ABSTRACT

Traditionally, the p-value is the criterion for the cutoff threshold to determine significant markers in genome-wide association studies (GWASs). Choosing the best subset of markers for the best linear unbiased prediction (BLUP) for improved prediction ability (PA) has become an interesting issue. However, when dealing with many traits having the same marker information, the p-values’ themselves cannot be used as an obvious solution for having a confidence in GWAS and BLUP. We thus suggest a genomic estimated breeding value-assisted reduction method of the single nucleotide polymorphism (SNP) set (GARS) to address these difficulties. GARS is a BLUP-based SNP set decision presentation. The samples were Landrace pigs and the traits used were back fat thickness (BF) and daily weight gain (DWG). The prediction abilities (PAs) for BF and DWG for the entire SNP set were 0.8 and 0.8, respectively. By using the correlation between genomic estimated breeding values (GEBVs) and phenotypic values, selecting the cutoff threshold in GWAS and the best SNP subsets in BLUP was plausible as defined by GARS method. 6,000 SNPs in BF and 4,000 SNPs in DWG were considered as adequate thresholds. Gene Ontology (GO) analysis using the GARS results of the BF indicated neuron projection development as the notable GO term, whereas for the DWG, the main GO terms were nervous system development and cell adhesion.

摘要

传统上，p值​​是确定全基因组关联研究 (GWAS) 中显着标记的截止阈值的标准。选择最佳标记子集以实现最佳线性无偏预测（BLUP）以提高预测能力（PA）已成为一个有趣的问题。然而，当处理具有相同标记信息的许多性状时，p值本身不能用作对 GWAS 和 BLUP 具有信心的明显解决方案。因此，我们建议采用单核苷酸多态性（SNP）集（GARS）的基因组估计育种值辅助减少方法来解决这些困难。GARS 是基于 BLUP 的 SNP 集决策表示。样本是长白猪，使用的性状是背膘厚度（BF）和日增重（DWG）。整个 SNP 集的 BF 和 DWG 的预测能力 (PA) 分别为 0.8 和 0.8。通过利用基因组估计育种值 (GEBV) 和表型值之间的相关性，根据 GARS 方法的定义，选择 GWAS 中的截止阈值和 BLUP 中的最佳 SNP 子集是合理的。BF 中的 6,000 个 SNP 和 DWG 中的 4,000 个 SNP 被认为是足够的阈值。使用 BF 的 GARS 结果进行基因本体 (GO) 分析表明神经元投射发育是值得注意的 GO 术语，而对于 DWG，主要的 GO 术语是神经系统发育和细胞粘附。