分子标记及其在标记辅助选择中的应用

运用分子标记来进行标记辅助选择，已经成为当今家畜育种的一种趋势。笔者对目前常见的几种分子标记进行了简要介绍，并就分子标记在标记辅助选择中的应用，特别就分子标记在标记辅助选择中分子标记图谱的构建、分子标记的筛选、QTL定位和与分子标记的连锁分析3个方面的应用作了较详尽的综述。     

Precision of estimated QTL positions in granddaughter designs using combined haplotype sharing TDT and linkage analysis

The aim of this study was to develop the linear haplotype sharing transmission disequilibrium test (LHS-TDT) method and combine this method with the simple regression method to estimate the precision of QTL positions in granddaughter designs. This precision was determined by Monte Carlo simulation in granddaughter designs. A single bi-allelic QTL at the midpoint of a linkage group and 26 markers with 1 cM intervals and with two alleles each were simulated. Three linear models, (i.e. the simple regression model, the linear haplotype sharing TDT method and the combination of these two models) were compared. The mean of absolute differences (A) between the estimated and true QTL position of each method was considered for six different scenarios consisting of combinations of a number of markers and the most frequent haplotypes. The mean of A, using the simple regression method, was 4.38 centimorgan (cM). The means of A using the LHS-TDT method were less than the simple regression method in all scenarios and ranged from 1.86 to 3.82 cM depending on the scenario. The mean of A using the combined method was more than the LHS-TDT method and less than the simple regression method. The means of A using the combined method ranged from 2.32 to 4.36 cM. Therefore, for populations similar to those population simulated in this study, the LHS-TDT was better than the simple regression method and the combined method for precision of estimated QTL position in granddaughter designs.     

Analysis of FMO genes and off flavour in pork

Off flavours in pork sometimes produce tastes such as sourness, fishy, metallic or other non‐typical flavours and are often caused by low pH. Loss of function mutations in flavin containing mono‐oxygenase 3 (FMO3) are known to be associated with a fishy off flavour in both chicken eggs and cow's milk and a similar autosomal recessive disorder is present in humans resulting in a fishy odour. FMO3 is a member of a gene family that is clustered on human chromosome 1. Comparative mapping suggested that FMO3 and the remaining FMO genes (ex. FMO1 and FMO5) might map to the orthologous region on pig chromosome 9 (SSC9) where a quantitative trait locus (QTL) for off flavour was previously identified. Primers were designed to amplify FMO1, FMO3 and FMO5 gene fragments and several SNPs were discovered and genotyping tests developed. The genotypes from the Iowa State University Berkshire × Yorkshire resource population were used to linkage map FMO1 and FMO3 to SSC9 and FMO5 to pig chromosome 4 (SSC4). QTL and associations analyses were performed using the map containing FMO1 and FMO3. Results demonstrated that FMO3 and FMO1 mapped less than 1 cM away from the peak for the off flavour QTL previously detected on SSC9 and provide indications of an association between the FMO3 polymorphism and off flavour in pork.     

畜禽数量性状基因座位的精细定位

数量性状基因座位(QTL)的精细定位是实施QTL克隆及标记辅助选择(MAS)的重要基础。然而就目前畜禽QTL定位的结果来看,除了通过候选基因法识别的少数基因外,大多数QTL定位的精度仍无法满足实际应用的要求。为进一步提高QTL定位的精度,缩小QTL定位的置信区间,人们相继提出并发展了一系列新的QTL定位方法。本文在分析畜禽QTL定位的基本方法及影响畜禽QTL定位精度的主要因素基础上,对提高QTL定位精确性的策略和方法进行了相应的探讨。     

辣椒分子连锁遗传图谱的构建及抗疫病QTL定位

以抗疫病材料perennial和感疫病材料83-60杂交得到的F2群体144个单株为试材,利用AFLP、RAPD、SSR等分子标记,采用JoinMap3.0构建辣椒分子连锁遗传图谱,该图谱包括12个连锁群,70个标记位点,其中AFLP标记54个,RAPD标记15个,SSR标记1个,覆盖长度为429.02cM,平均图距为6.13cM,连锁群长度在4.30～85.85cM之间。对F2群体进行辣椒疫病的室内人工接种,利用Windows QTL Cartographer2.0软件进行QTL分析,在第4连锁群上检测到两个QTL,解释表型差异的贡献率分别为9.5%和16.4%。     

