基于NC III和TTC设计的胚乳性状QTL区间作图方法

根据三倍体胚乳性状的遗传模型, 提出基于NC III以及TTC两种遗传设计的胚乳性状QTL作图新方法, 并通过计算机模拟数据, 对该方法的可行性和有效性进行了分析验证。模拟研究供试因素包括QTL遗传力、F₂群体植株数和每家系混合样品测定的胚乳数。考察指标包括QTL的统计功效以及QTL位置和效应估计的准确度与精确度。结果表明, 2种设计均有较高的QTL统计功效, 在供试的大多数处理中, 即使QTL的遗传力只有5%, 其被发现能力也可达100%。两种设计均可有效区分胚乳QTL的各种遗传效应。基于TTC设计的胚乳QTL作图方法, 在QTL的统计功效、位置估计和效应估计上, 均优于NC III设计, 虽然这一优势随着QTL遗传力的提高以及样本容量的增大而有降低的趋势。

Interval Mapping of Quantitative Trait Loci Underlying Endosperm Traits with NC III and TTC Designs

The endosperm of cereal crops is a triploid tissue produced by a male gamete from the pollen grain fusing with two female nuclei. Endosperm traits refer to those characteristics that determined by the physical and chemical properties of grain endosperm. Because many quality traits are closely related to endosperms, the genetic study of endosperm traits plays a fundamental and vital role in improving grain quality. There have been several publications detailing the unique properties of endosperm traits. A series of QTL mapping models and approaches specific for triploid traits have been proposed, using single grain observation or more practical one that obtained from bulked samples. However, current methods based on bulked samples often fail to estimate two dominance effects of endosperm QTL separately, or involve a heavy work of crossing. On the basis of quantitative genetic models for triploid endosperm traits, a new mapping approach based on North Carolina Design III (NC III) and Triple Test Cross (TTC) was proposed in this study. Feasibility and efficacy of the method were investigated through simulated data. Experimental factors considered in the simulations include QTL heritability, number of plants in F₂ population, and number of endosperms collected per family. The results suggested that the two methods had satisfactorily high detection powers, which reached 100% even when the QTL heritability was 5%. But the TTC-based approach tended to have slightly higher power than the NC III-based one. Both approaches can distinguish two dominance effects successfully. But overall, the TTC-based approach can offer better estimation of either QTL position or effect, though this superiority waned as the heritability and sample size increase.