抗性育种研究方法探讨--赤霉病

其实不管做哪个性状，以下几个名词算是过去二三十年以及未来若干年在植物育种领域所能看到的大部分文章了,这里先跟大家简单总结一下。另外对于几种方法的评论其实也主要来源于下面解读的文章，加上小编的一些拙见。

最传统也是目前最有效的育种方法：Phenotypic selection

后来从动物研究领域引入了四种结合遗传学的方法：

鉴定优良性状遗传位点的方法：QTL mapping 和GWAS

优良性状实际应用的方法：MAS和GS

QTL mapping做了几十年，GWAS也火了十来年了，但感觉对育种上也就对像rust这种单基因抗病的有点作用。不过随着小麦序列的释放，让我们对这四种方法拭目以待。

目前感觉植物上未来最有前途的方法：High-throughput phenotyping。

接下来进入今天正文：最近关于赤霉病的文章还是挺多的！小编过去三周已经给大家介绍了几篇文章，有两篇也都是最新发表的。刚刚过去的这一周又有一篇相关文章发表，虽然所在杂志的影响因子不是很高，但实验的内容还是非常扎实的。小编今天也正好借这篇文章来谈谈目前赤霉病抗性育种研究的方法！

文章题目如下，第一作者是Thomas Miedaner，通讯作者是Tobias Wurschum，单位是成立于1818年的德国霍恩海姆大学（University of Hohenheim），在农业研究领域算是非常好的一所大学了。另外，我们上次介绍的Hermann Buertmayr也是本文的作者之一。

文章概括：

本文的主体是用GWAS的方法来鉴定一个durum germplasm中的抗性位点。但最有趣的部分是文章利用所鉴定到的主效QTL以及所有QTL来评估MAS，GS，和Phenotypic Selection三种方法在实际抗性育种中的应用价值。最后的结论大家一定会觉得有点意思，但应该也不会惊讶。

材料：

  1. A highly diverse panel of 184 durum wheat lines.

  2. In brief, this diversity panel comprised 170 winter and 14spring types, including old as well as modern cultivars with economic importance as well as current breeding lines.

  3. Six groups according to their geographic origin: Austria and Germany (Group 1), France (Group 2), Canada and United States (Group 3), Italy and Spain (Group 4), Hungary (Group 5) and Bulgaria, Romania, Russia, Slovakia,Turkey and Ukraine (Group 6).

田间设计：

小编前段时间讲过一些field design的内容，目前也还在继续学习当中，这篇文章用到的是a-lattice design，好像在国内和美国用的并不是很多。

  1. In total, five environments (location-by-year combinations) were tested.

  2. The genotypes were sown mechanically in an a-lattice design (Schutz & Cockerham, 1962) with three replications in HOH and OLI, and two replications in TUL, as observation plots of one row with 1.25 m length.

接种方法：

田间Spray

Disease Evaluation：

本文采用的方法在genetic study中好像并不常见，但是在育种项目中还是蛮多的：一个数值（1-9）综合考虑Incidence和Severity，这在育种中算是简单有效的一种方法。但是对于genetic study来说，FHB的incidence和severity经常是由不同遗传位点控制的，所以拿这样一个数值作为唯一的phenotyping来进行GWAS算是本文的一个弱点吧。另外，DON的含量其实对于育种有更大的价值，本文也没有关于这项的数据。

  1. FHB was rated from 1 to 9, where 1 indicates no visible symptoms within a plot, and 2–9 represents <5%, 5%–15%, 15%–25%, 25%–45%, 45%–65%, 65%–  85%, 85%–95% and >95%, respectively, of all spikelets of a plot showing symptoms

  2. This rating reflects the number of infected spikes per plot(type I resistance) as well as the number of infected spikelets per spike (typeII resistance) in a single score.

GS和Prediction ability：如果有做相关方面的可以详细阅读一下这部分的内容：

  1. The genomic prediction was made by Ridge Regression-BLUP with the R package “rrBLUP”

  2. To compare the predictive ability of MAS and genomic selection, we used the four markers explaining more than 5% of the genotypic variance for MAS, while genomic prediction was based on all genome-wide markers.

  3. The predictive ability of both approaches was estimated as Pearson’s correlation coefficient between the predicted and observed trait values of 20% of the lines, with the prediction being based on effect estimates from the remaining 80%of the lines. Resampling was repeated 1000 times.

  4. All calculations were made with the open-source programming language and statistical software R (R Core Team 2013) and the statistical software package ASReml-R 3.0 to solve the mixed models.

结果：

对于表型数据，遗传力算是FHB当中非常高的了，应该是由于大部分都是感病。

对于GWAS结果，感觉并不是很好，检测到的几个抗病位点都只是单一marker，而没有出现helicopter的多markers的结果，说明本文的marker密度还是不够的。

最后，比较MAS和GS两种方法的predictive ability。It must be noted here that the predictive ability of MAS also exploits relatedness between lines and not only QTL effects (Gowda et al., 2014)。

Boxplots for the comparison of marker-assisted selection using the same four QTL and genomic prediction (GP) using all markers. Genotypes with the tall Rht-B1a allele were excluded from both analyses

讨论

  1. Marker-assisted selection (MAS) based on the four strongest QTL yielded a predictive ability of 0.65, while genomic selection (GS) taking into account all genome-wide markers only marginally improved the predictive ability to 0.70. 对于本文用到的这个群体来说，如果marker的密度在加大一些的话，相信MAS和GS的predition要能增加很多！

  2. The predictive ability of phenotypic selection, being roughly estimated as the square root of the heritability, reached 0.92 and was thus by far the highest value in this comparison.

  3. Phenotypic selection certainly takes longer than genomic selection, but on the other hand, population sizes are also not so limited as reliable high-throughput FHB phenotyping platforms have been developed in breeding companies (E. Ebmeyer, personal communication).

  4. Furthermore, recurrent selection can be accelerated using early generations for FHB evaluation, such as F1:2 bulks after recombination of the best entries (Miedaner et al., 2009).

  5. Nevertheless, the rather high predictive ability of genomic prediction suggests that this approach might be a valuable genomic tool to assist FHB resistance breeding if the lines are already genotyped with markers, for example, to utilize genomic prediction for yield or quality traits.

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