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少即是多:我们如何使大麦基因组选择更具成本效益和准确性?

When less can be better: How can we make genomic selection more cost-effective and accurate in barley?

机构信息

Département de Phytologie, Université Laval, Quebec City, QC, Canada.

Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, QC, Canada.

出版信息

Theor Appl Genet. 2018 Sep;131(9):1873-1890. doi: 10.1007/s00122-018-3120-8. Epub 2018 Jun 1.

Abstract

We were able to obtain good prediction accuracy in genomic selection with ~ 2000 GBS-derived SNPs. SNPs in genic regions did not improve prediction accuracy compared to SNPs in intergenic regions. Since genotyping can represent an important cost in genomic selection, it is important to minimize it without compromising the accuracy of predictions. The objectives of the present study were to explore how a decrease in the unit cost of genotyping impacted: (1) the number of single nucleotide polymorphism (SNP) markers; (2) the accuracy of the resulting genotypic data; (3) the extent of coverage on both physical and genetic maps; and (4) the prediction accuracy (PA) for six important traits in barley. Variations on the genotyping by sequencing protocol were used to generate 16 SNP sets ranging from ~ 500 to ~ 35,000 SNPs. The accuracy of SNP genotypes fluctuated between 95 and 99%. Marker distribution on the physical map was highly skewed toward the terminal regions, whereas a fairly uniform coverage of the genetic map was achieved with all but the smallest set of SNPs. We estimated the PA using three statistical models capturing (or not) the epistatic effect; the one modeling both additivity and epistasis was selected as the best model. The PA obtained with the different SNP sets was measured and found to remain stable, except with the smallest set, where a significant decrease was observed. Finally, we examined if the localization of SNP loci (genic vs. intergenic) affected the PA. No gain in PA was observed using SNPs located in genic regions. In summary, we found that there is considerable scope for decreasing the cost of genotyping in barley (to capture ~ 2000 SNPs) without loss of PA.

摘要

我们能够通过大约 2000 个 GBS 衍生 SNP 获得良好的基因组选择预测准确性。与基因间区域的 SNP 相比,基因区域的 SNP 并没有提高预测准确性。由于基因分型在基因组选择中可能代表重要的成本,因此在不影响预测准确性的情况下,尽量减少成本非常重要。本研究的目的是探索降低基因分型单位成本对以下方面的影响:(1)单核苷酸多态性(SNP)标记的数量;(2)所得基因型数据的准确性;(3)物理图谱和遗传图谱的覆盖程度;(4)大麦六个重要性状的预测准确性(PA)。通过测序协议的基因分型变化,生成了 16 个 SNP 集,范围从大约 5000 到大约 35000 SNP。SNP 基因型的准确性在 95%到 99%之间波动。物理图谱上的标记分布高度偏向于末端区域,而遗传图谱上的覆盖范围相当均匀,除了最小的 SNP 集之外。我们使用三种统计模型来估计 PA,这些模型捕捉(或不捕捉)了上位性效应;选择同时建模加性和上位性的模型作为最佳模型。使用不同的 SNP 集测量了 PA,并发现除了最小的 SNP 集外,PA 保持稳定,在最小的 SNP 集中观察到显著下降。最后,我们检查了 SNP 位置(基因内与基因间)是否影响 PA。位于基因内区域的 SNP 没有增加 PA。总之,我们发现,在不损失 PA 的情况下,大麦基因分型成本有相当大的降低空间(以捕获大约 2000 个 SNP)。

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