Bauer Andrea Michaela, Hoti F, von Korff M, Pillen K, Léon J, Sillanpää M J
Institute of Crop Science and Resource Conservation, University of Bonn, Bonn, Germany.
Theor Appl Genet. 2009 Jun;119(1):105-23. doi: 10.1007/s00122-009-1021-6. Epub 2009 Apr 11.
A common difficulty in mapping quantitative trait loci (QTLs) is that QTL effects may show environment specificity and thus differ across environments. Furthermore, quantitative traits are likely to be influenced by multiple QTLs or genes having different effect sizes. There is currently a need for efficient mapping strategies to account for both multiple QTLs and marker-by-environment interactions. Thus, the objective of our study was to develop a Bayesian multi-locus multi-environmental method of QTL analysis. This strategy is compared to (1) Bayesian multi-locus mapping, where each environment is analysed separately, (2) Restricted Maximum Likelihood (REML) single-locus method using a mixed hierarchical model, and (3) REML forward selection applying a mixed hierarchical model. For this study, we used data on multi-environmental field trials of 301 BC(2)DH lines derived from a cross between the spring barley elite cultivar Scarlett and the wild donor ISR42-8 from Israel. The lines were genotyped by 98 SSR markers and measured for the agronomic traits "ears per m(2)," "days until heading," "plant height," "thousand grain weight," and "grain yield". Additionally, a simulation study was performed to verify the QTL results obtained in the spring barley population. In general, the results of Bayesian QTL mapping are in accordance with REML methods. In this study, Bayesian multi-locus multi-environmental analysis is a valuable method that is particularly suitable if lines are cultivated in multi-environmental field trials.
定位数量性状基因座（QTL）时的一个常见困难是，QTL效应可能表现出环境特异性，因此在不同环境中存在差异。此外，数量性状可能受到多个具有不同效应大小的QTL或基因的影响。目前需要有效的定位策略来兼顾多个QTL以及标记与环境的相互作用。因此，我们研究的目的是开发一种贝叶斯多位点多环境QTL分析方法。将该策略与以下方法进行比较：（1）贝叶斯多位点定位，即分别分析每个环境；（2）使用混合层次模型的限制最大似然法（REML）单基因座方法；（3）应用混合层次模型的REML向前选择法。在本研究中，我们使用了来自春大麦优良品种斯嘉丽与来自以色列的野生供体ISR42 - 8杂交产生的301个BC(2)DH系的多环境田间试验数据。这些品系通过98个SSR标记进行基因分型，并对农艺性状“每平方米穗数”“抽穗天数”“株高”“千粒重”和“籽粒产量”进行测量。此外，还进行了一项模拟研究，以验证在春大麦群体中获得的QTL结果。总体而言，贝叶斯QTL定位结果与REML方法一致。在本研究中，贝叶斯多位点多环境分析是一种有价值的方法，尤其适用于在多环境田间试验中种植品系的情况。
