The availability of whole genome sequencing (WGS) data enables the discovery of causative single nucleotide polymorphisms (SNPs) or SNPs in high linkage disequilibrium with causative SNPs. This study investigated effects of integrating SNPs selected from imputed WGS data into the data of 54K chip on genomic prediction in Danish Jersey. The WGS SNPs, mainly including peaks of quantitative trait loci, structure variants, regulatory regions of genes, and SNPs within genes with strong effects predicted with variant effect predictor, were selected in previous analyses for dairy breeds in Denmark–Finland–Sweden (DFS) and France (FRA). Animals genotyped with 54K chip, standard LD chip, and customized LD chip which covered selected WGS SNPs and SNPs in the standard LD chip, were imputed to 54K together with DFS and FRA SNPs. Genomic best linear unbiased prediction (GBLUP) and Bayesian four-distribution mixture models considering 54K and selected WGS SNPs as one (a one-component model) or two separate genetic components (a two-component model) were used to predict breeding values. For milk production traits and mastitis, both DFS (0.025) and FRA (0.029) sets of additional WGS SNPs improved reliabilities, and inclusions of all selected WGS SNPs generally achieved highest improvements of reliabilities (0.034). A Bayesian four-distribution model yielded higher reliabilities than a GBLUP model for milk and protein, but extra gains in reliabilities from using selected WGS SNPs were smaller for a Bayesian four-distribution model than a GBLUP model. Generally, no significant difference was observed between one-component and two-component models, except for using GBLUP models for milk.

中文翻译：

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通过整合从估算的全基因组测序数据中选择的额外单核苷酸多态性来改进基因组预测

全基因组测序 (WGS) 数据的可用性能够发现致病单核苷酸多态性 (SNP) 或与致病 SNP 高度连锁不平衡的 SNP。本研究调查了将从估算的 WGS 数据中选择的 SNP 整合到 54K 芯片数据中对丹麦泽西岛基因组预测的影响。WGS SNPs，主要包括数量性状基因座的峰值、结构变异、基因调控区域和基因内的 SNPs，具有用变异效应预测因子预测的强效应，是在之前对丹麦-芬兰-瑞典 (DFS) 奶牛品种的分析中选择的和法国 (FRA)。用 54K 芯片、标准 LD 芯片和覆盖标准 LD 芯片中选定 WGS SNP 和 SNP 的定制 LD 芯片进行基因分型的动物，与 DFS 和 FRA SNP 一起估算为 54K。基因组最佳线性无偏预测 (GBLUP) 和贝叶斯四分布混合模型将 54K 和选定的 WGS SNP 作为一个（单组分模型）或两个单独的遗传组分（双组分模型）用于预测育种值。对于产奶性状和乳腺炎，DFS (0.025) 和 FRA (0.029) 组额外的 WGS SNP 提高了可靠性，并且所有选定 WGS SNP 的包含物通常实现了最高的可靠性提高 (0.034)。对于牛奶和蛋白质，贝叶斯四分布模型比 GBLUP 模型产生更高的可靠性，但使用选定的 WGS SNP 获得的可靠性额外增益对于贝叶斯四分布模型来说比 GBLUP 模型小。一般来说，单组分和双组分模型之间没有观察到显着差异，