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基于玉米地方品种的加倍单倍体库的基因组预测。

Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces.

机构信息

Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593 Stuttgart, Germany.

Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany.

出版信息

Genetics. 2018 Dec;210(4):1185-1196. doi: 10.1534/genetics.118.301286. Epub 2018 Sep 26.

Abstract

Thousands of maize landraces are stored in seed banks worldwide. Doubled-haploid libraries (DHL) produced from landraces harness their rich genetic diversity for future breeding. We investigated the prospects of genomic prediction (GP) for line performance in DHL from six European landraces and 53 elite flint (EF) lines by comparing four scenarios: GP within a single library (sL); GP between pairs of libraries (LwL); and GP among combined libraries, either including (cLi) or excluding (cLe) lines from the training set (TS) that belong to the same DHL as the prediction set. For scenario sL, with = 50 lines in the TS, the prediction accuracy (ρ) among seven agronomic traits varied from -0.53 to 0.57 for the DHL and reached up to 0.74 for the EF lines. For LwL, ρ was close to zero for all DHL and traits. Whereas scenario cLi showed improved ρ values compared to sL, ρ for cLe remained at the low level observed for LwL. Forecasting ρ with deterministic equations yielded inflated values compared to empirical estimates of ρ for the DHL, but conserved the ranking. In conclusion, GP is promising within DHL, but large TS sizes ( > 100) are needed to achieve decent prediction accuracy because LD between QTL and markers is the primary source of information that can be exploited by GP. Since production of DHL from landraces is expensive, we recommend GP only for very large DHL produced from a few highly preselected landraces.

摘要

全世界的种子库中储存了数千种玉米地方品种。从地方品种中产生的双单倍体库(DHL)利用其丰富的遗传多样性,为未来的育种服务。我们通过比较四种情景来研究来自六个欧洲地方品种和 53 个精英硬质玉米(EF)品系的 DHL 中系表现的基因组预测(GP)的前景:在单个库内进行 GP(sL);在库对之间进行 GP(LwL);以及在组合库中进行 GP,包括(cLi)或排除(cLe)来自预测集(PS)的与训练集(TS)相同的 DHL 的系。对于 sL 情景,在 TS 中包含 50 个系,七个农艺性状的预测准确性(ρ)在 DHL 中从-0.53 到 0.57,在 EF 系中达到 0.74。对于 LwL,所有 DHL 和性状的 ρ 都接近零。而情景 cLi 与 sL 相比显示出改善的 ρ 值,cLe 的 ρ 值仍保持与 LwL 观察到的低值。用确定性方程预测 ρ 会导致与 DHL 的 ρ 的经验估计相比inflated values,但保持了排序。总之,GP 在 DHL 中是有前途的,但需要大的 TS 大小(> 100)才能实现良好的预测准确性,因为 QTL 和标记之间的 LD 是 GP 可以利用的主要信息来源。由于从地方品种中生产 DHL 成本较高,我们建议仅对从少数高度预选的地方品种中生产的非常大的 DHL 进行 GP。

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