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>