【作者】 高会江；

【导师】 潘玉春；

【作者基本信息】 东北农业大学 ， 动物遗传育种与繁殖， 2004， 博士

【摘要】 我们把组织器官的生长发育和随生命时间或某些定量因素变化的生产性能统称为动态性状。本文针对F2资源群体，从理论和模拟实验两个方面系统地研究了动态性状基因复合区间定位的原理与方法，取得了如下研究成果： 1 创建了动态性状基因复合区间定位分析的数学模型 根据Legendre多项式的正交、可加和普适的特性，将关于时间(测定日期)的Legendre多项式镶嵌在遗传模型的每个遗传效应中，以刻画QTL和协同因子对动态性状变化过程的作用。发挥复合区间定位策略的优势，提高对控制动态性状多个QTL的检测效力。 2 推证了动态性状基因复合区间定位分析模型参数的似然估计和回归估计方法 包括适于非平衡抽样的模型参数的似然估计和回归估计一般方法与平衡抽样的简化方法。在此基础上，给出了每种参数估计方法对应的参数求解过程。 3 采用蒙特卡罗模拟方法，比较了在相同的定位环境下，动态性状基因区间定位和复合区间定位同时检测多个QTL的效率的差异；探讨了以回归法代替似然法估计动态性状基因复合区间定位模型参数的可行性；着重地分析了模型中协同因子数目对动态性状基因复合区间定位效率的影响 模拟试验结果表明：复合区间定位法不仅检测出的QTL个数明显多于区间定位法，而且对所检测出的QTL位置、加性与显性回归效应值的估计准确度和精确度均优于区间定位法；在假设误差方差齐次条件下，回归分析法对多个QTL的检测效果与极大似然法基本相同，但回归分析法比极大似然法表现出相当高的运算速度。当被检测的染色体片段上密集地分布着多个QTL的情况下，随着模型中协同因子数目的增多，复合区间定位分析法检测出的QTL个数越多，对QTL位置、加性与显性回归效应的估计也越准确，但统计效力略微有所下降；当稀疏地分布着QTL的情况下，少量的协同因子数目对应的检测效果间还表现与密集地分布着多个QTL的情况相似的规律，但当协同因子数目增加到一定程度后，受需要吸收的剩余QTL效应的限制，检测效果趋于稳定。 同现有的方法相比，本文所提出的动态性状基因复合区间定位分析方法具有明显的优越性：1)因能够处理非平衡抽样资料而大大地方便了取样过程，也降低了取样成本；2)只要选择适宜阶数的Legendre多项式，就可以实现对任意动态性状变化规律的最佳拟和；3)能够同时分析除了遗传效应以外的各种系统环境因素对动态性状变化过程的影响；4)很容易推广到其他资源群体和复杂交配群体。 鉴于动态性状的广泛性及经济重要性，探索这类性状的基因定位分析方法，对丰富数量性状基因定位理论，加快其遗传改良速度具有极其重要的理论和实际意义。更多还原

【Abstract】 Growth development of tissues or organs and productive performances changing with life time or certain quantitative factors are called with one voice as dynamic traits. From theory and simulation experiment, the principle and method of composite interval mapping QTL are systemically investgated for dynamic traits in F2 population. The study results are as follows:1 Construction of mathematical models of composite interval mapping QTL for dynamic traits Based on the orthogonal, additive and universal-practicable properties of Legendre polynomials, Legendre polynomials with appropriate order are nested within genetic effects in genetic model for mapping QTL to describe the effects of QTL and cofactors on changing process of dynamic trait The powers of detecting multiple QTL controlling dynamic traits will therefore be increased by means of the advantages of composite interval mapping.2 Deduction and verification of maximum likelihood estimation and regression estimation of parameters in mathematical models of composite interval mapping QTL for dynamic traits It is included that general parameter estimation method for disequilibrium sampling data and simplified one for disequilibrium sampling data. The corresponding solving processes are provided at the same time.3 Simulation study by using Monte Carlo method The powers of detecting multiple QTL controlling dynamic traits between interval mapping and composite interval mapping are compared in the same mapping environments; Feasibility of replacing maximum likelihood estimation (ML) with regression estimation (RG) is discussed to estimate the parameters in mathematical models of composite interval mapping QTL for dynamic traits, and the effects of cofactors on detecting powers are emphatically analyzed in composite interval mapping QTL for dynamic traits. The results from simulations show that composite interval mapping is not only more than interval mapping in the number of QTL detected, but also is better in estimation accuracy and precision for QTLpositions, additive and dominant regression effects; Under assuming that variances of residual errors are the same in different test days, The statistical efficiencies of detecting multiple QTL are almost same between ML and RG, but the calculating speeds of RG are greatly higher than ML; When multiple QTL are placed densely on specific chromosome fraction, the number of QTL is much more detected and the estimation accuracy of parameters is higher as increased number of cofactors in composite interval mapping model; When multiple QTL are placed distantly, the differences of detecting efficiencies among models with a few cofactors are similar to results from above dense QTL, but The detecting efficiencies tend to be stable until number of cofactors attain to be six because of limitation of residual QTL effects needed to be absorbed.Compared with present methods, composite interval mapping QTL for dynamic trait presented here perform obvious advantages as: 1) it is greatly convenient to sample as well as increase sampling costs because of the ability to deal with the disequilibrium sampling data; 2) it is suitable for any dynamic trait Changing law of dynamic trait can be fitted with the best precision as long as the Legendre polynomial with appropriate order is chosen; 3) Ability to simultaneously analyze effects of genetic and various systemic environment factors, and 4) it is easy to extent it to other resource populations and complicate mating populations.In view of the economical importance of dynamic traits and its extensive existence in features, the study on mapping QTL for these traits would be important in theoretical and practical for revealing its genetic law and increasing its improvement efficiency.更多还原