Wang Yi, Li Yi, Hao Meng, Liu Xiaoyu, Zhang Menghan, Wang Jiucun, Xiong Momiao, Shugart Yin Yao, Jin Li
Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Shanghai, China.
Human Phenome Institute, Fudan University, Shanghai, China.
Front Genet. 2019 Apr 9;10:319. doi: 10.3389/fgene.2019.00319. eCollection 2019.
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) 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.
全基因组关联研究（GWAS）已经鉴定出大量的遗传易感性位点，由于统计效力低和假阳性结果，小样本量的GWAS远未达到先前的预期。需要有效的统计方法来进一步改进对海量GWAS数据的分析。在此，我们提出了一种新的统计方法（稳健参考驱动关联检验），以大型公共数据库（gnomad）作为参考，减少对潜在群体分层的担忧。为了评估该统计方法在各种情况下的性能，我们模拟了多组样本量和频率来计算统计效力。此外，我们将我们的方法应用于几个真实数据集（银屑病全基因组关联数据集和精神分裂症全基因组关联数据集）来评估性能。仔细分析表明，我们新开发的统计方法优于几种先前开发的GWAS应用。重要的是，在对照-参考差异较小时，该统计方法比简单合并方法更稳健，因此可能检测到更多的关联信号。
