SNP characteristics and validation success in genome wide association studies.
SNP characteristics and validation success in genome wide association studies.
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DOI：
10.1007/s00439-021-02407-8
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发表时间：
2022-03
期刊：
影响因子：
5.3
通讯作者：
Gorlov IP
中科院分区：
文献类型：
--
作者：
Gorlova OY;Xiao X;Tsavachidis S;Amos CI;Gorlov IP
全基因组关联研究（GWAS）已经鉴定出数万种与人类疾病和特征相关的单核苷酸多态性（SNP）。GWAS发现的很大一部分可能是假阳性。真阳性的金标准是独立验证。本研究的目的是确定与验证成功相关的SNP特征。在分析中使用了来自已发表的GWAS目录的汇总统计数据。由于我们的目标是分析可重复性，因此我们专注于至少10种GWAS靶向的疾病/表型。GWAS根据发布时间排列在发现-验证对中，发现GWAS在验证之前发布。我们使用了四种不同严格性的验证成功定义。SNP特征与验证成功的关联在所有定义中是一致的。SNP验证的最强预测因子是发现GWAS的统计学显著性水平。效应量的大小以非线性方式与验证成功相关。与更罕见或更常见的SNP相比，风险等位基因频率在30-70%范围内的SNP显示出更高的验证成功率。错义、5' UTR、终止子获得和位于转录因子结合位点的SNP与基因间、内含子和同义SNP相比具有更高的验证成功率。验证成功率和网站的进化保护水平之间存在正相关。此外，当发现和验证GWAS针对同一种族时，验证成功率更高。验证成功的所有预测因素在多变量logistic回归模型中仍然显着，表明其独立贡献。总之，我们确定了预测GWAS命中验证成功的SNP特征。这些特征可用于选择SNP用于验证和下游功能研究。
Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30–70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5’ UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergeneic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.
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