real datasets (psoriasis genome-wide association datasets and schizophrenia genome-wide association dataset) to evaluate the performance. Careful analyses indicated that our newly developed statistic outperformed several previously developed GWAS applications. Importantly, this statistic is more robust than naive merging method in the presence of small control-reference differentiation, therefore likely to detect more association signals.</p>