肉牛分子育种的研究进展

分子生物学的发展,使人们能够从分子水平上探讨肉牛的育种.本文综述了肉牛遗传资源的保护和利用、基因组学和转基因技术的研究现状和进展,着重阐述了肉牛基因图谱的构建和数量性状基因座的检测与利用,同时指出肉牛分子育种的前景和存在问题.     

A method for mapping the distribution of willow at a catchment scale using bi-seasonal SPOT5 imagery

This study showed that seasonal imagery acquired at specific stages of phenology can be used to improve the mapping accuracy of invasive willow at a catchment scale. SPOT5 XI (10 m) satellite imagery was acquired for early autumn and winter to represent the phenological stages of leaf cover and leaf fall respectively. Four classification regimes were evaluated using single‐ and bi‐seasonal composite imagery to determine the most accurate method. Significant spectral noise was found in willow populations, especially in the winter image, due to the effects of undergrowth exposure, shadowing, topography and boundary‐mixed pixels. Two noise reduction techniques were applied to the bi‐seasonal composite image to improve the classification results. The noise‐reduced bi‐seasonal composite image was classified using the spectral angle mapper (SAM) algorithm before importation into a geographical information system. Aerial photography was used to reduce the errors of commission associated with misclassification of pastures. The class accuracy achieved for willow using the method described in this study was 77.5% (Kappa =0.87). The high cost of eradicating willow means that managers must establish priorities for control; this technique can provide a powerful tool for prioritizing control programmes and for monitoring results at a catchment scale.     

一个新的乘法性质的证明

本文用环的理论证明了关于２个自然数相乘的一个新的性质，并将这一性质进行了推广。     

基于特征筛选算法的数字土壤制图研究

平缓地带数字土壤制图中，环境协变量的选择是提高制图精度的关键。已有研究证明遥感影像可作为推理制图的辅助因子，而如何确定环境因子推理制图时各自的权重已成为现阶段研究的重点。选取湖北省麻城市乘马岗镇为研究区，采用3种特征筛选方法进行有效环境变量筛选，探索参与平原-丘陵混合区域制图的因子并确定其重要性，依据选择的相对稳定的指标，进一步探索提高土壤类型制图准确性的途径。根据141个野外独立样点的检验结果表明：在推理制图中，遥感因子在平原区域的重要性程度高于丘陵区域，且遥感因子中归一化植被指数(NDVI)和均值(Mean)较为稳定；基于递归特征算法的按地形推理制图精度最高为75.89%，分别高于ReliefF算法和基于Tree的特征筛选算法13.48%和4.97%；此外3种特征筛选算法制图结果中，按地形因子分区制图的精度均高于整体区域制图。因此，遥感因子作为辅助手段参与推理过程可有效提高制图精度。本研究采用的特征挖掘与机器学习算法对提升土壤制图精度具有一定的理论意义。     

黄瓜叶面积遗传及QTL定位分析

选用2个叶面积大的黄瓜品种（D9320、D2005）和2个叶面积小的黄瓜品种（D0455、D0401）配制杂交组合，筛选叶面积差异较大的亲本组合D0401×D9320构建F2分离群体，采用加性－显性（AD）模型进行遗传分析。结果表明：黄瓜叶面积是由多基因控制的数量性状，以显性效应为主，存在一定的加性效应，狭义遗传力和广义遗传力较高。采用SSR分子标记构建遗传连锁图谱，利用复合区间法对黄瓜叶面积进行QTL定位。结果表明：CSWACC03、CSWCT05BZ和CSCTTT15a位于同一个连锁群上，连锁群总长度为68.3 cM；检测出1个与黄瓜叶面积有关的QTL，与最近标记的距离为3.6 cM，且变异贡献率为8.65 %。