整合七种方法解析北美大麦花期及Alpha淀粉酶QTL

Integration of Seven Methods for Mapping QTL Underling Heading Time and Alpha Amylase of American Barley

本研究以北美大麦双单倍体资源群体(DH)为研究对象,针对花期和Alpha淀粉酶的QTL定位进行研究。采用单QTL模型分析、四种贝叶斯方法和两种QTL后验概率推断七种方法分别分析了花期及Alpha淀粉酶两个性状QTL,最后将七种方法整合来共同筛选"可信的"QTL。结果显示:贝叶斯方法通常具有极高的QTL检测效力,特别是对处于连锁的QTL;此外,即使不同方法都发现了同一个QTL,其位置估计有时略有偏差,可能是由于单QTL方法对QTL位置估计的有偏性造成的。最后经过整合我们发现了2个"可信的"影响北美大麦花期的QTL和4个"可信的"影响Alpha淀粉酶的QTL。

We mapped QTL underline heading time and Alpha amylase for American Barley DH population. We employed 7 methods, including single QTL analysis, 4 Bayesian methods and two QTL posterior probability methods to analyze this dataset, and integrated the seven methods to ascertain ＂confident＂QTL. The results showed that Bayesian methods usually had excellent performance in QTL detection power, especially for detecting linked QTL; Further, even different methods identified the essentially same QTL, the position estimate of them have slight difference, maybe caused by the biased estimate of single QTL analysis. Finally, we found 2 and 4＂confident＂QTL underlining heading time and Alpha amylaseafter integration, respectively.