A genomic estimated breeding value-assisted reduction method of single nucleotide polymorphism sets: a novel approach for determining the cutoff thresholds in genome-wide association studies and best linear unbiased prediction.

IF 2.5 2区 生物学 Q3 CELL BIOLOGY
Young-Sup Lee, Jae-Don Oh, Jun-Yeong Lee, Donghyun Shin
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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.

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单核苷酸多态性集的基因组估计育种价值辅助减少方法:确定全基因组关联研究的截止阈值和最佳线性无偏预测的新方法。
传统上,p值是确定全基因组关联研究(GWASs)中显著标记的截止阈值的标准。为提高预测能力,选择最佳线性无偏预测(BLUP)标记的最佳子集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值本身不能作为对GWAS和BLUP具有置信度的明显解决方案。因此,我们提出了一种基因组估计育种价值辅助的单核苷酸多态性(SNP)集(GARS)减少方法来解决这些困难。GARS是一种基于blup的SNP集合决策表示。选用长白猪,试验性状为背膘厚(BF)和日增重(DWG)。对整个SNP集BF和DWG的预测能力(PAs)分别为0.8和0.8。利用基因组估计育种值(gebv)与表型值之间的相关性,选择GWAS中的截断阈值和BLUP中的最佳SNP亚群是可行的。BF的6000个snp和DWG的4000个snp被认为是足够的阈值。利用BF的GARS结果进行基因本体(GO)分析表明,神经元投射发育是显著的GO项,而对于DWG,主要的GO项是神经系统发育和细胞粘附。
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来源期刊
Animal Cells and Systems
Animal Cells and Systems 生物-动物学
CiteScore
4.50
自引率
24.10%
发文量
33
审稿时长
6 months
期刊介绍: Animal Cells and Systems is the official journal of the Korean Society for Integrative Biology. This international, peer-reviewed journal publishes original papers that cover diverse aspects of biological sciences including Bioinformatics and Systems Biology, Developmental Biology, Evolution and Systematic Biology, Population Biology, & Animal Behaviour, Molecular and Cellular Biology, Neurobiology and Immunology, and Translational Medicine.
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